Massive access or communication, emerging as one of six usage scenarios in 6G, has attracted considerable recent attention due to its potential to empower next-generation industrial cyber-physical systems such as smart grids, factory automation, industrial internet-of-things (IIoT), etc. However, to guarantee its QoS, the associated power control becomes computationally intractable with a huge number of users. In this paper, we present a tractable algorithm for power control in massive access, based on mean-field approximations. In particular, our aim is to maximize the overall throughput in each scheduling period, at the beginning of which each user has a finite number of backlogged bits. To achieve this goal and overcome the curse of dimensionality, a mean-field game (MFG) is formulated. Unfortunately, the formulated MFG is still a non-convex optimization problem. Enlightened by MAPEL, an efficient solver for non-convex power control problem, we leverage multiplicative linear fractional programming (MLFP) to tackle the non-convexity in our formulated MFG. Furthermore, the mean-field approximation assisted power control strategy requires low signaling overhead consumed for estimation and feedback of channel state information (CSI). Simulation results demonstrate that the proposed tractable power control attains substantial performance gains in both the overall throughput and computational complexity.

Interpretability is essential in Whole Slide Image (WSI) analysis for computational pathology, where understanding model predictions helps build trust in AI-assisted diagnostics. While Integrated Gradients (IG) and related attribution methods have shown promise, applying them directly to WSIs introduces challenges due to their high-resolution nature. These methods capture model decision patterns but may overlook class-discriminative signals that are crucial for distinguishing between tumor subtypes. In this work, we introduce Contrastive Integrated Gradients (CIG), a novel attribution method that enhances interpretability by computing contrastive gradients in logit space. First, CIG highlights class-discriminative regions by comparing feature importance relative to a reference class, offering sharper differentiation between tumor and non-tumor areas. Second, CIG satisfies the axioms of integrated attribution, ensuring consistency and theoretical soundness. Third, we propose two attribution quality metrics, MIL-AIC and MIL-SIC, which measure how predictive information and model confidence evolve with access to salient regions, particularly under weak supervision. We validate CIG across three datasets spanning distinct cancer types: CAMELYON16 (breast cancer metastasis in lymph nodes), TCGA-RCC (renal cell carcinoma), and TCGA-Lung (lung cancer). Experimental results demonstrate that CIG yields more informative attributions both quantitatively, using MIL-AIC and MIL-SIC, and qualitatively, through visualizations that align closely with ground truth tumor regions, underscoring its potential for interpretable and trustworthy WSI-based diagnostics

Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

With the rapid expansion of large-bandwidth grid services in recent years, efficient resource allocation has become a critical challenge. This paper explores an optimized resource allocation strategy that integrates satellite communication technology to ensure efficient and stable grid communication services in high-bandwidth scenarios. The main contributions of this study are as follows: we propose a two-stage prediction–allocation closed-loop framework for high-throughput-satellite (HTS)–enabled smart-grid communications. The framework comprises an attention-based traffic-prediction module and a dynamic bandwidth-allocation module, which respectively provide accurate forecasts of future node traffic and priority-aware multibeam bandwidth optimization, thereby offering end-to-end decision support for satellite resource scheduling. Experimental results show that the proposed scheme enhances the grid’s adaptability to future traffic variations at communication nodes, addresses bandwidth provisioning under uncertain high-bandwidth conditions, improves priority-aligned bandwidth utilization and priority efficiency, and ensures both the stability of large-bandwidth grid communications and the performance of critical services.

The rapid expansion of the Internet of Things (IoT) has led to unprecedented growth in multivariate time-series (MVTS) data, which are vital for real-world applications such as industrial monitoring, cyber-physical security, and smart city operations. These data streams are susceptible to anomalies that may indicate system malfunctions, security breaches, or environmental hazards. However, existing MVTS anomaly detection (MTAD) approaches, typically trained in centralized settings, struggle in IoT deployments due to data heterogeneity, resource constraints, and privacy concerns. We propose FEDKO, a novel federated learning (FL) framework that couples Reservoir Computing with Koopman operator theory for efficient, privacy-preserving MTAD in distributed IoT networks. At its core, ReKO, a lightweight spatio-temporal Reservoir-Koopman model, lifts nonlinear MVTS dynamics into a linear space for stable prediction and reconstruction. We formulate the FL training as a bi-level optimization procedure where the inner level learns locally stable Koopman dynamics, and the outer level refines lifted feature representations and reconstruction mappings. We further provide theoretical convergence guarantees, anomaly discriminability analysis, and a structural privacy characterization of the framework. Experiments on four IoT MVTS datasets and deployment on an NVIDIA Jetson edge device show that FEDKO achieves a balanced precision–recall profile with competitive F1-scores under heterogeneous federated settings, while substantially reducing communication and memory footprints compared with MTAD baselines.

Computation offloading has been extensively studied in recent years for the internet of vehicles (IoV), where roadside units (RSUs) are deployed to assist computation offloading. However, it is still challenging to decide when to offload regarding to multiple factors, such as load differences among RSUs, a vehicle’s moving speed, and a vehicle’s energy constraint. In this paper, an optimal offloading decision method is proposed based on optimal stopping theory (OST) to decide when to offload considering the aforementioned factors. Firstly, two offloading decision problems with and without energy constraint are constructed to find the optimal RSU which can minimize expected cost, where the expected cost is determined by the decision on offloading to the current RSU or continuing observing the next RSU. Then, OST is utilized to solve these two problems. Specifically, a sequence of thresholds are pre-calculated based on the OST. An offloading decision can be made by comparing the load of current RSU with the threshold. Moreover, some facts on a vehicle’s moving speed in the environment without energy constraint and the number of observations on RSUs in the environment with energy constraint are revealed. What’s more, the optimal moving speed which can minimize the expected cost is also provided. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed method. The effects of a vehicle’s moving speed and the number of observations on the performance of the proposed method are also verified. Comparing to the benchmarks, the proposed method can achieve superior performance in terms of cost and hit ratio, and has comparable performance with the best offloading method which has full RSUs’ load information. Moreover, the proposed method is robust to the estimation deviation of RSUs’ load distribution.

The integrated satellite-terrestrial network architecture has emerged as a hotspot for the Internet of Everything, which is a promising approach to provide communication service anytime and anywhere. In this paper, we aim to facilitate the system delay reduction through the two-tier cooperative edge computing of terrestrial base stations (BSs) and the satellite. To avoid the two-way propagation delay brought by the centralized decision process, we propose a distributed offloading scheme for the BSs and the satellite. Firstly, we equivalently decompose the offloading problem into multiple subproblems, based on which the optimal User-BS time slot allocation strategy for each BS is derived. Then, by means of multi-agent deep deterministic policy gradient reinforcement learning, the resource allocation strategy of each BS is obtained distributedly, which effectively avoids the two-way propagation delay. Based on the BS resource allocation strategy obtained, the optimal satellite computation capacity allocation strategy is proposed to minimize the total edge computing delay at the satellite. Finally, exhaustive simulations are implemented to demonstrate the performance of the proposed distributed offloading strategy.

Robust Federated Learning (RoFL) extends traditional federated learning, not only by enabling multiple clients to collaboratively train a shared model under the coordination of an edge server, but also by incorporating client-side defense mechanisms (e.g., adversarial training) to defend against adversarial attacks while preserving data privacy. However, recent studies have shown that RoFL also remains vulnerable to the challenges posed by non-independent and identically distributed (non-IID) data distributions across heterogeneous clients, which can degrade overall model generalization and robustness. To mitigate this challenge, in this paper, we propose a novel RoFL framework, called RoFLCCA, to address non-IID challenges while defending against adversarial attacks. In particular, we first introduce a local classifier calibration mechanism that utilizes feature-level augmentation to mitigate the effects of non-IID data. By incorporating global class-wise feature statistics, each client can adjust its classifier using synthetic features derived from these shared representations. Second, we propose a calibrated classifier-guided global adversarial alignment strategy, which enforces consistency between augmented and adversarial predictions to improve robustness. Simulation results demonstrate the effectiveness of the proposed RoFLCCA, which consistently outperforms existing robust federated baselines across different datasets and settings. On average, it achieves a 7.07% improvement in clean accuracy and a 4.71% gain in adversarial robustness, highlighting its ability to enhance both generalization and defense against adversarial threats.

The rapid growth of the Internet of Things (IoT) presents sustainability challenges, including increased maintenance requirements and overall higher energy consumption. This motivates self-sustainable IoT ecosystems based on Energy Harvesting (EH). This paper treats IoT deployments in which IoT devices (IoTDs) rely solely on EH to sense and transmit information about events/alarms to a base station (BS). The objective is to effectively manage the duty cycling of the IoTDs to prolong battery life and maximize the relevant data delivered to the BS. The BS can also selectively wake up specific IoTDs to gather extra information following initial detection. We propose a K-nearest neighbors (KNN)-based duty cycling management to optimize energy efficiency and detection accuracy by considering spatial correlations among IoTDs’ activity and their EH process. We evaluate machine learning approaches, including reinforcement learning (RL) and decision transformers (DT), to maximize information captured from events while managing energy consumption. All three approaches (KNN, RL, and DT) achieve significant energy savings over state-of-the-art methods. Moreover, the RL-based solution approaches the performance of a genie-aided benchmark as the number of IoTDs increases.

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this paper, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multi-category energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption preferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes. A model-free framework applying two state-of-the-art deep reinforcement learning algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

Low Earth Orbit (LEO) satellite networks are characterized by dynamic network topologies and on-demand service requirements from Internet-of-Things (IoT) applications, which make efficient and intelligent flow scheduling challenging. Conventional schemes rely on static configurations or manual rules, thus making it difficult to capture and respond to diverse service demands. Moreover, they often fail to model task–resource relationships effectively, hindering the generation of real-time, executable scheduling policies. To address these challenges, we propose a knowledge-enhanced, intent-driven flow scheduling (KIFS) framework. Specifically, we design a unified pipeline that first translates user intents into precise Quality of Service (QoS) requirements. It incorporates a network state awareness module to estimate per-link bandwidth and utilization, and constructs a task–resource knowledge graph (KG) to enhance the Deep Q-Network (DQN) agent via state augmentation, action pruning, and reward shaping. Finally, the framework translates the resulting policies into standards-compliant SRv6 configurations for real-time deployment. In simulations, the proposed KIFS framework demonstrates superior performance compared to standard baselines in terms of flow success rate and QoS satisfaction.

The low earth orbit (LEO) satellite communication network has recently been proposed by 3GPP as a new paradigm of infrastructure to enhance the capacity and coverage of existing terrestrial wireless networks. However, the mobility of LEO satellite nodes leads to a dynamic environment, which introduces unique challenges for handover and throughput optimization in multi-user access control for LEO networks. We formulate an optimization problem of joint access control and resource allocation to maximize the long-term system throughput and avoid frequent handovers, which is non-deterministic polynomial-time hard. To overcome this challenge problem, we propose a multi-agent deep reinforcement learning algorithm and design the proximal policy optimization (PPO) network structure with the long short-term memory (LSTM) layers. In our proposed algorithm, the centralized trainer node is responsible for training the parameters of all networks, and then each ground user independently makes its own access decisions based on its local observation. We deploy a policy network on each ground user that is able to intelligently access a proper LEO satellite node to maintain high system throughput and avoid frequent handovers over a long period. The simulation results have demonstrated the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in addressing the access control and resource allocation issue for the multi-user LEO satellite network.

Satellite edge federated learning (FL) is an important application paradigm of the integrated artificial intelligence and communication in the future sixth-generation (6G) system. However, achieving efficient FL tasks within highly dynamic, computationally constrained low-earth orbit (LEO) satellite networks while balancing delay and energy consumption remains a significant challenge. Therefore, we explore a satellite-collaborative computation federated learning (SCCFL) system in LEO edge intelligence networks. To this end, we formulate a multi-metric trade-off optimization problem weighted by delay and energy consumption, by jointly optimizing computation satellite (CS) offloading, aggregation satellite (AgS) selection, and computation resource allocation. We propose a multi-metric intelligent alternating (MIA) optimization method, in which each independent agent is responsible for providing decision reference Q-values of specific variables (such as CSs or AgS) for specific metrics (such as delay or energy consumption). The final decision is made based on weighted Q-values. During training, each agent is trained alternately. We also propose a multi-metric computation resource allocation strategy that minimizes delay while reducing the energy consumption as much as possible. Simulation results validate the superiority of our proposed method in terms of FL accuracy and the trade-off between delay and energy consumption.

The rapid evolution of Large Language Models (LLMs) into multi-modal LLM agents has enabled autonomous systems capable of complex reasoning and action. While deploying these Multi-Agent Systems (MAS) at the edge promises reduced latency and enhanced privacy, it introduces significant challenges due to the heterogeneity of edge resources, the massive computational overhead of multimodal LLMs, and the volatility of multimodal data transmission. Existing service placement strategies often overlook the intricate dependencies within agent workflows and the substantial configuration latency required for model switching. In this paper, we propose a latency-aware algorithm for placing heterogeneous multimodal LLM agents at the edge. We model agent collaboration as a Directed Acyclic Graph (DAG), and then formulate the placement problem to maximize the number of satisfied user requests under strict resource constraints. We introduce a dynamic programming-based algorithm with theoretical performance bounds for single-request optimization, explicitly accounting for dynamic model configuration costs. For multi-request scenarios, we develop an online list-scheduling algorithm leveraging B-level heuristics to maximize request completion rates. Extensive experiments using five state-of-the-art agent systems (e.g., AWorld, OpenManus) across four benchmarks (e.g., GAIA, OSWorld) demonstrate that our approach reduces average latency by up to 42.62% compared to baselines, and therefore significantly improves throughput in high-load environments.

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on Red–Green–Blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current Red–Green–Blue and Depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher–student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancement (CAFE) module to capture contextual information, along with a depth feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multi-level distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 MParams to 13.97 MParams, and the computational cost decreases from 19.79 GFLOPs to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

Satellite mega-constellations (SMCs), comprising thousands of interconnected satellites, have emerged as critical infrastructure for 6G networks to achieve seamless global coverage. This paper addresses two fundamental challenges in SMC operation: 1) the inherent spatial-temporal traffic heterogeneity with continuously escalating demand, and 2) the diverging quality-of-service (QoS) requirements for diverse traffic types requiring robust end-to-end performance guarantees. To enhance resource utilization while ensuring service differentiation, we propose a novel dual-scale traffic management framework encompassing macroscopic network-level coordination and microscopic node-level adaptation. The macroscopic component formulates a multi-objective optimization framework that strategically allocates transmission paths by simultaneously minimizing inter-satellite link load disparities and end-to-end queuing delays. The microscopic component introduces an adaptive resource allocation mechanism that decomposes end-to-end QoS requirements into per-node service level agreements, employing federated learning based traffic prediction to enable dynamic resource pre-allocation based on real-time load conditions. This hybrid approach achieves load-aware resource provisioning that maximizes traffic completion rates while minimizing inefficient transmissions. Simulation results show our scheme outperforms the on-demand multi-objective optimization approach, improving traffic completion rates by 17.0-27.5% and resource utilization by 23.29-62.34% across varying loads, while reducing latency and enhancing fairness.

Covert communications provide a stronger privacy protection than cryptography and physical-layer security (PLS). However, previous works on covert communications have implicitly assumed the validity of channel reciprocity, i.e., wireless channels remain constant or approximately constant during their coherence time. In this work, we investigate covert communications in the presence of a disco RIS (DRIS) deployed by the warden Willie, where the DRIS with random and time-varying reflective coefficients acts as a “disco ball”, introducing time-varying fully-passive jamming (FPJ). Consequently, the channel reciprocity assumption no longer holds. The DRIS not only jams the covert transmissions between Alice and Bob, but also decreases the error probabilities of Willie’s detections, without either Bob’s channel knowledge or additional jamming power. To quantify the impact of the DRIS on covert communications, we first design a detection rule for the warden Willie in the presence of time-varying FPJ introduced by the DRIS. Then, we define the detection error probabilities, i.e., the false alarm rate (FAR) and the missed detection rate (MDR), as the monitoring performance metrics for Willie’s detections, and the signal-to-jamming-plus-noise ratio (SJNR) as a communication performance metric for the covert transmissions between Alice and Bob. Based on the detection rule, we derive the detection threshold for the warden Willie to detect whether communications between Alice and Bob is ongoing, considering the time-varying DRIS-based FPJ. Moreover, we conduct theoretical analyses of the FAR and the MDR at the warden Willie, as well as SJNR at Bob, and then present unique properties of the DRIS-based FPJ in covert communications. We present numerical results to validate the derived theoretical analyses and evaluate the impact of DRIS on covert communications.

Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.

Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are increasingly deployed on network infrastructures to serve humans automatically. This emerging paradigm, known as the agentic network, presents new optimization challenges due to the demand to incorporate subjective intents of human users expressed in natural language. Traditional generic Deep Reinforcement Learning (DRL) struggles to capture intent semantics and adjust policies dynamically, thus leading to suboptimality. In this paper, we present LAMeTA, a Large AI Model (LAM)-empowered Two-stage Approach for intent-aware agentic network optimization. First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities from resource-intensive LAMs to lightweight edge LAMs (E-LAMs) to serve end users. Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework. In SRL, E-LAMs translate natural language user intents into structured preference vectors that guide both state representation and reward design. The DRL, in turn, optimizes the generative service function chain composition and E-LAM selection based on real-time network conditions, thus optimizing the subjective Quality-of-Experience (QoE). Extensive experiments conducted in an agentic network with 81 agents demonstrate that IoKD reduces mean squared error in intent prediction by up to 22.5%, while SRL outperforms conventional generic DRL by up to 23.5% in maximizing intent-aware QoE.

Unmanned aerial vehicles (UAVs) enable flexible network deployment but are vulnerable to detection due to the open-access nature of wireless signals. Visible light communication (VLC) emerges as a compelling technology for this context, offering inherent covertness through its reliance on line-of-sight links, while its wide spectrum supports high covert transmission rates. However, the high mobility of UAVs creates dynamic channel conditions that challenge traditional multiple access schemes. This motivates the utilization of rate-splitting multiple access (RSMA), a more robust and flexible framework for managing multi-user interference. Therefore, we investigate a cooperative, RSMA-based VLC system where a source UAV provides covert data transmission, assisted by a jamming UAV that creates power randomness to confuse wardens. By jointly optimizing the three-dimensional (3D) trajectory design and resource allocation of UAVs, we aim to maximize the minimum average covert transmission rate of users. To achieve this goal, the original problem is decomposed into three subproblems, namely horizontal coordinate arrangement, altitude adjustment, along with joint power assignment and rate-splitting, which are solved by the successive convex approximation method, geometric programming-based approach, and majorization-minimization algorithm, respectively. Numerical results validate the effectiveness of the proposed approach through extensive simulations under various parameters and comparative analyses against baselines, highlighting the potential of UAV-assisted visible light covert communications.

Accurate orientation estimation of objects can aid in scene understanding in many applications. In this paper, we consider use cases where passive tags could be deployed to assist radar systems in estimating object orientation. Towards that end, we propose the concept of passive iridescent reflective tags that selectively reflect different wavelengths in different directions. We propose a conceptual tag design based on leaky-wave antennas. We develop a framework for signal modeling and orientation estimation with a multi-tone radar. We analyze the impact of imperfect tag location information, revealing that it minimally impacts orientation estimation accuracy. To reduce estimator complexity, we propose a radiation pointing angle-based estimator with near-optimal performance. We derive its feasible orientation estimation region and show that it depends mainly on the system bandwidth. Monte Carlo simulations validate our analytical results while evincing that the low-complexity estimator achieves near-optimal accuracy and that its feasible orientation estimation region closely matches that of the other estimators. Finally, we show that the optimal number of tones increases with the sensing time under a power budget constraint, multipath effects may be negligible, signal-to-noise ratio gains rise with the number of tones, and many radar antennas can hurt estimation performance when the signal contains very few tones.

Mega satellite constellations (MSCs) based on low Earth orbit (LEO) satellites and inter-satellite links (ISLs) have become increasingly important due to the seamless coverage and high throughput. Unfortunately, the communication components of satellites are susceptible to radiation-induced single event upsets (SEUs), which lead to the failure of ISLs and the decline in network throughput. In this paper, we study the impact of SEUs on network throughput and propose MSC design algorithms to enhance the throughput. To mitigate the impact of SEUs, each satellite is equipped with low-cost mitigation techniques, under which ISLs experience different levels of impairment. Furthermore, we derive the expressions of network throughput and observe the mismatch between the traffic pattern and the network topology. Based on the expressions, we develop the MSC design algorithm to address the gap for throughput enhancement. Simulation results validate the accuracy of the theoretical results, and demonstrate that the proposed algorithm can effectively enhance the network throughput by 8.42% compared to the classical topology under the impact of SEUs.

With the growing demand for computation-intensive applications, multi-access edge computing (MEC) has emerged as a critical paradigm that decentralizes computation and storage by bringing resources closer to users. As distributed computing undergoes ongoing development propelled by the advancements in the Internet of Things (IoT) and mobile communication technologies, the issue of edge node cooperation and resource sharing needs to be investigated. In this paper, the issue of edge node cooperation and resource sharing is modeled as a two-layer framework. More specifically, in the lower layer, a heuristic matching algorithm between users and edge nodes is developed, and a resource sharing algorithm among edge nodes in the same coalition is proposed. In the upper layer, a centralized coalition formation algorithm is designed based on the Hungarian method, and then we further define the coalition rules among edge nodes and propose a distributed coalition formation algorithm. Simulation results demonstrate that the proposed algorithms reduce the network cost effectively compared with non-cooperative schemes. Moreover, we analyze the impact of various network parameters on the network cost, thereby providing insights for future optimization and development in MEC networks.

The advent of the sixth-generation (6G) wireless networks, enhanced by artificial intelligence, promises ubiquitous connectivity through Low Earth Orbit (LEO) satellites. These satellites are capable of collecting vast amounts of geographically diverse and real-time data, which can be immensely valuable for training intelligent models. However, limited inter-satellite communication and data privacy constraints hinder data collection on a single server for training. Therefore, we propose SemSpaceFL, a novel hierarchical federated learning (HFL) framework for LEO satellite networks, with integrated semantic communication capabilities. Our framework introduces a two-tier aggregation architecture where satellite models are first aggregated at regional gateways before final consolidation at a cloud server, which explicitly accounts for satellite mobility patterns and energy constraints. The key innovation lies in our novel aggregation approach, which dynamically adjusts the contribution of each satellite based on its trajectory and association with different gateways, which ensures stable model convergence despite the highly dynamic nature of LEO constellations. To further enhance communication efficiency, we incorporate semantic encoding-decoding techniques trained through the proposed HFL framework, which enables intelligent data compression while maintaining signal integrity. Our experimental results demonstrate that the proposed aggregation strategy achieves superior performance and faster convergence compared to existing benchmarks, while effectively managing the challenges of satellite mobility and energy limitations in dynamic LEO networks.

A key challenge in integrated sensing and communications (ISAC) is the synthesis of waveforms that can modulate communication messages and achieve good sensing performance simultaneously. In ISAC systems, standard communication waveforms can be adapted for sensing, as the sensing receiver (co-located with the transmitter) has knowledge of the communication message and consequently the waveform. However, the randomness of communications may result in waveforms that have high sidelobes masking weak targets. Thus, it is desirable to refine communication waveforms to improve the sensing performance by reducing the integrated sidelobe levels (ISL). This is similar to the peak-to-average power ratio (PAPR) mitigation in orthogonal frequency division multiplexing (OFDM), in which the OFDM-modulated waveform needs to be refined to reduce the PAPR. In this paper, inspired by PAPR reduction algorithms in OFDM, we employ trellis shaping in OFDM-based ISAC systems to refine waveforms for specific sensing metrics using convolutional codes and Viterbi decoding. In such a scheme, the communication data is encoded and then mapped to the signaling constellation in different subcarriers, such that the time-domain sidelobes are reduced. An interesting observation is that sidelobe reduction in OFDM-based ISAC is dual to PAPR reduction in OFDM, thereby sharing a similar signaling structure. Numerical simulations and hardware software defined radio USRP experiments are carried out to demonstrate the effectiveness of the proposed trellis shaping approach.

Multi orbit low earth orbit (LEO) satellites communication is envisioned as a key infrastructure to deliver global coverage, enabling future services from space air ground integrated networks.However, the optimized design of LEO which jointly addresses satellite selection, association control, and resource scheduling while accounting for dynamic visibility in multi orbit constellations still remains open. Satellites moving along distinct orbital planes yield phase shifted ground tracks and heterogeneous, time varying coverage patterns that significantly complicate the optimization.To bridge the gap, we propose a dynamic visibility aware multi orbit satellite selection framework which can determine the optimal serving satellites across orbital layers. The framework is built upon Markov approximation and matching game theory. Specifically, we formulate a combinatorial optimization problem that maximizes the sum rate under per satellite power budgets. The problem is NP hard , combining discrete user association (UA) decisions with continuous power allocation, and an inherently non convex sum rate maximization objective. We address it through a problem specific Markov approximation. Moreover, we alternately solve UA or bandwidth allocation via a matching game and power allocation via a Lagrangian dual program, which together form a block coordinate descent method tailored to this problem. Simulation results show that the proposed algorithm converges to a suboptimal solution across all scenarios. Extensive experiments against four state of the art baselines further demonstrate that our algorithm achieves, on average, approximately 7.85% higher sum rate than the best performing baseline.

The pervasive threat of jamming attacks, particularly from adaptive jammers capable of optimizing their strategies, poses a significant challenge to the security and reliability of wireless communications. This paper addresses this issue by investigating anti-jamming communications empowered by an active reconfigurable intelligent surface. The strategic interaction between the legitimate system and the adaptive jammer is modeled as a Stackelberg game, where the legitimate user, acting as the leader, proactively designs its strategy while anticipating the jammer’s optimal response. We prove the existence of the Stackelberg equilibrium and derive it using a backward induction method. Particularly, the jammer’s optimal strategy is embedded into the leader’s problem, resulting in a bi-level optimization that jointly considers legitimate transmit power, transmit/receive beamformers, and active reflection. We tackle this complex, non-convex problem by using a block coordinate descent framework, wherein subproblems are iteratively solved via convex relaxation and successive convex approximation techniques. Simulation results demonstrate the significant superiority of the proposed active RIS-assisted scheme in enhancing legitimate transmissions and degrading jamming effects compared to baseline schemes across various scenarios. These findings highlight the effectiveness of combining active RIS technology with a strategic game-theoretic framework for anti-jamming communications.

Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

The proliferation of unmanned aerial vehicles (UAVs) in low-altitude airspace necessitates sophisticated resource management supporting both cooperative communications and unauthorized intrusion detection. This paper investigates joint optimization of cell association and power allocation in integrated sensing and communication (ISAC)-enabled low-altitude networks. We propose a novel dual-function framework where ground base stations simultaneously provide communication services to authorized UAVs and localize non-cooperative UAVs for collision avoidance. We establish a channel model capturing the relationship between communication rate and sensing accuracy, formulating an optimization problem that maximizes the weighted sum of system average sum rate and localization quality of service (QoS). The problem jointly optimizes cell association, communication power allocation, and sensing power allocation under UAV localization QoS and cooperative sum rate constraints. To solve the resulting mixed-integer non-convex problem, we propose a joint optimization algorithm based on optimal transport theory (J2OT) that directly handles discrete variables without relaxation, avoiding accuracy losses of conventional approximation methods. J2OT decomposes the problem using optimal transport-based cell association optimization (OTC) and power allocation optimization (OTP). Simulation results demonstrate J2OT’s superiority, achieving 1.5 bits/s/Hz improvement in system objective and 7.5% reduction in localization Cramér-Rao bound compared to Weighted Voronoi and Iterative Water-filling baseline methods.

Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoWblockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.

Malicious jamming presents a pervasive threat to the secure communications, where the challenge becomes increasingly severe due to the growing capability of the jammer allowing the adaptation to legitimate transmissions. This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS), where the channel uncertainties are particularly addressed for robust anti-jamming design. Towards this issue, we adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary, acting as the leader and follower, respectively. We prove the existence of the game equilibrium and adopt the backward induction method for equilibrium analysis. We first derive the optimal jamming policy as the follower’s best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design. We address the uncertainty issue and reformulate the legitimate-side problem by exploiting the error bounds to combat the worst-case jamming attacks. The problem is decomposed within a block successive upper bound minimization (BSUM) framework to tackle the power allocation, transceiving beamforming, and active reflection, respectively, which are iterated towards the robust jamming mitigation scheme. Simulation results are provided to demonstrate the effectiveness of the proposed scheme in protecting the legitimate transmissions under uncertainties, and the superior performance in terms of jamming mitigation as compared with the baselines.

Ambient backscatter communication (AmBC) has become an integral part of ubiquitous Internet of Things (IoT) applications due to its energy-harvesting capabilities and ultra-low- power consumption. However, the open wireless environment exposes AmBC systems to various attacks, and existing authentication methods cannot be implemented between resource-constrained backscatter devices (BDs) due to their high computational demands. To this end, this paper proposes PLCRA-BD, a novel physical layer challenge-response authentication scheme between BDs in AmBC that overcomes BDs’ limitations, supports high mobility, and performs robustly against impersonation and wireless attacks. It constructs embedded keys as physical layer fingerprints for lightweight identification and designs a joint transceiver that integrates BDs’ backscatter waveform with receiver functionality to mitigate interference from ambient RF signals by exploiting repeated patterns in orthogonal frequency division multiplexing (OFDM) symbols. Based on this, a challenge-response authentication procedure is introduced to enable low-complexity fingerprint exchange between two paired BDs leveraging channel coherence, while securing the exchange process using a random number and unpredictable channel fading. Additionally, we optimize the authentication procedure for high-mobility scenarios, completing exchanges within the channel coherence time to minimize the impact of dynamic channel fluctuations. Security analysis confirms its resistance against impersonation, eavesdropping, replay, and counterfeiting attacks. Extensive simulations validate its effectiveness in resource-constrained BDs, demonstrating high authentication accuracy across diverse channel conditions, robustness against multiple wireless attacks, and superior efficiency compared to traditional authentication schemes.

Passive eavesdropping compromises confidentiality in wireless networks, especially in resource-constrained environments where heavyweight cryptography is impractical. Physical layer security (PLS) exploits channel randomness and spatial selectivity to confine information to an intended receiver with modest overhead. However, typical PLS techniques, such as beamforming, artificial noise, and reconfigurable intelligent surfaces, often require additional active power or specialized deployment and rely on precise time synchronization and perfect CSI estimation, which limits their practicality. Meanwhile, the role of ambient backscatter devices (AmBDs) in potentially strengthening the legitimate channel while limiting eavesdroppers in generalized wireless network settings has not been fully investigated. To this end, we propose AmbShield, an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers' reception without requiring extra transmit power and with minimal deployment overhead. In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices. We further develop a unified analytical framework that analyzes the exact probability density function (PDF) and cumulative distribution function (CDF) of legitimate and eavesdropper signal-to-interference-noise ratio (SINR), a closed-form secrecy outage probability (SOP), its high-SNR asymptote, and a secrecy diversity order (SDO). The analysis provides clear design guidelines on various practical system parameters to minimize SOP. Extensive experiments that include Monte Carlo simulations, theoretical derivations, and high-SNR asymptotic analysis demonstrate the security gains of AmbShield across diverse system parameters under imperfect synchronization and CSI estimation.

This paper addresses the need for an explainable and generalized intrusion detection system (IDS) for the in-vehicle networks (IVNs). While machine learning (ML)-based IDS solutions show promising performance, there are still some challenges, such as the lack of trustworthiness and scarcity of attack representing data, hindering their adoption in the automotive cybersecurity. To address these issues, this paper proposes a centralized ML model training and decentralized execution-based framework, namely ACHILLES, that facilitates an explainable and generalizable automotive IDS. Under ACHILLES, different ML models can be trained centrally to enhance decentralized and onboard intrusion detection performance with multiple automotive datasets. In addition, we generate standard feature formats to assess the ML model’s generalization efficacy, where the quality of generalization and explainability is evaluated with SHapley Additive exPlanations (SHAP) by identifying the importance of the feature. We also propose a meta-learning scheme to construct suitable ML models trained by the proposed standard feature formats. The proposed feature format exhibits significant performance gain during ML model training and testing with four state-of-the-art controller area network (CAN)-bus datasets containing real, advanced attacks. The experimental results indicate that developing ML models using the generated generalized features and the meta learning-based model building process leads to enhanced performance. In particular, under the dataset cross train-test setting, the proposed feature format enhances the average accuracy by 40.1% for the baseline model, 32.4% for the meta-learned DNN, and 23.6% for the meta-learned Random Forest, compared with the baseline feature format.

Legal Judgment Prediction (LJP) focuses on predicting judgment results based on the facts of cases. While State-of-the-Art (SOTA) methods have shown impressive performance in law article prediction and charge prediction, they still exhibit weaknesses in prison term prediction. One major reason is that existing models fail to mimic human legal quantitative reasoning to understand monetary features in case facts. Consequently, they do not rigorously quantify the severity of the crime, which is essential for prison term prediction. In this article, we explore and explain how to leverage monetary features to improve LJP via quantitative reasoning. Specifically, we propose QR-LJP, a quantitative reasoning-based LJP model, to integrate legal reasoning knowledge into the prediction process. QR-LJP first employs a curated LLM to extract monetary values from case facts and uses legal quantitative reasoning logic to determine the total crime amount, serving as the quantitative measure of the crime’s severity. This measure is subsequently used to make judgment predictions. We evaluate our model on the real-world dataset CAIL-2018. Experimental results demonstrate that our model outperforms current SOTAs, highlighting the effectiveness of legal quantitative reasoning. Moreover, applying our quantitative reasoning strategy to existing SOTA methods yields significant improvements, especially in macro-F1 scores.

In the above article [1], the email address and bio of Muhammad Rizwan are incorrect. The correct information is below:

The proliferation of data-intensive, low-latency applications has driven the adoption of multi-access edge computing (MEC) to meet the demand for high-performance computing at the network edge. However, ensuring reliable communication under non-line-of-sight (NLoS) conditions remains a significant challenge. While reconfigurable intelligent surfaces (RISs) and the more recent simultaneously transmitting and reflecting RISs (STAR-RISs) offer promising solutions, their passive nature and susceptibility to multiplicative fading limit performance gains. To address these challenges, we propose a novel active STAR-RIS-assisted MEC system that enhances signal strength and adaptability by enabling amplification and joint control over signal transmission and reflection. Our objective is to minimize the energy consumption of user devices, considering both local task computation and uplink task offloading, while maintaining task queue stability. We formulate a joint energy minimization problem with system constraints and long-term queue stability requirements. This problem is decomposed into subproblems: (i) sequential fractional programming is applied to optimize user transmit power, (ii) convex optimization is used to determine partial task offloading ratios, and (iii) a modified Lyapunov optimization combined with double deep Q-networks (DDQN) is proposed to iteratively solve the active STAR-RIS parameters (amplitude and phase shift), amplification control, and task admission at the user side. Numerical results indicate that our proposed system outperforms the conventional passive STAR-RIS-assisted system by 18.64% and the conventional passive RIS-assisted system by 30.43%, respectively.

Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration. Traditional underwater communication faces limitations like low bandwidth, high latency, and susceptibility to noise, while semantic communication (SC) offers a promising solution by focusing on the exchange of semantics rather than symbols or bits. However, SC encounters challenges in underwater environments, including semantic information mismatch and difficulties in accurately identifying and transmitting critical information that aligns with the diverse requirements of underwater applications. To address these challenges, we propose a novel SC framework based on Large Language Models (LLMs). Our framework leverages visual LLMs to perform semantic compression and prioritization of underwater image data according to the query from users. By identifying and encoding key semantic elements within the images, the system selectively transmits high-priority information while applying higher compression rates to less critical regions. On the receiver side, an LLM-based recovery mechanism, along with Global Vision ControlNet and Key Region ControlNet networks, aids in reconstructing the images, thereby enhancing communication efficiency and robustness. Our framework reduces the overall data size to 0.8% of the original. Experimental results demonstrate that our method significantly outperforms existing approaches, ensuring high-quality, semantically accurate image reconstruction.

Unmanned aerial vehicle (UAV)-assisted ground robot networks (UGRNets) are playing an increasingly critical role in a wide range of time-sensitive and mission-critical applications, such as environmental monitoring, infrastructure inspection, and emergency response. UGRNets require not only low-latency communication but also high spatial awareness to ensure effective coordination and decision-making. This paper proposes a unified spatiotemporal framework that evaluates and enhances the quality of updated information in UGRNets from both temporal and spatial dimensions. On the temporal side, we develop a martingale-theory-based prediction method for the delay violation probability bound (DVPB), coupled with a novel joint decay rate model to accurately characterize latency violations in heterogeneous multi-hop communication UGRNets. On the spatial side, we introduce the use of Wasserstein distance to quantify and improve the spatial completeness of robotic coverage. By integrating these metrics, we formulate a spatiotemporal optimization problem that jointly minimizes DVPB and maximizes spatial completeness, enabling robotic agents to adapt their information collection strategies accordingly. Numerical results demonstrate that the proposed framework significantly improves information timeliness and spatial completeness in heterogeneous and dynamic UGRNets scenarios, thereby providing practical insights for real-world deployment.

Enhanced by inter-satellite links and satellite direct-to-device capabilities, satellite networks can offer low-latency communication globally. However, limited spectrum resources and the capacity bounds of the Shannon's information theory pose fundamental challenges for supporting bandwidth-intensive multimedia services. Semantic communication (SemCom) offers a promising solution by transmitting compressed semantic representations instead of raw data, thereby alleviating bandwidth pressure. However, it also introduces SemCom-related constraints that render conventional schemes such as contact graph routing inapplicable. To overcome this challenge, we investigate SemCom-compliant path selection and formulate it as a non-NP hard mixed-integer linear programming problem. To address the problem, we develop a graph-based scheme that exploits the special structure of the solution space, the sparsity of SemCom-capable satellites, and the property of Dijkstra's algorithm, thus achieving optimal solutions with polynomial-time complexity. Simulation results on the Starlink constellation confirm that the proposed scheme facilitates SemCom with negligible computational overhead and significant bandwidth reduction. While the bandwidth reduction comes at the cost of increased delay and path hops, these effects are shown to be mitigatable through higher SemCom deployment in a satellite network or by enabling semantic processing at the user side.

A metaverse can bring many benefits (i.e., self-sustaining and proactive analytics (e.g., analysis before user requests)) to wireless applications; however, its deployment is very challenging due to simultaneous quality of service (QoS) and quality of physical experience (QoE) constraints. Furthermore, the computing and communication resources of end-nodes are limited. For instance, immersive experience devices (e.g., augmented reality (AR) headsets) in a metaverse have limited computing power and therefore, might not be able to perform rendering tasks. Consequently, this paper proposes a novel task offloading framework for metaverse-empowered wireless systems. Our formulated problem aims at minimizing the cost of task offloading in the metaverse while considering both QoS and QoE constraints by optimizing the task offloading, resource allocation, and transmit power allocation variables. For QoS, we consider latency and reliability, whereas for QoE, we consider both immersive experience and packet error rate. To optimize the formulated problem, we use a decomposition-based scheme that further uses modified block-successive upper-bound minimization (BSUM), convex optimization, and multi-agent reinforcement learning (MARL) for transmit power allocation, resource allocation, and task offloading, respectively. Our solution of using convex optimization-assisted MARL for joint resource allocation and task offloading significantly improves the performance of learning in terms of reward and attaining fast QoS as well as QoE. Furthermore, BSUM significantly improves transmit power allocation when used in conjunction with a convex optimizer and MARL. Other than that, we also use a dueling (i.e., it is a reinforcement learning architecture combining the dueling network structure with the double deep Q-learning network method for more stable and efficient Q-value learning) concept to further improve the performance of MARL. Our analyses show that convex optimization, BSUM, and dueling help in significantly improving the performance of MARL. Compared to traditional MARL, our proposal results in significant improvement in terms of reward and cost, as illustrated by the results.

Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g.,polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.

We consider decentralized federated learning (DFL) in mobile computing networks (MCNs), where dynamically changing neighborhood sets among devices arise from mobility and environmental perturbations. The time-varying topology coupled with inherent system heterogeneity poses significant challenges to achieve stable and efficient convergence in DFL. However, existing studies rarely consider both dynamic connectivity and statistical heterogeneity. To close this gap, this paper proposes a novel DFL framework enhanced with topology learning (DFL-TL) to mitigate the spatio-temporal volatility induced by MCNs, where each mobile device faces coupled constraints on its temporal windows for local updates and spatial scopes for model interaction. We introduce a new bounded neighborhood heterogeneity to jointly measure and constrain both the inter-device heterogeneity and the spectral properties of the topologies. Additionally, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly optimize learning costs and neighborhood heterogeneity. Through problem decomposition, DFL-TL efficiently identifies optimal resource allocations and adaptive mixing matrices, thereby enabling the selection of optimal training time windows while reducing the adverse effects of dynamic topologies in heterogeneous networks. Furthermore, we establish the iteration complexity of DFL-TL under non-convex settings and show that solving the proposed MINLP formulation leads to a tighter convergence bound. Extensive experiments demonstrate that DFL-TL achieves a faster convergence performance and reduces the wall-clock training time compared to the state-of-the-art baselines.

With the widespread adoption of location-based services (LBS), user-generated trajectory data shows strong spatiotemporal correlation, rendering it highly vulnerable to inference attacks that expose sensitive information. In particular, once semantic locations like “hospital” and “bank” are identified, the risk of trajectory leakage increases substantially. To address this issue, this paper formulates a personalized spatiotemporal trajectory privacy protection framework, which is designed to protect locations with varying semantic sensitivities on the trajectory from the attacker with spatiotemporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the reinforcement learning (RL) technique to adjust the privacy parameters dynamically. Specifically, we leverage the twin delayed deep deterministic policy gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Furthermore, a safe exploration strategy is incorporated to continuously evaluate and avoid high-risk state-action pairs, thereby enhancing privacy protection. Simulation results demonstrate that the proposed mechanism significantly improves privacy protection while effectively reducing Quality of Service (QoS) loss, exhibiting better convergence and overall system utility.

Multi-access Edge Computing (MEC) enables users to handle resource-intensive and latency-sensitive tasks. However, the offloading behaviors, which are closely correlated with wireless channel conditions, can inadvertently reveal users' location information to untrustworthy MEC servers. Existing location privacy-aware task offloading (LPTO) mechanisms have not fully considered and comprehensively analyzed personalized location privacy protection requirements. To address this gap, this paper proposes a differential privacy (DP)-based personalized LPTO mechanism for MEC environments that jointly optimizes the perturbation region, privacy budget, and offloading rate while maximizing the offloading utility. We quantify personalized privacy requirements by incorporating task sensitivity, user privacy preference, and task priority. Then, we propose a two-timescale (2Ts) optimization framework to solve the complex personalized location privacy-aware task offloading optimization problem. Specifically, we optimize the perturbation region on a long timescale to align with long-term privacy requirements. In contrast, the offloading ratio and privacy budget are dynamically optimized on a short timescale based on instantaneous channel states and offloading workloads. Furthermore, we model the privacy-aware offloading problem as a Markov decision process (MDP) and develop a dual-agent deep reinforcement learning (DRL)-based personalized LPTO mechanism (DDPLM) to optimize strategies under dynamic MEC systems. Simulation results validate that the proposed DDPLM achieves personalized location privacy protection while reducing computational costs.

Satellites have been envisioned as primary non-terrestrial networks capable of seamless global network and surveillance services. Among various satellite types, Cube Satellites (CubeSats) have been actively researched because multiple CubeSats can be conveniently positioned in a target orbit simultaneously and in proximity to Earth. However, CubeSats are small-scale, and thus, they are not able to accommodate a sizable battery, imposing constraints on the duration of their mission. Considering this energy limitation, in order to realize global network services using multiple CubeSats, this paper proposes a novel two-stage Reinforcement Learning (RL) algorithm for energy-efficient CubeSats where RL is utilized for dynamic control under uncertainty. Firstly, sustainable control for single-CubeSat orbital maneuver is considered using deep deterministic policy gradient for vertical position adjustment over a continuous action domain. Secondly, a novel quantum multi-agent RL algorithm for multi-CubeSat cooperative scheduling is designed to realize action dimension reduction into a logarithmic scale based on our proposed Projection-Valued Measure (PVM) over the quantum domain. It is highlighted that our considering two single- and multi-CubeSat problems cannot be separately considered for extreme energy management. The performance evaluation results demonstrate that the proposed algorithm outperforms other benchmarks with 1.51× higher performance in orbital control, 2.71× higher converged reward in enormous action dimensions, and 2.27× higher average network performance.

Vehicular fog computing (VFC) has emerged as a promising paradigm, which leverages the idle computational resources of nearby fog vehicles (FVs) to complement the computing capabilities of conventional vehicular edge computing. However, utilizing VFC to meet the delay-sensitive and computation-intensive requirements of the FVs poses several challenges. First, the limited resources of road side units (RSUs) struggle to accommodate the growing and diverse demands of vehicles. This limitation is further exacerbated by the information asymmetry between the controller and FVs due to the reluctance of FVs to disclose private information and to share resources voluntarily. This information asymmetry hinders the efficient resource allocation and coordination. Second, the heterogeneity in task requirements and the varying capabilities of RSUs and FVs complicate efficient task offloading, thereby resulting in inefficient resource utilization and potential performance degradation. To address these challenges, we first present a hierarchical VFC architecture that incorporates the computing capabilities of both RSUs and FVs. Then, we formulate a delay minimization optimization problem (DMOP), which is an NP-hard mixed integer nonlinear programming (MINLP) problem. To solve the DMOP, we propose a joint computing resource allocation and task offloading approach (JCRATOA), which comprises the components of computing resource allocation and task offloading. Specifically, we propose a convex optimization-based method for RSU resource allocation and a contract theory-based incentive mechanism for FV resource allocation. Moreover, we present a two-sided matching method for task offloading by employing the matching game. Additionally, we theoretically prove the polynomial complexity of JCRATOA. Simulation results demonstrate that the proposed JCRATOA outperforms the benchmark approaches, achieving at least 7.6%, 6.6%, 6.25%, and 11.9% improvements in terms of the task completion delay, task completion ratio, system throughput, and resource utilization fairness, respectively, while satisfying the energy constraints of task vehicles (TVs), RSUs, and FVs.

In vehicular cooperative perception (CP), numerous resource allocation strategies have been proposed to enhance urban autonomous driving. However, existing studies often overlook the competition between self-perception and cooperative perception, where degrading a ground vehicle's (GV's) self-perception may introduce safety risks and reduce passenger comfort. Moreover, UAV–GV cooperation—which can improve sensing precision, reduce task execution delay, and enhance CP service availability—has received limited attention. It is worth noting that unmanned aerial vehicles (UAVs) are unavailable for cooperative perception during the recharging process. To address these issues, this paper investigates on-demand scheduling strategy in UAV–GV cooperative perception. At the millisecond timescale, resource competition is considered in real-time sensing, communication, and computation (SC2) resource allocation. At the minute timescale, the idle flying period between consecutive tasks is utilized for UAV recharging through attachment to GVs along the route. Specifically, we first develop a model that captures the mutual influence between UAVs and GVs on perception performance under resource constraints. Then, a mixed-timescale solution is proposed: at the small timescale, a multi-agent deep reinforcement learning (MA-DRL) algorithm with gradient-free projection and auxiliary supervision is designed to schedule SC2resources; at the large timescale, a Hungarian-based algorithm is employed to control UAV recharging. Simulation results show that the proposed approach outperforms benchmark schemes by reducing task execution delay and energy consumption, and enhancing CP service availability, while satisfying sensing precision, GV safety, and passenger comfort requirements.

Existing speech semantic communication systems mainly based on Joint Source-Channel Coding (JSCC) architectures have demonstrated impressive performance, but their effectiveness remains limited by model structures specifically designed for particular tasks and datasets. Recent advances indicate that generative large models pre-trained on massive datasets, can achieve outstanding performance and exhibit exceptional effectiveness across diverse downstream tasks with minimal fine-tuning. To exploit the rich semantic knowledge embedded in large models and enable adaptive transmission over lossy channels, we propose a Large Speech Model enabled Semantic Communication (LargeSC) system. Simultaneously achieving adaptive compression and robust transmission over lossy channels remains challenging, requiring trade-offs among compression efficiency, speech quality, and latency. In this work, we employ the Mimi as a speech codec, converting speech into discrete tokens compatible with existing network architectures. We propose an adaptive controller module that enables adaptive transmission and in-band Unequal Error Protection (UEP), dynamically adjusting to both speech content and packet loss probability under bandwidth constraints. Additionally, we employ Low-Rank Adaptation (LoRA) to fine-tune the Moshi foundation model for generative recovery of lost speech tokens. Simulation results show that the proposed system supports bandwidths ranging from 550 bps to 2.06 kbps, outperforms conventional baselines in speech quality under high packet loss rates and achieves an end-to-end latency of approximately 460 ms, thereby demonstrating its potential for real-time deployment.

Vertical federated learning (VFL) has been widely adopted in various domains for collaborative decision-making. However, recent studies have revealed critical privacy vulnerabilities in VFL, particularly label inference attacks, which significantly undermine label confidentiality and limit the applicability of VFL in privacy-sensitive scenarios. To mitigate such threats, several defense methods have been proposed by incorporating diverse privacy-preserving techniques. Nevertheless, existing defenses fail to effectively prevent the recently proposed model completion-based label inference attacks. To address this limitation, we propose a novel defense method, termed Label Compression-Based Defense (LCD), to defend against this class of attacks. The core idea of LCD is to train the VFL model using fake labels, thereby decoupling the ground-truth labels from the outputs of the malicious bottom model, which constitute the critical component exploited in the model completion-based attacks. Specifically, we introduce a multi-stage training strategy that decomposes the training process into different stages to deceive the malicious bottom model without affecting the original task. In addition, we design a deep feature-based label compression mechanism to generate fake labels for misleading the attacker. To further enhance the defense effectiveness, we propose an embedding compaction strategy based on center loss, which substantially increases the difficulty of label inference. Moreover, we theoretically prove the effectiveness of LCD from an information-theoretic perspective. Extensive experiments on both tabular and image datasets demonstrate that LCD can effectively defend against label inference attacks. The source code of LCD is publicly available at GitHub:https://github.com/YuanShunJie1/LCD.

With the increasing diversity and complexity of mobile applications, an application typically needs to execute multiple dependent tasks to achieve its functionality. Computation offloading in multi-access edge computing aims to improve user experience, such as reducing makespan and terminal energy consumption, by offloading some tasks to the designated edge server. Task dependencies impose constraints on the execution order of the tasks, which complicates the offloading decisions. Besides, transmission rates exhibit fluctuations in real-world scenarios due to the mobility of users, and they pose new challenges to the problem of dependent task offloading. To minimize the terminal energy consumption under the given deadline, a joint optimization problem of dependent task offloading and local computing resource allocation with time-varying transmission rates is investigated. Since the proposed problem is NP-hard, we decompose it into two subproblems to reduce the complexity and deal with the coupling of decision variables. Specifically, we first solve the subproblem of dependent task offloading by generating special task sets to implement divide and conquer with a fixed local processing speed. Then, we employ Karush-Kuhn-Tucker (KKT) method to solve the subproblem of local resource allocation with the offloading solution derived from the first subproblem while ensuring that the makespan constraint is met. The proposed scheme makes dynamic decisions on dependent task offloading and resource allocation according to the fluctuations of transmission rates, and dynamically adjusts the solution accordingly to achieve better performance. Experimental results show that our solution outperforms baseline methods, reducing makespan by 27–31%, terminal energy consumption by 70–77%, while achieving 25–65% higher service success ratio on average.

With the rapid development of edge devices and growing awareness of privacy protection, recent developers have transformed to fine-tune LLMs via federated learning instead of centralized training. Federated fine-tuning can leverage distributed data sources and computation power, but it also suffers from system and statistical heterogeneity. Client selection is an effective tool to solve the system and statistical heterogeneity in FL, but existing client selection schemes that involve online measurement will not be as effective in LLM fine-tuning as in conventional FL due to the huge LLM size and fewer fine-tuning rounds. In this paper, to the best of our knowledge, we are the first to consider both system and statistical heterogeneity in federated LLM fine-tuning, and we formulate a new latency minimization problem. We propose to measure client data overlap via knowledge graph offline to assist client selection in federated LLM fine-tuning. Our client selection scheme excels in both model accuracy and fine-tuning latency. We evaluate our scheme via two LLMs and two applications via four datasets. The experiment results illustrate that our scheme achieves the highest accuracy while 2.05x faster than the baselines.

Falls remain a major cause of injury and death among older adults, which shows the need for reliable and non-intrusive monitoring solutions in healthcare environments. In this paper, we propose a novel Ambient Internet of Things (IoT) backscatter sensing system that utilizes a dense array of passive tags and a minimal number of reader antennas for cost-effective fall detection and localization. To fully exploit the spatial and temporal characteristics of ambient backscatter sensing data, we design a hierarchical multi-task spatio-temporal graph attention network (HM-STGAT), which jointly models the spatial relationships among tags and antennas as well as the temporal dynamics of human activities. The proposed unified framework simultaneously detects fall events and accurately estimates fall locations. We validate the proposed approach through a real-world experiment to collect a diverse dataset of fall and non-fall scenarios. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in both fall detection accuracy and localization precision, highlighting its potential for practical deployment in healthcare monitoring applications.

This paper proposes an AI framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-assisted base station (BS)-enabled wireless network. The AI-driven framework aims to achieve optimized power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO BS for serving the users. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the communication signal-to-interference-plus-noise ratio (SINRc) of the received signals and beam-pattern gains to improve the sensing SINR of reflected echo signals, which in turn maximizes the achievable rate of users. A novel AI-driven framework is presented to tackle the formulated NP-hard problem that divides it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the sensing information achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINRcimprovement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively. Therefore, our proposed AI-driven framework guarantees efficient power allocation for holographic beamforming through ISAC schemes leveraging AoS.

The dense deployment of Integrated Access and Backhaul (IAB) networks exacerbates spectrum consumption. This paper aims to enhance Spectrum Efficiency (SE) in IAB networks through multi-hop Quality of Service (QoS) analysis and network interference decomposition. We propose a multi-hop delay QoS analysis method that increases computational efficiency and accuracy, thus preventing spectrum over-allocation. We introduce a Transformer-based Interference Path Loss Assessment Neural Network (TIPA-NN) to tackle the issue of inadequate interference information in complex IAB networks, ensuring efficient spectrum reuse. The simulation results show that the proposed QoS analysis method effectively approximates delay unreliability probability across varying hop counts, demonstrating good scalability. The minimum service rate derived supports diverse QoS requirements in multi-hop scenarios. Our algorithm guarantees QoS and enhances SE in IAB networks, outperforming baselines and exhibiting topology-agnostic adaptability. Notably, there is a minimum of 25.03% reduction in subcarrier consumption compared to existing approaches, while ensuring improved SE.

Edge slicing is envisioned to support connected autonomous vehicle (CAV) applications with diverse key performance indicator (KPI) requirements by splitting the shared physical infrastructure into several virtual networks. Unfortunately, existing provisioning approaches struggle to accommodate the spatiotemporal dynamics of CAV traffic, leading to significant violated KPIs or soared resource usage. In this paper, we introduce the transient sharing mechanism among edge slices to obtain reused gains without generating harmful performance interference, in which a slice is allowed to access to the under-utilized reserved resources of other slices but may experience interruptions at any time. Considering the heterogeneity and uncertainty of transient resources, we further develop a two-timescale provisioning scheme. Specifically, slices proactively make reservation decisions based on multi-armed bandit architectures at the beginning of large timescales, while hinging on cost-incentive auction mechanisms selectively preempt transient resources in terms of real-time application demands at each small timescale. With extensive experiments based on real traffic traces, we demonstrate that the proposed scheme can improve 10.43% resource utilization and make slices reduce 42.92% cost than state-of-the-art works, which verifies its high assurance and adaptability.

Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pretrained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce “cached model-as-a-resource” for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources.We design “age of thought” (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize satellite/ground network operators in real-time, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and being free from adverse selection.

Vehicular fog computing (VFC) offers a promising paradigm to alleviate vehicular computing burdens and ensure timely processing of computing tasks. In this paper, a heterogeneous VFC system leveraging hybrid visible light communication (VLC) and radio frequency (RF) communications is investigated, exploiting the interference resilience of VLC and the extended coverage of RF for efficient task offloading to multiple idle vehicles while harnessing their computing resources for parallel task computing. On this basis, an average task processing delay (TPD) minimization problem is formulated, thereby encompassing task offloading, computing and communication resource allocation for rapid processing of computing tasks. Then, the non-convex problem is decomposed into three subproblems and iteratively solved within a block coordinate descent (BCD) framework. Within this framework, the residual additive majorization-minimization algorithm is employed for task offloading and computing resource allocation, the transformed majorization-minimization method is applied for joint power adjustment of both VLC and RF, and the relaxed optimization and reconstruction approach is developed for subchannel assignment. Comprehensive simulations validate the superiority of the heterogeneous VFC system over local computing and VFC systems relying on VLC or RF, and demonstrate the convergence of the proposed BCD-based algorithm and its superiority over baselines. Additionally, the influence of key parameters on the average TPD is also characterized, providing essential design guidelines for practical implementations.

With the explosive advancement of unmanned aerial vehicles (UAVs), the security of efficient UAV networks has become increasingly critical. Owing to the open nature of its communication environment, illegitimate malicious UAVs (MUs) can infer the position of the source UAV (SU) by analyzing received signals, thus compromising the SU location privacy. To protect the SU location privacy while ensuring efficient communication with legitimate receiving UAVs (RUs), we propose an Active Reconfigurable Intelligent Surface (ARIS)-assisted covert communication scheme based on virtual partitioning and artificial noise (AN). Specifically, we design a novel ARIS architecture integrated with an AN module. This architecture dynamically partitions its reflecting elements into multiple sub-regions: one subset is optimized to enhance communication between the SU and RUs, while the other subset generates AN to interfere with the localization of the SU by MUs. We first derive the Cramér-Rao Lower Bound (CRLB) for localization with received signal strength (RSS), based on which, we establish a joint optimization framework for communication enhancement and localization interference. Subsequently, we derive and validate the optimal ARIS partitioning and power allocation under average channel conditions. Finally, tailored optimization methods are proposed for the reflection precoding and AN design of the two partitions. Simulation results validate that, compared to baseline schemes, the proposed scheme significantly increases the localization error of MUs by approximately 37.65% with only a 3.69% reduction in the communication rate between the SU and RUs, thereby effectively protecting the SU location privacy.

Intelligent omni-surfaces (IOSs) with 360° electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a “DISCO” IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a “DISCO ball” that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results1based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU.

Unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to extend the ubiquitous connectivity for ground users (GUs) in the sixth-generation (6G) era. However, it is challenging to cooperatively deploy multiple UAV swarms in large-scale remote areas. Hence, in this paper, we propose a hierarchical UAV swarms structure for 6G aerial access networks, where the head UAVs serve as aerial BSs, and tail UAVs (T-UAVs) are responsible for relay. In detail, we jointly optimize the dynamic deployment and trajectory of UAV swarms, which is formulated as a multi-objective optimization problem (MOP) to concurrently minimize the energy consumption of UAV swarms and GUs, as well as the delay of GUs. However, the proposed MOP is a mixed integer nonlinear programming and NP-hard to solve. Therefore, we develop a K-means and Voronoi diagram based area division method, and construct Fermat points to establish connections between GUs and T-UAVs. Then, an improved non-dominated sorting whale optimization algorithm is proposed to seek Pareto optimal solutions for the transformed MOP. Finally, extensive simulations are conducted to verify the performance of proposed algorithms by comparing with baseline mechanisms, resulting in a 50% complexity reduction.

As a critical component of sixth-generation (6G) wireless networks, ultra-reliable and low-latency communication (URLLC) is expected to support real-time and reliable information exchange in low-altitude environments. However, achieving URLLC often incurs significant resource overhead, including increased bandwidth consumption, higher transmit power, and denser access point (AP) deployment, which pose significant challenges to both spectral efficiency (SE) and energy efficiency (EE). Besides, existing iterative optimization algorithms are computationally intensive and struggle to meet the latency requirements of URLLC. To address these challenges, we propose a hybrid aerial-terrestrial cell-free massive MIMO (CF-mMIMO) network to support diverse services, along with a channel prediction network and a deep mixture of experts (MoE) network for uplink optimization. First, we design a channel prediction network (CP-Net) to mitigate channel aging caused by high-mobility user equipment (UE). CP-Net employs three Transformer-based sub-networks for aged channel state information (CSI) prediction, while a channel quality-aware loss function is introduced to improve the prediction accuracy of weak links. Based on the predicted CSI, we develop a deep MoE network (MoE-Net) for power allocation comprising three expert models targeting different objectives. Then, we introduce a weighted gating network (WT-Net) to learn an efficient adaptive combination of expert outputs. The proposed framework better captures heterogeneous UE requirements and improves communication performance under URLLC constraints. Numerical results demonstrate the effectiveness of the proposed method.

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these challenges, this paper proposes a novel wireless channel model based on symmetric α-stable Lévy processes, thereby enabling continuous-time state-space characterization of both long-term and short-term fading. Building on this model, a generalized optimal control framework is developed via a fractional Hamilton-Jacobi-Bellman (HJB) equation that incorporates the Riesz fractional operator to capture non-local spatial effects and memory-dependent dynamics. The existence and uniqueness of viscosity solutions to the fractional HJB equation are rigorously established, thus ensuring the theoretical validity of the proposed control formulation. Numerical simulations conducted in a multi-cell, multi-user downlink setting demonstrate the effectiveness of the fractional HJB-based strategy in optimizing transmission power under heavy-tailed co-channel and multi-user interference.

We study a control-based design of beamforming and trajectory that incorporates the dynamic model, focusing on a scenario where a multi-antenna unmanned aerial vehicle (UAV) simultaneously performs radar sensing of multiple targets in a specific region and communication with multiple ground users. Two optimization problems are formulated for the three-degree-of-freedom (3-DoF) and six-degree-of-freedom (6-DoF) dynamic models of UAV, which are often overlooked in existing designs. These problems aim to maximize the average weighted communication rate while maintaining the dynamic constraints and the sensing service requirements by designing the UAV trajectory and the communication and sensing beamforming vectors. To deal with the challenges posed by the UAV dynamic constraints, we decompose the original problem into two subproblems: the communication and sensing beamforming design subproblem, and the UAV trajectory optimization subproblem. Given the UAV trajectory, we employ the sequential convex approximation (SCA) and semi-definite relaxation (SDR) methods to transform the beamforming design subproblem into a convex problem. Given the communication and sensing beamforming vectors, we propose a control-based approach with piecewise parameterization and exact penalty function strategies to transform the UAV trajectory optimization subproblem into a static nonlinear program, which can be efficiently solved by sequential quadratic programming (SQP). Numerical simulations indicate that the proposed scheme is more feasible in terms of the UAV control than the existing scheme in practical systems, with less performance loss or even no performance degradation.

Covert communications play a crucial role in modern communication systems, ensuring privacy and security in urban environments. Visible light communication (VLC) emerges as a promising technology due to its potential for covert operations within established infrastructures. In this paper, the covert communication for non-orthogonal multiple access (NOMA)-based VLC systems is investigated, by leveraging the assistance of optical intelligent reflecting surface (OIRS).We develope a system model that allows covert user information to be hidden in the public user information, and to disrupt the warden by altering the channel gain through OIRS. Moreover, the problem is formulated to maximize the effective covert spectral efficiency (SE) of the visible light covert communication (VLCC) system with the detection error probability (DEP) and connection outage probability (COP) constraints, and then an optimization framework is proposed to solve the non-convex problem for both multiple-input single-output (MISO) and single-input single-output (SISO) scenarios. For MISO, a block coordinate descent-based optimization algorithm is developed, decomposing the problem into two sub-problems, named power allocation and OIRS configuration sub-problems. For SISO, closed-form solutions for the power ratio relative to OIRS configuration are derived, simplifying the optimization process. Moreover, we analyzed two distinct cases where Willie either possesses or lacks the knowledge of OIRS state, thereby revealing the impact of warden capabilities on covert communications. Furthermore, the effect of imperfect acquisition of Willie’s channel state information is also examined on the effective covert SE. Simulation results demonstrate that the proposed algorithm enhances the effective covert SE of VLCC systems, while exploring the impact of OIRS units number and other key factors. These findings highlight the potential of OIRS to improve the covert performance in future optical communications.

The growth of indoor terminals has put higher demands on wireless communications, which not only need to support high-rate communications, but are also required to provide power for energy-limited terminals. However, conventional wireless communication systems face challenges in meeting these demands due to the spectrum congestion and electromagnetic interference. Recently, visible light communication (VLC) has been emerged as a promising technology, which can take advantage of its ability to operate within the unlicensed spectra. Therefore, in this paper, VLC benefits from the facilitation of optical intelligent reflecting surface (OIRS) and non-orthogonal multiple access (NOMA) to support energy-constrained devices through simultaneous lightwave information and power transfer (SLIPT), which is well suited for future VLC systems. Moreover, an optimization framework is proposed for NOMA-based VLC systems with OIRS for SLIPT, and then the problem is formulated to minimize the total energy consumption with several key constraints. To solve this non-convex problem, we decompose it into four sub-problems, including the OIRS configuration, NOMA coefficient assignment, transmit power allocation, and direct current bias arrangement, which are solved iteratively by the block coordinate descent algorithm through relaxed iterative optimization, minorization-maximization algorithm, and successive convex approximation. Simulation results validate the convergence and effectiveness of the proposed algorithm, demonstrating the influence of OIRS unit number and other key factors on the system performance. These findings highlight the potential of OIRS to enhance the power transfer capabilities of future optical communications.

An optical reconfigurable intelligent surface (ORIS) was expected to offer extra secrecy performance gain in a visible light communication (VLC) system. However, nonlinear impairments involved degrade the confidential signal reception and have not been fully considered in designing physical layer security (PLS). In this paper, a novel PLS approach is proposed for an ORIS-aided VLC system with consideration of practical nonlinear impairments. It is mathematically formulated to be an optimization problem that maximizes the signal-to-interference-plus-distortion-and-noise ratio of the legitimate link, while entirely suppressing that of multiple eavesdroppers by jointly optimizing the beamforming, jamming and clipping at the transmitters, and also the surface configuration in terms of mirror assignments and rotation angles at the ORIS. We decompose this mixed combinatorial and non-convex optimization problem into three sub-problems and elaborately transform them to be conventional convex programming, quadratic programming and nonlinear programming problems, respectively. Moreover, we also develop a time-efficient iterative approach to achieve the suboptimal solution with low-computational complexity. Simulation results demonstrate the improvement of secrecy performance as compared with conventional schemes, and also the robustness to severe nonlinear impairments and spatial correlation, thereby confirming the beneficial insights of this methodology for secure VLC with nonlinear devices.

The rapid evolution towards the sixth-generation (6G) networks demands advanced beamforming techniques to address challenges in dynamic, high-mobility scenarios, such as vehicular communications. Vision-based beam prediction utilizing RGB camera images emerges as a promising solution for accurate and responsive beam selection. However, reliance on visual data introduces unique vulnerabilities, particularly susceptibility to adversarial attacks, thus potentially compromising beam accuracy and overall network reliability. In this paper, we conduct the first systematic exploration of adversarial threats specifically targeting vision-based mmWave beam selection systems. Traditional white-box attacks are impractical in this context because ground-truth beam indices are inaccessible and spatial dynamics are complex. To address this, we propose a novel black-box adversarial attack strategy, termed Spatial Proxy Attack (SPA), which leverages spatial correlations between user positions and beam indices to craft effective perturbations without requiring access to model parameters or labels. To counteract these adversarial vulnerabilities, we formulate an optimization framework aimed at simultaneously enhancing beam selection accuracy under clean conditions and robustness against adversarial perturbations. We introduce a hybrid deep learning architecture integrated with a dedicated Feature Refinement Module (FRM), designed to systematically reshaping irrelevant, noisy and adversarially perturbed visual features. Evaluations using standard backbone models such as ResNet-50 and MobileNetV2 demonstrate that our proposed method significantly improves performance, achieving up to an +21.07% gain in Top-K accuracy under clean conditions and up to a +37.32% increase in Top-1 adversarial robustness compared to different baseline models.

Deep learning based channel prediction has garnered significant attention to mitigate channel aging in high-mobility multiple-input multiple-output (MIMO) systems. However, existing channel prediction methods extract the temporal correlations from the channel sequences estimated at uniform pilots, which require dense pilot configuration to mitigate Doppler aliasing in high-mobility scenarios and incur substantial estimation overhead. To tackle this problem, we propose a channel prediction method based on continuous-time transformer with the non-uniform pilot pattern, thereby enabling accurate prediction across arbitrary time scales with only a small number of pilots. Specifically, we first design the non-uniform pilot pattern based on Chebyshev polynomial roots and then prove its optimality under Doppler-dominated channel variations with relatively stable user velocity, wherein a subset of pilots are densely configured to provide a finer resolution of Doppler phase estimation. To adapt to the non-uniform pattern, a continuous-time transformer is further proposed, which integrates the superior feature extraction capability of transformer with the continuous-time modeling strength of neural ordinary differential equation (ODE) for flexibly processing the estimated channel sequences with non-uniform time scales. More concretely, the attention mechanism is extended to the continuous-time domain by incorporating neural ODE, while a high-frequency temporal encoding is designed to fit rapidly time-varying channels. Besides, an element-wise prediction mechanism is proposed to efficiently capture temporal correlations and prevent overfitting. Simulation results demonstrate that our proposed method can realize accurate continuous-time channel prediction in high-mobility scenarios, and significantly outperforms existing channel prediction methods.

Jamming attacks pose a significant threat to the security of air-ground communications, where the challenge becomes more severe when involving multiple unmanned aerial vehicles (UAVs) incurring complex interference. To address this issue, this paper proposes a graph attention-based reinforcement learning strategy for anti-jamming UAV communications. Specifically, we consider the multi-UAV transmission and deployment in the presence of jamming attacks. Then, we formulate a zero-sum game with the legitimate side and adversary to maximize and minimize the overall transmission rate, respectively. Given the complicated structure of the game, we decompose it into two layers, tackled in a hierarchical learning framework. Particularly, the inner layer addresses the legitimate beamforming, for which we establish the graph attention network (GAT) to track the complicated interference and jamming relationship based on the graph representation of the UAV network. The outer layer address the legitimate UAV deployment and adversarial jamming policy, which is reinterpreted in a multi-agent deep reinforcement learning framework to obtain the strategies of both sides. The inner GAT is then nested within the outer multi-agent learning framework in a hierarchical manner to approximate the equilibrium of the original game model. Simulation results demonstrate the convergence and the performance superiority of the proposed learning scheme in terms of anti-jamming transmission rate. Also, the results exhibit significant generalization capability to cover different network configurations and parameters with reliable communication performance.

In emergency scenarios, unmanned aerial vehicles (UAVs) show significant potential as aerial base stations (BSs) to establish reliable communication links and provide localization services through integrated air-ground collaboration. This paper proposes a novel energy-efficient collaborative framework based on the solo-UAV-rescuer cooperative (SURC) paradigm, which synergistically enhances both communication capacity and localization accuracy. From a system optimization perspective, we formulate an optimization problem using a normalized combination of three critical metrics: achievable data rate, localization accuracy, and energy consumption. Specifically, to maximize the system’s utility, we design a signal perception-based localization method that incorporates angle-of-arrival (AOA) localization information for guidance, and develop a beamforming scheme to facilitate high data rate communication. Building on these methods, we propose a deep reinforcement learning (DRL)-based synergistic communication and localization reinforcement (SYNCORE) approach that dynamically optimizes three key operational parameters: UAV trajectory planning, flight time, and transmission power control, achieving reliable services with energy-efficient operation. Based on the simulation results, we validate that the proposed scheme enhances communication and localization performance, while also improving energy efficiency, surpassing the baseline schemes.

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers. The results validate that the proposed framework consistently achieves superior performance compared to Proximal Policy Optimization (PPO), Soft Actor Critic (SAC), and ablated baselines in terms of reward, failure rate, and migration overhead, demonstrating the effectiveness of the proposed spatio-temporal modeling and hybrid reinforcement learning approach in dynamic satellite edge environments.

Low Earth orbit (LEO) satellite communications have received significant attention due to the global coverage and low-latency characteristics. Due to the limited size, weight, and power of LEO satellites, meeting the demand of satellite-terrestrial communications with minimal energy consumption while suppressing interference to co-existing systems remains a significant challenge. In this paper, a LEO satellite architecture with beam hopping (BH) is proposed, where the dynamic resource management and real-time load balancing are jointly implemented. Specifically, an optimization problem for the BH pattern, resource management, and load balancing with multi-satellite collaboration is formulated to achieve the long-term average energy minimization, while suppressing the interference to the co-existing system. Subsequently, by introducing the Lyapunov optimization method, the long-term energy minimization is transformed into a series of dynamic optimization problems for each time slot, thereby ensuring both the energy minimization and queue stability, where the block coordinate descent algorithm is employed to alternately address these issues. In particular, the BH pattern is determined by the multiplier penalty method combined with the majorization-minimization algorithm. Additionally, the resource management is addressed through the successive convex approximation technique, while real-time load balancing is achieved via quadratic programming. Overall, the proposed method, which can be effectively implemented within polynomial time, outperforms other benchmarks in simulations by achieving interference suppression in co-existing systems and reducing the energy consumption, while satisfying the communication demands.

The mega low-Earth-orbit (LEO) satellite constellation can achieve high throughput and low latency satellite communications, which is highlighted for next-generation communications. In this paper, the LEO satellite communications with coordinated beam hopping (BH) are investigated for throughput maximization and load balancing. Generally, the geographically non-uniform and time-varying packet arriving, along with the interference caused by dense beams, can present significant challenges. Hence, the proper BH pattern selection and dynamic power allocation are necessitated to enhance the throughput of satellite-terrestrial communications. Additionally, load balancing via inter-satellite links (ISLs) adjusts the queue length among satellites, thus indirectly altering the throughput by mitigating congestion. Therefore, an optimization problem is formulated to maximize long-term throughput by determining the BH pattern and dynamic power, while the problem for load balancing via ISLs is also proposed for stabilizing queues in a low congestion state to enhance the throughput. As both problems are inherently associated with each other through the queue length, they are solved in cycles. Specifically, the problem for long-term throughput is converted into decisions for each time slot with the Lyapunov drift-plus-penalty method, and then the BH pattern and power allocation are optimized alternately through successive convex approximation. Moreover, load balancing via ISLs is determined according to the queue length by minimizing the upper bound of Lyapunov drift, thereby decreasing the congestion of queues. Simulation results indicate that the proposed method can effectively improve the throughput and reduce the packet loss compared with the existing baselines, demonstrating the effectiveness of the Lyapunov optimization based method.

In this paper, we investigate the low-complexity distributed combining scheme design for near-field cell-free extremely large-scale multiple-input-multiple-output (CF XL-MIMO) systems. Firstly, we construct the uplink spectral efficiency (SE) performance analysis framework for CF XL-MIMO systems over centralized and distributed processing schemes. Notably, we derive the centralized minimum mean-square error (CMMSE) and local minimum mean-square error (LMMSE) combining schemes over arbitrary channel estimators. Then, focusing on the CMMSE and LMMSE combining schemes, we propose five low-complexity distributed combining schemes based on the matrix approximation methodology or the symmetric successive over relaxation (SSOR) algorithm. More specifically, we propose two matrix approximation methodology-aided combining schemes: Global Statistics & Local Instantaneous information-based MMSE (GSLI-MMSE) and Statistics matrix Inversion-based LMMSE (SI-LMMSE). These two schemes are derived by approximating the global instantaneous information in the CMMSE combining and the local instantaneous information in the LMMSE combining with the global and local statistics information by asymptotic analysis and matrix expectation approximation, respectively. Moreover, by applying the low-complexity SSOR algorithm to iteratively solve the matrix inversion in the LMMSE combining, we derive three distributed SSOR-based LMMSE combining schemes, distinguished from the applied information and initial values.

As edge applications demand real-time processing with limited bandwidth and energy, traditional communication systems face challenges to meet performance requirements due to the centralized architecture and redundant data transmission. To address these challenges, we propose a UAV-assisted semantic edge computing network that leverages UAV mobility and semantic communication. We formulate a joint optimization problem involving UAV trajectory, data allocation, and semantic extraction to maximize the semantic processing rate. To solve this problem, we develop a hybrid deep deterministic policy gradient (H-DDPG) algorithm that integrates deep reinforcement learning (DRL) with convex optimization via block coordinate descent (BCD), thereby enabling efficient joint decision-making across tightly coupled variables. Furthermore, we propose a hybrid diffusion deep deterministic policy gradient (H-D3PG) algorithm, which incorporates denoising diffusion models into the DRL framework. By addressing the limited adaptability of deterministic strategies, this design enhances policy expressiveness and stability. As a result, the algorithm enables adaptive trajectory control under time-varying semantic tasks and wireless channel conditions in UAV-assisted edge networks. Simulations show that H-D3PG improves the semantic processing rate by up to 38.8% while reducing energy consumption compared to Raw Data Transmission.

Space-air-ground integrated networks can provide better connectivity and improved performance to ground users and thus have been well-recognized as the innovative trend for future 6G and beyond wireless systems. However, such heterogeneous and complex integrated communication systems also pose many new challenges for joint performance optimization. In this paper, we study the data delivery optimization problem in a space-air-ground network, where joint resource management decisions on user association, bandwidth allocation, cache placement, and high-altitude platform (HAP) location selection have to be optimized. To tackle this complex and challenging mixed integer programming optimization problem, we introduce a quantum-assisted method, named the Hybrid quantum-classical Benders’ Decomposition (HyBD) algorithm, which leverages the strengths of both quantum and classical computing. Experiments on the commercial quantum annealing machine demonstrate the effectiveness and robustness of the proposed HyBD method, with up to 64.3% improvement of iteration number and 82.8% improvement of average computation time over the classical Benders’ Decomposition algorithm on classical CPUs even at small scales, which demonstrates the quantum advantage.

Beamforming and waveform design are key essentials for integrated sensing and communication (ISAC) in future wireless networks, which also inspires the emerging research of optical wireless ISAC (OW-ISAC). In this paper, we investigate the joint design of optical phased array (OPA) beamforming and orthogonal frequency division multiplexing (OFDM) waveform, thus extending the radio-frequency (RF)-ISAC paradigm to the optical band. First, the system model for the OPA-based OW-ISAC framework is introduced to facilitate simultaneous communication and sensing. Next, signal processing techniques are tailored for an optical OFDM waveform with precisely separated communication and sensing subcarriers. Then, the joint optimization problem of OPA beamforming and OFDM waveform design is formulated, decomposed, and resolved to reach a flexible compromise between communication and sensing performances. Moreover, numerical simulations validate the effectiveness of the proposed OW-ISAC scheme and reveal the trade-off between communication and sensing functionalities. Consequently, the capabilities of reliable communication and precise sensing will establish OW-ISAC as a powerful complement to RF-ISAC in the near future.

Larger antenna arrays, combined with higher transmission frequencies, are prospective in fulfilling the demands of the sixth-generation (6G) communications, enabling a 10-fold increase in overall spectral efficiency. However, such configurations give rise to near-field effects, requiring spherical rather than planar wave modeling. In practical multi-user communications, it is typical that part of the user equippments (UEs) resides in the near-field region, while others are located in the far-field region, thereby leading to a unified near/far-field scenario. Conventional codebooks tailored to either regime alone thus become mismatched, resulting in notable spectral efficiency degradation. In view of this, the ringwise codebook based on the slope-intercept formulation is proposed to address the unified near/far-field communication scenario. Specifically, the slope-intercept domain is first illustrated as the foundation of our codebook design, where the correlation between near/far-field channel steering vectors is exploited by mapping the angle-distance into the slope-intercept parameters. In the slope-intercept domain, the correlation pattern of steering vectors exhibits a dual triangle structure, supported by rigorous analyses on axial symmetry and correlation width, which paves the way for the subsequent codebook development. Secondly, the ringwise codebook is proposed where all UEs coarsely estimate its own slope parameter, based on which ring-wise codebooks composed of orthogonal codewords derived from the slope-intercept domain are constructed for different UEs. The resulting codebooks are then fed back to the base station through specific slope parameters, which are leveraged in the subsequent precoding procedure. Finally, rigorous analysis demonstrates that both the computational complexity and hardware cost of the proposed ringwise codebook design are acceptable, while numerical results validate its effectiveness and feasibility, achieving gains in both spectral efficiency and complexity compared to conventional counterparts.

The widespread adoption of wireless communication systems in both military and civilian applications has significantly advanced technological progress and social development across various industries. However, narrowband interference signals pose a significant challenge, severely disrupting the normal operation of wireless communication equipment. A major obstacle in existing narrowband interference detection lies in enhancing robustness under complex channel propagation conditions and diverse, dynamically changing types of interference. In view of those challenges, we propose a robustness-enhanced narrowband interference detection method by utilizing unlabeled data. The proposed detection network incorporates soft-shrink technology to isolate irrelevant signal features while adaptively extracting and fusing original and time-frequency features. The proposed method leverages the distribution characteristics of interference frequency bands to enhance model robustness in varying channel propagation environments. Additionally, we design a pseudo-label-based model tuning process to exploit the potential of unlabeled data, further enhancing the model’s robustness. Comparative experiments demonstrate the superiority of the proposed method against various baselines, as well as against configurations incorporating individual network modules.

In mountainous environments, terrain occlusion causes non-line-of-sight (NLoS) transmission, significantly reducing the signal propagation range. To improve emergency rescue efficiency, a mobile unmanned aerial vehicle (UAV)-based integrated localization and communication (ILAC) network should be deployed to achieve optimal performance through adaptive trajectory planning and resource allocation. However, irregular and unpredictable terrain occlusions, coupled with dynamic users, make traditional optimization ineffective and reinforcement learning (RL) inefficient. To address these challenges, this paper proposes a hybrid action space soft Actor-Critic with geographic information-based state aggregation (GeoAgg-HSAC) decision-making scheme. First, an RL state aggregation method based on graph contrastive learning is designed. Through a pre-trained graph neural network (GNN), the UAV network states experiencing the same occlusion are mapped to similar low-dimensional representations. This method reduces the state dimension and allows similar states to share policy experience, thereby improving sample efficiency and accelerating convergence. A hybrid action space SAC network is then designed, which simultaneously makes decisions for continuous UAV trajectories and discrete resource allocation. Finally, a simulation environment based on real mountain terrain and wireless data is built for the experiment. The experimental results show that the proposed scheme has significant advantages for optimizing communication and localization performance.

In this paper, we propose a federated learning (FL) system enhanced by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which is designed to protect client-side data privacy and reinforce the security of data exchange over wireless channels. Assuming an honest-but-curious central server that may infer private information from user gradients, we adopt differential privacy (DP) by injecting noise into local updates to safeguard user data. Theoretical results are derived to characterize the systematic privacy guarantees provided by the DP noise power and the gradient information in the proposed STAR-RIS-enabled DP-FL systems. Building on these results, the secrecy sum rate of local information is formulated by jointly optimizing the STAR-RIS coefficient matrices, users’ transmission power, artificial jamming power, and the power of DP noise introduced by the FL users. To tackle the non-convex optimization challenge, we develop a block coordinate descent algorithm that partitions the original problem into four manageable subproblems. The closed-form expressions are obtained for users’ transmit power, artificial jamming power, and the power of DP noise. For the STAR-RIS phase shift design, approximate solutions are derived through semidefinite relaxation combined with a surrogate lower bound method. Finally, simulation results demonstrate that the proposed STAR-RIS-enabled DP-FL systems achieve significantly improved secrecy performance compared to conventional FL systems with randomly configured STAR-RIS amplitude, phase shifts, and transmit power. Furthermore, the proposed FL algorithm achieves model training and testing performance that closely approximates that of FL without DP, highlighting its effectiveness in preserving both data privacy and model utility.

Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in underlying environments for performance enhancement. Specifically, the additional reflective signal paths provided by these ambient devices are exploited to enhance sensing accuracy and communication reliability, respectively. In this system, we define the Pareto boundary characterizing the trade-off between sensing mutual information (SMI) and communication rates to provide fundamental insights for its design. To derive the boundary, we formulate a performance optimization problem within an orthogonal frequency division multiplexing (OFDM) framework, by jointly optimizing time-frequency resource element (RE) allocation, transmit power management, and BD modulation decisions. To tackle the non-convexity of the problem, we decompose it into three subproblems, solved iteratively through a block coordinate descent (BCD) algorithm. Specifically, the RE subproblem is addressed using the successive convex approximation (SCA) method, the power subproblem is solved using an augmented Lagrangian combined water-filling method, and the BD modulation subproblem is tackled using semidefinite relaxation (SDR) methods. Additionally, we demonstrate the generality of the proposed system by showing its adaptability to bistatic ISAC scenarios and MIMO settings. Finally, extensive simulation results validate the effectiveness of the proposed system and its superior performance compared to existing state-of-the-art ISAC schemes.

With the integrated development of global marine exploitation and 6G technology, building an all-domain marine wireless network has become crucial for supporting marine activities. However, the unique communication environment, varying collaboration of heterogeneous devices, and dynamic network changes pose technical bottlenecks for balancing real-time and efficient resource competition. To overcome those challenges, this paper proposes a novel integrated marine wireless network with multi-type unmanned device clusters across space-surface-submarine media. To address heterogeneous resource allocation, we consider channel capacity and device connection, modeling it as a three-sided matching framework with size constraints and cyclic preferences (TMSC). Building on this, we propose the satellite-prioritized restricted double-TMSC (SPR-DT) algorithm to solve optimal matching in quasi-static networks, aiming to maximize total backhaul revenue. To handle rapid dynamic network changes, we initialize the proximal policy optimization (PPO) with the stable solution of SPR-DT, thus addressing the challenge of acquiring real training data while accelerating algorithm convergence. Then, we propose a PPO-assisted multi-slot matching algorithm to enhance solution efficiency in large-scale dynamic scenarios. The simulation results show that the proposed algorithm achieves an optimal effect of 94.6% in quasistatic scenarios, with a complexity reduced to 3.2%. In dynamic scenarios, the results are 87.2% and 28.7%, respectively.

Existing large-scale zero-shot text-to-speech (TTS) models deliver high speech quality but suffer from slow inference speeds due to massive parameters. To address this issue, this paper introduces ZipVoice, a high-quality flow-matching-based zero-shot TTS model with a compact model size and fast inference speed. Key designs include: 1) a Zipformer-based vector field estimator to maintain adequate modeling capabilities under constrained size; 2) Average upsampling-based initial speech-text alignment and Zipformer-based text encoder to improve speech intelligibility; 3) A flow distillation method to reduce sampling steps and eliminate the inference overhead associated with classifier-free guidance. Experiments on 100 k hours multilingual datasets show that ZipVoice matches state-of-the-art models in speech quality, while being 3 times smaller and up to 30 times faster than a DiT-based flow-matching baseline. Codes, model checkpoints and demo samples are publicly available.11https://github.com/k2-fsa/ZipVoice

Large Language Models (LLMs) have shown great promise in automating data analytics tasks by interpreting natural language queries and generating multi-operation execution plans. However, existing LLM-agent-based analytics frameworks operate under the assumption of centralized data access, offering little to no privacy protection. In contrast, federated analytics (FA) enables privacy-preserving computation across distributed data sources, but lacks support for natural language input and requires structured, machine-readable queries. In this work, we present LAFA, the first system that integrates LLM-agent-based data analytics with FA. LAFA introduces a hierarchical multi-agent architecture that accepts natural language queries and transforms them into optimized, executable FA workflows. A coarse-grained planner first decomposes complex queries into sub-queries, while a fine-grained planner maps each sub-query into a Directed Acyclic Graph of FA operations using prior structural knowledge. To improve execution efficiency, an optimizer agent rewrites and merges multiple DAGs, eliminating redundant operations and minimizing computational and communicational overhead. Our experiments demonstrate that LAFA consistently outperforms baseline prompting strategies by achieving higher execution plan success rates and reducing resource-intensive FA operations by a substantial margin. This work establishes a practical foundation for privacy-preserving, LLM-driven analytics that supports natural language input in the FA setting.

Due to the open nature of unmanned aerial vehicles (UAVs) communication, UAV applications face severe challenges in preserving location privacy. In open-space environments, illegitimate malicious nodes (MNs) can estimate the position of the source UAV (SU) through analysis of the signals they receive, which facilitates further attacks. Therefore, while ensuring efficient communication between UAVs, it is crucial to protect the location privacy of the SU. To address this issue, this work designs a scheme utilizing virtual partition of Active Reconfigurable Intelligent Surface (ARIS) to improve the communication rate of legitimate links while simultaneously reducing the localization accuracy of MNs with controllable artificial noise (AN) sources. Furthermore, we derive the Cramér-Rao Lower Bound (CRLB) for the illegitimate localization model based on received signal strength (RSS), and formulate the corresponding joint optimization problem. Finally, the optimal division of ARIS elements and power are derived. Meanwhile, we propose dedicated reflection matrix optimization algorithms for ARIS. Simulation results validate that the proposed scheme drastically reduces the localization accuracy of MNs, while preserving communication efficiency and reliability.

Satellite-Air-Ground Integrated Networks (SAGINs) provide ubiquitous connectivity, global coverage and flexible deployment convenience for terrestrial users, which are beneficial to optimizing network resources and achieving task scheduling functions. However, the corresponding SAGIN nodes are dynamic and complex, leading to intractable multi-modal features and high network latency when graph model is used for collaborative task completion. Therefore, we establish a directed SAGIN federated graph model to minimize the total transmission latency via computation offloading and quantization methods. Specifically, we utilize the federated graph learning to process the time-varying graph nodes and sizes, and then perform deep reinforcement learning (DRL) to optimize the computation and quantization resources. Moreover, federated learning is convoked to accelerate the convergence speed. Finally, our simulation results show that the proposed method outperforms some advanced benchmarks in terms of convergence performance and transmission latency for multiple data modals.

With the rising sophistication of cyber threats and the growing reliance on communication infrastructure, ensuring secure transmission has become a critical challenge. This work investigates the visible light covert communication for unmanned aerial vehicle (UAV) systems employing rate-splitting multiple access, aiming to conceal the very presence of transmission. Besides, the optimal detection threshold and detection error probability are derived to assess the covertness quantitatively. Through jointly optimizing the trajectory design and resource allocation, this work is devoted to maximizing the minimum average covert transmission rate (MACTR) among multiple users. Simulation results verify that the proposed algorithm substantially improves the MACTR compared to the baseline and conventional multiple access schemes, highlighting its effectiveness in enhancing the security for UAV systems.

Vision-based beam prediction has emerged as a promising alternative to pilot-driven beamforming in millimeter-wave (mmWave) communications, particularly in highly dynamic 6G environments. By leveraging visual sensing modalities, these systems enable low-latency beam selection without relying on extensive pilot transmissions. However, existing approaches are fundamentally limited by the assumption of fixed user distributions and static codebooks, thus rendering them ineffective in scenarios involving continual user arrivals and evolving beam configurations. In this paper, we propose the first continual learning framework for vision-based beam prediction that adaptively incorporates new user types and expanding codebooks without full model retraining. To ensure scalable adaptation, the proposed method integrates task-driven learning with knowledge distillation and Replay Buffer. As new user types emerge, the model incrementally expands its output space to reflect the updated codebook while retaining a compact Buffer of past samples for stability. A dual-objective optimization governs model updates, thereby combining a supervised loss over new data with a temperature-scaled divergence term that aligns current and prior model predictions on Replayed instances. This mitigates catastrophic forgetting and ensures robust performance across tasks. Experimental results demonstrate that the proposed framework effectively maintains high beam prediction accuracy under continual adaptation, while also achieving lower power loss and enhanced normalized received power relative to baseline methods.

This work explores the integration of Non-Orthogonal Multiple Access (NOMA) and Orthogonal Delay-Doppler Division Multiplexing (ODDM) within an Integrated Sensing and Communication (ISAC) framework. The proposed system leverages ODDM for high-mobility scenarios and NOMA for efficient resource utilization, thereby enabling enhanced trade-offs between communication and sensing. To achieve a balance between communication and sensing performance, an optimization problem is formulated to maximize the weighted sum of a sensing performance metric and communication throughput by jointly optimizing user grouping, power allocation, and beamforming, which are inherently coupled. To solve this problem efficiently, it is decomposed into three subproblems—user grouping, power allocation, and beamforming—which are then addressed iteratively to improve overall system performance. Simulation results validate the efficacy of the proposed framework under various mobility conditions, demonstrating improved sum-rate and sensing accuracy compared to Orthogonal Multiple Access (OMA) systems. This study offers valuable insights into advanced ISAC architectures, which are critically important for future 6G networks.

Extending wireless connectivity to remote areas is essential for delivering intelligent services in critical sectors, i.e., healthcare, agriculture, and disaster management. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their agile mobility, low deployment cost, and line-of-sight (LoS) communication capabilities. However, efficiently managing UAV resources while integrating sensing, communication, and computing (ISCC) functionalities presents significant challenges. In this paper, we propose a multi-UAV-assisted ISCC framework that simultaneously supports wireless communication links for computational task offloading, remote computing, and active target sensing. A comprehensive system model is developed, and a joint optimization problem is formulated to minimize the weighted sum energy consumption of UAVs and remote users, subject to constraints on latency, power budget, and UAV mobility. To solve the resulting non-convex problem, we design a decomposition-based solution that integrates a convex optimization technique with the deep deterministic policy gradient (DDPG) algorithm. Simulation results demonstrate the effectiveness of the proposed framework in achieving energy-efficient operation under practical system constraints.

Integrated sensing and communication (ISAC) has been recognized as a key technology in the low-altitude economy. Leveraging the flexibility and high maneuverability of unmanned aerial vehicles (UAVs), we propose a multi-UAV enabled ISAC system to provide communication and tracking services for multiple ground mobile targets (GMTs). By jointly optimizing the communication scheduling and UAV trajectory, we aim to maximize the system rate while guaranteeing tracking demands, subject to anti-collision and energy consumption constraints. Specifically, we decompose the original non-convex optimization problem into two subproblems and develop an efficient approach based on successive convex approximation (SCA) to solve them iteratively. Numerical results demonstrate that the proposed multi-UAV enabled system achieves superior communication performance through the joint optimization of scheduling and trajectories, while fulfilling real-time tracking requirements.

Optical wireless integrated sensing and communication (OW-ISAC) is rapidly advancing as a compelling alternative to its radio-frequency counterpart. In this paper, an OW-ISAC framework utilizing optical phased array (OPA) is presented, which addresses the critical challenge of angle-domain sensing in OW-ISAC by incorporating OPA beamforming. First, a comprehensive system structure is proposed to enable simultaneous optical wireless communication and profile estimation for extended targets, where the interference among different angle grids arises from the omnidirectional detection of photodiode. Then, an interference-cancellation scheme is developed based on OPA beamforming, which is further transformed into the optimization for the observation matrix and subsequently solved by a perturbation-based method. Moreover, the capabilities of concurrent reliable communication and precise sensing are validated through numerical analysis. Furthermore, the trade-off between communication and sensing performance metrics is also unveiled by tuning the optimization threshold, which enhances the practical viability of OW-ISAC.

Reliable and ultra-low-latency communication is essential for multiagent communication involving autonomous ground robots and unmanned aerial vehicles (UAVs). These mobile platforms form dynamic, multi-hop heterogeneous networks where timely delivery of critical information, such as health status or hazard detection, is vital. While average delay is commonly used, it fails to reflect the risk of rare but critical delay violations, which delay violation probability bound (DVPB) quantifies and helps predict for better planning and control. In this paper, we propose a martingale-based framework to predict the DVPB in the UAV-assisted ground robot communication networks. We specifically introduce a joint decay rate derivation method and define a stability condition to derive closed-form expressions for end-to-end DVPB in multi-hop heterogeneous networks. Simulation results demonstrate that the proposed method significantly outperforms conventional moment generating function in stochastic network calculus (MGF SNC) approaches under various network loads, hop counts, and data types. The proposed martingale-based DVPB offers accurate and reliable delay guarantees for real-world emergency communication networks.

Integrated sensing and communication (ISAC) has emerged as a key enabling technology for next-generation wireless networks, seamlessly combining communication and radar sensing functionalities by utilizing shared wireless resources. However, ISAC systems suffer from diminished sensing accuracy and compromised communication reliability in complex propagation environments, particularly in densely obstructed urban or non-line-of-sight scenarios. To cope with these issues, this paper proposes a novel backscatter device (BD)-assisted ISAC system where passive BDs are utilized to concurrently enhance sensing accuracy and communication reliability by providing additional signal-reflecting paths. Furthermore, a joint time-frequency-power resource optimization problem is formulated between sensing mutual information and communication capacity in an orthogonal frequency division multiplexing (OFDM) setting. Then, a Pareto boundary is characterized by solving this dual problem, providing clear insights into fundamental tradeoffs involved between sensing and communication. Specifically, we decompose the formulated optimization problem into two manageable subproblems and iteratively solve them through successive convex approximation (SCA) techniques with a block coordinate descent (BCD) algorithm to address the inherent non-convexity of the problem. Simulation results demonstrate the superior performance of the proposed BD-assisted ISAC system, showing substantial improvements in both sensing and communication metrics compared to state-of-the-art ISAC methods.

Social media popularity prediction is increasingly crucial for optimizing user engagement and guiding content recommendation systems. However, existing methods suffer from an excessive reliance on user information, which disproportionately influences predictions and leads to the neglect of content diversity. This oversight results in user bias, which adversely impacts the accuracy of predictions. In this paper, an approach named Feature Disentangling Dual-Stream Network (FDDN) is introduced to address this gap. In FDDN, we introduce the Multimodal Extraction Module to extract content features from different modalities. Additionally, the User Popularity Extraction Module helps to analyze the impact of user features on popularity. The Disentangled Adaptation Module distinguishes the impact of user features from content features, thus alleviating user bias and ensuring a more comprehensive prediction. Extensive experiments on a large public dataset demonstrate the robustness and effectiveness of our approach, indicating superior performance compared to existing methods.

With the development of 6G and beyond, space-air-ground integrated networks (SAGINs) have become a key factor in promoting high-speed seamless connectivity for users. In this paper, we studied a heterogeneous SAGIN in the millimeter wave (mmWave) band, which comprises unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low earth orbit satellite as aerial base stations (ABSs) in a scenario featuring partial damage to ground base stations (GBSs). Under the constraint of limited system power resources, we optimize the deployment of ABSs and the association and scheduling of users. We formulate a mixed-integer nonlinear programming (MINLP) problem aimed at maximizing the scheduling of users with quality of service (QoS) requirements and propose a block coordinate descent (BCD) based sequential quadratic programming-minimum rate ratio (SQP-MQR) algorithm to solve this problem. Simulation results demonstrate the superiority of our proposed algorithm in scheduling users with QoS requirements compared to other selected schemes in the heterogeneous SAGIN.

This paper introduces a novel approach named the Privacy-Preserving Socio-Aware Model (PSocLF) for Short-Term Residential Load Forecasting, which addresses the need for forecasting models tailored to district-specific socio-demographic characteristics. By leveraging sociodemographic characteristics and personalized socio-aware knowledge sharing, PSocLF develops district-level forecasting models that enhance load forecasting precision while safeguarding individual privacy. Within PSocLF, we propose a new model structure named the Self-Gating TSMixer (SGTSMixer), which integrates self-gating mixing procedures with stacked multi-layer perceptrons (MLPs). It can efficiently extract temporal patterns and incorporate socioaware information to improve prediction accuracy. Simulation results based on real-world data demonstrate the effectiveness of the proposed PSocLF framework, outperforming alternative training paradigms and benchmarks in model structure design, particularly in scenarios with varying sociodemographic characteristics among districts. This paper contributes to advancing federated residential load forecasting and highlights the practical benefits of integrating sociodemographic information for improved forecasting accuracy and effectiveness.

The Open Radio Access Network (O-RAN) paradigm offers a compelling solution to the constraints of traditional RAN by establishing an open framework that enables data-driven optimization at the individual user level, which is essential for the evolution of the next-generation (NextG) cellular networks. Accurate predictions of CPU demand for each user's equipment (UE) in the O-Cloud at the next step will enable more efficient CPU resource optimization. While AI is promising to optimize CPU utilization, it encounters two significant challenges. First, the varying number of UEs leads to shifts in feature dimensions, rendering the model unable to accept these inputs since the input dimension of the AI model remains fixed. Second, the ongoing introduction of new types of UEs over time with distinct CPU demands and dynamic combinations of various active devices adds further variability, thereby complicating predictive accuracy. To address the first challenge, in this research, we propose a novel dynamic number of devices management (DNDM) framework that effectively accommodates a dynamic number of devices in O-RAN, addressing the challenges associated with variable UE demands in future NextG O-RAN. We formulate an optimization problem for the second challenge, enabling the model to learn new demand scenarios while preserving knowledge from previously encountered configurations. To solve the optimization, we propose an exemplar replaybased continual adaptation (CA) framework designed to operate within the near real-time RAN Intelligence Controller (RT-RIC). The CA-DNDM actively prevents catastrophic forgetting and delivers continuous adaptability, seamlessly handling evolving UE types and quantities. Through extensive experimental results, we demonstrate that the proposed CA-DNDM framework effectively handles scenarios with varying UE counts and reliably predicts CPU demand for new situations while preserving the knowledge gained from prior scenarios.

In this paper, we investigate the optimization of power allocation and optical intelligent reflecting surface (OIRS) configuration for simultaneous lightwave information and power transfer (SLIPT) in non-orthogonal multiple access (NOMA)based visible light communication (VLC) systems, while both the line-of-sight and OIRS-reflected channels are considered. The problem is formulated to minimize the total power consumption of the whole system, which is solved by the minorizationmaximization method and relaxed iterative algorithm under various key constraints. Moreover, simulation results not only show that the proposed algorithm effectively reduces the system energy consumption while satisfying the constraints, but also demonstrate the enhancement of the system by OIRS, which is especially significant at low signal-to-noise ratio conditions.

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, “footprint fixation,” which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a$\mathbf{2 0 - 3 6 \%}$improvement in 5th percentile throughput and an$8-20 {\%}$improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2 % and significantly reducing handover frequency.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

In this paper, a multi-satellite cooperative beam hopping (BH) scenario in a low Earth orbit (LEO) satellite constellation is studied for satellite-terrestrial communications. Generally, the geographically non-uniform and time-varying packet arrival process, as well as the interference caused by dense beams, presents significant challenges in throughput maximization. To address these issues, an optimization problem is formulated to maximize long-term throughput in a multi-satellite cooperation scenario by determining the BH pattern and dynamic power allocation, while satisfying both peak and average power constraints. Subsequently, the original long-term problem is converted into decisions for each time slot through the Lyapunov drift-plus-penalty method. Moreover, the non-convex problem is solved with the successive convex approximation algorithm. Simulation results indicate that the proposed method can effectively improve throughput compared with the existing baseline, which demonstrates the effectiveness of the proposed method.

Urban Air Mobility (UAM) systems are emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, the UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet unpredictable passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate communication quality. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework that integrates diverse data sources, balancing global and local insights for responsive, realtime path planning. Experimental results demonstrate that our approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency.

The reconfigurable intelligent surface (RIS)-based carrier modulation technology, which operates without conventional radio-frequency chains, offers hardware simplicity and cost efficiency. In particular, most current RIS-based amplitude modulation schemes rely on the ON/OFF state switching or amplifier gain control of RIS elements, and ambiguity-free high-order phase modulation requires correspondingly highresolution phase shifts. However, practical hardware limitations often impose a low-resolution phase shift and near-constant reflecting amplitude of RIS elements, complicating high-order modulation. To address this, we propose an efficient composite phase shift keying (CPSK) scheme to generate virtual highorder rectangular quadrature amplitude modulation (RQAM) signals at the receiver, referred to as CPSK-RQAM. By decomposing high-order RQAM signals into multiple low-order quadrature phase shift keying signal components, with each component transmitted by a distinct RIS group, CPSK-RQAM eliminates the need for additional amplitude control and ensures robustness to phase-shift quantization errors. Unlike the similar schemes based on conventional I/Q decomposition and equal grouping, CPSK-RQAM achieves a significantly larger minimum Euclidean distance by employing a tailored grouping criterion. Furthermore, we derive the closed-form expressions for the approximate symbol error probability (SEP) of CPSK-RQAM under maximum likelihood detection. Simulation results validate the theoretical analysis and demonstrate the superiority of CPSKRQAM over the state-of-the-art schemes.

Traffic state estimation (TSE) is critical in underpinning the route planning of intelligent transportation systems (ITS). In light of vertical split traffic data might be from various entities, such as municipal authority (MA) and multiple mobility providers (MPs), vertical federated learning (VFL)-based TSE is proposed to resolve the vertical data privacy issue. However, due to discrepancies in data collection and missing data imputation technologies of MPs, the data quality of MPs regarding the same road segment might vary. To this end, we propose a reliable VFL-based TSE framework, including data provider selection and VFL model training. Concretely, given the high-dimension nature of traffic data, the MA will train a tiny mutual information (MI) model for data provider selection. After that, the MA will split the well-trained MI model into sub-models and top models and deploy them on MPs and MA, respectively, so as to preserve the nature of VFL. Eventually, upon MI models, the most representative MP of each road segment is selected for a reliable VFL model. Numerical simulation on real-world datasets shows that our framework augments the performance of traffic flow and traffic density by 11.23% and 21.15% in comparison with the baseline without data provider selection.

In this paper, an optical intelligent reflecting surface (OIRS)-aided visible light positioning (VLP) system that adopt received signal strength (RSS) fingerprint method is proposed to enhance the positioning accuracy. We employ OIRS to improve the minimal RSS difference between neighboring positions in conventional RSS fingerprint-based VLP systems, involving a two-stage positioning scheme where the OIRS is strategically aligned to refine the positioning accuracy. Subsequently, the considered problem is formulated to maximize the minimum Euclidean distance by optimizing the OIRS configuration, which is then solved by an successive convex approximation (SCA)-based algorithm. Moreover, simulation results demonstrate that the proposed OIRS-assisted VLP system substantially outperforms conventional benchmarks, thereby achieving superior positioning accuracy.

Low Earth orbit (LEO) satellites nowadays play a pivotal role in collecting images for the Earth observation. However, the images collected by satellites are possibly tremendous, which causes challenges in dealing with the satellite images. Those challenges include: 1) the unrealistic of transmitting those massive image data to the ground station for centralized analysis because of restricted satellite communication bandwidth and the data privacy issue, and 2) satellite data may be non-independent and identically distributed (non-IID). In this paper, we propose a prototype-guided federated knowledge distillation (Pro-FedKD) approach in an LEO Satellite-high altitude platform (HAP) system, which is designed based on self-knowledge distillation (SKD), federated prototype learning (FedProto) and federated learning (FL). Owing to the adoption of FL, the first challenge can be handled since FL does not require data to leave the local side. To cope with the second challenge, SKD and FedProto are employed. In addition, both model aggregation and prototype aggregation are employed on a pre-defined HAP. To enhance the effectiveness, a top-$N$model aggregation mechanism is proposed, in which among all models,$N$local models that can achieve the top$N$maximum accuracies over the validation dataset of the pre-defined HAP will be selected for aggregation. Experiments demonstrate the error rate gained by the proposed Pro-FedKD method is separately 3.76×, 3.17×, 1.55×, and 1.18× smaller than FedExP, MOON, FedProto, and pFedSD over the EuroSAT dataset, demonstrating a significant reduction. The proposed method also exhibits preeminence in other datasets.

In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are poised to advance the development of a global-scale quantum Internet. These networks leverage free space optical satellite and aerial quantum networks to complement optical fiber-based terrestrial quantum networks to enable the distribution of high-fidelity quantum entanglement over long distances. However, establishing multi-hop end-to-end quantum entanglement remains highly challenging, not only due to time-varying link conditions and structural heterogeneity inherent in SATQNs, but also because noise in quantum channels and imperfections in quantum operations can degrade the quality of entanglement. To address this challenge, we formulate an optimization problem that maximizes SATQN throughput by jointly optimizing routing path selection and entanglement generation rates (PS-EGR) while ensuring high entanglement fidelity. The resulting problem is a mixed-integer linear programming (MILP) formulation, which is NP-hard. We propose a Benders’ decomposition (BD)-based approach to solve this problem efficiently. Specifically, the MILP is decomposed into a master problem for binary routing path selection and a subproblem for continuous entanglement generation rate optimization. Numerical results validate the effectiveness of the proposed PS-EGR scheme, offering critical insights into the optimization and deployment of SATQNs.

Intelligent poisoning attacks will pose fundamental challenges for sixth-generation (6G) wireless network security due to the massive deployment of native AI in radio units as well as in core networks. Network metrics and parameters, which are inherently uncertain, can become susceptible to intelligent poisoning through native generative AI (GenAI) mechanisms. In this paper, GenAI-driven intelligent attacks in wireless networks are investigated in order to understand their impact and severity by using uncertainty-informed root cause analysis. Then, a new approach for mitigating GenAI-driven attacks is proposed through the use of trustworthy service aggregation. First, a joint decision problem is formulated for generating intelligent adversarial attacks, understanding uncertain attack severity, and mitigating them in wireless networks. Second, a novel evidencetheoretic trustworthy AI (ET-TAI) framework is developed to address the formulated problem by understanding the root-cause of the native GenAI-driven intelligent attack and establishing defense in wireless networks. In particular, the proposed ET-TAI framework enables a narrow GenAI scheme that is designed to penetrate intelligent adversarial attacks in wireless networks’ metrics and parameters. Then a Dempster–Shafer-based mechanism that is deployed to capture the uncertain behavior of those intelligent attacks through prior evidence to quantify the trust for further mitigation. Extensive experimental analysis shows the proposed ET-TAI framework’s efficacy in understanding the trust in GenAI-driven intelligent poisoning attacks on network parameters and metrics by quantifying root causes and mitigating rates. Results show that the GenAI can penetrate intelligent poisoning attacks with high reconstruction capabilities of 95% for downlink services.

The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent research has explored leveraging the reasoning capabilities of LLMs to enhance rating prediction tasks. However, existing distillation-based methods suffer from limitations such as the teacher model's insufficient recommendation capability, costly and static supervision, and superficial transfer of reasoning ability. To address these issues, this paper proposes RecZero, a reinforcement learning (RL)-based recommendation paradigm that abandons the traditional multi-model and multi-stage distillation approach. Instead, RecZero trains a single LLM through pure RL to autonomously develop reasoning capabilities for rating prediction. RecZero consists of two key components: (1) "Think-before-Recommendation" prompt construction, which employs a structured reasoning template to guide the model in step-wise analysis of user interests, item features, and user-item compatibility; and (2) rule-based reward modeling, which adopts group relative policy optimization (GRPO) to compute rewards for reasoning trajectories and optimize the LLM. Additionally, the paper explores a hybrid paradigm, RecOne, which combines supervised fine-tuning with RL, initializing the model with cold-start reasoning samples and further optimizing it with RL. Experimental results demonstrate that RecZero and RecOne significantly outperform existing baseline methods on multiple benchmark datasets, validating the superiority of the RL paradigm in achieving autonomous reasoning-enhanced recommender systems.

Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the ground because the connection between the satellites and the ground stations is intermittent, and 2) the challenge of processing the non-independent and identically distributed (non-IID) satellite data. In this paper, to cope with those challenges, we propose an orbit-based spectral clustering-assisted clustered federated self-knowledge distillation (OSC-FSKD) approach for each orbit of an LEO satellite constellation, which retains the advantage of FL that the observed data does not need to be sent to the ground. Specifically, we introduce normalized Laplacian-based spectral clustering (NLSC) into federated learning (FL) to create clustered FL in each round to address the challenge resulting from non-IID data. Particularly, NLSC is adopted to dynamically group clients into several clusters based on cosine similarities calculated by model updates. In addition, self-knowledge distillation is utilized to construct each local client, where the most recent updated local model is used to guide current local model training. Experiments demonstrate that the observation accuracy obtained by the proposed method is separately$1. 01\times, 2.15\times, 1.10\times$, and$1.03\times$higher than that of pFedSD, FedProx, FedAU, and FedALA approaches using the SAT4 dataset. The proposed method also shows superiority when using other datasets.

Large-scale low-Earth-orbit (LEO) satellite systems are increasingly valued for their ability to enable rapid and wide-area data exchange, thereby facilitating the collaborative training of artificial intelligence (AI) models across geographically distributed regions. Due to privacy concerns and regulatory constraints, raw data collected at remote clients cannot be centrally aggregated, posing a major obstacle to traditional AI training methods. Federated learning offers a privacy-preserving alternative by training local models on distributed devices and exchanging only model parameters. However, the dynamic topology and limited bandwidth of satellite systems will hinder timely parameter aggregation and distribution, resulting in prolonged training times. To address this challenge, we investigate the problem of scheduling federated learning over satellite networks and identify key bottle-necks that impact the overall duration of each training round. We propose a discrete temporal graph–based on-demand scheduling framework that dynamically allocates communication resources to accelerate federated learning. Simulation results demonstrate that the proposed approach achieves significant performance gains over traditional statistical multiplexing-based model exchange strategies, reducing overall round times by 14.20% to 41.48%. Moreover, the acceleration effect becomes more pronounced for larger models and higher numbers of clients, highlighting the scalability of the proposed approach.

Blockchain-based covert communication (BCC) provides high reliability and anonymity by embedding secret data into blockchain transactions. However, existing BCC approaches still face three fundamental limitations: (i) data mining risk, since transactions containing the secret data are permanently recorded on-chain and may be detected perpetually; (ii) limited efficiency, as only small payloads (e.g., 256 bits) can be carried per transaction; and (iii) private key leakage, where receivers often need access to the sender’s private key and may incur private key exposure. To address these issues, we propose a novel covert communication model with transaction withdrawal (BCC-TW) and a multisig-based data embedding scheme (MUL-DE). BCC-TW prevents covert transactions from being confirmed by constructing higher-fee double-spend transactions, thereby ensuring that secret data only exists temporarily in the mempool. MUL-DE encodes data into redundant public keys of Bitcoin multisig addresses, thus enabling higher efficiency and avoiding private key exposure. We implement a prototype on Bitcoin testnet and evaluate its concealment and efficiency. Experimental results demonstrate that the proposed approach achieves strong indistinguishability against statistical and deep-learning-based detectors, improves communication efficiency up to 251 bits per public key, and significantly reduces cost compared with state-of-the-art baselines.

With the development of autonomous driving technology, Multi-Access Edge Computing (MEC) is an effective paradigm to support delay-sensitive applications in vehicular networks. However, achieving the real-time offloading strategy and resource allocation in MEC-empowered vehicular networks becomes a challenge. In this paper, we first formulate an offloading optimization problem to minimize system latency and energy consumption. To obtain the optimal policy in real time, the formulated problem is transformed into a Markov Decision Process (MDP) and then solved by the proposed Attention and Feature Fusion Deep Deterministic Policy Gradient (AFF-DDPG) algorithm, where a multi-head attention mechanism is combined with feature fusion to improve the accuracy of the decision. In addition, the exploration ability and learning efficiency of the AFF-DDPG algorithm are further enhanced by exploiting Ornstein-Uhlenbeck (OU) noise and the priority experience replay mechanism. The simulation results show that the proposed AFF-DDPG algorithm achieves a 9.44 % improvement over the DDPG algorithm.

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50 % in communication rate and at least 1 dB in beampatterning error.

Orthogonal time frequency space (OTFS) modulation is widely acknowledged as a prospective waveform for future wireless communication networks. To provide insights for the practical system design, this paper analyzes the outage probability of OTFS modulation with finite blocklength. To begin with, we present the system model and formulate the analysis of outage probability for OTFS with finite blocklength as an equivalent problem of calculating the outage probability with finite blocklength over parallel additive white Gaussian noise (AWGN) channels. Subsequently, we apply the equivalent noise approach to derive a lower bound on the outage probability of OTFS with finite blocklength under both average power allocation and water-filling power allocation strategies, respectively. Finally, the lower bounds of the outage probability are determined using the Monte-Carlo method for the two power allocation strategies. The impact of the number of resolvable paths and coding rates on the outage probability is analyzed, and the simulation results are compared with the theoretical lower bounds.

Space-air-ground integrated networks (SAGINs) hold great promise in delivering ubiquitous aerial access, effectively meeting the demands for large-coverage on-demand services. Moreover, in 6G networks, the integration of Communication, Computation, Caching, and Control (4C) enables seamless connectivity, efficient data processing, optimized content delivery, and intelligent decision-making for next-generation services. However, various components like unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), satellites, and terrestrial networks each face distinct limitations. In this demo, we first showcase a SAGIN-4C-6G platform capable of establishing a high-capacity backhaul link to the core network while ensuring stable and continuous coverage. Experiments demonstrate that the proposed platform can deliver high-speed, on-demand air-to-ground (A2G) coverage with wireless backhaul, extending over an area of up to 100 km2. Beyond communication enhancement and optimization control, we also illustrate the potential for computation and caching services by deploying the proposed SAGIN platform.

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to play an important role in future sixth generation (6G) networks. Most existing works in this area focus on single-polarized XL-MIMO, where transceivers transmit and receive signals in only one polarization direction, leading to degraded data rates. To improve multiplexing performance, in this paper, we investigate downlink XL-MIMO networks with dual-polarized antennas. However, unlike conventional dual-polarized massive MIMO, the cross-polarization discrimination (XPD) of channels vary across base station antennas in dual-polarized XL-MIMO due to the enlarged antenna aperture, leading to following two challenges. First, conventional near-far field boundary is insufficient as it only accounts for phase differences across array elements while irrespective of XPD differences. Second, existing transmit covariance optimization methods developed for dual-polarized massive MIMO cannot be directly utilized, since they are developed based on uniform XPD and pathloss assumptions. To address these challenges, we model the variations of XPD across antennas, based on which a non-uniform XPD distance is introduced to complement existing near-far field boundary. Based on the new distance criterion, we propose an efficient scheme for optimizing the transmit covariance, which considers the non-uniform XPD and pathloss. Numerical results validate our analysis and demonstrate the effectiveness of the proposed algorithm.

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments.

The integration of reconfigurable intelligent surfaces (RIS) into unmanned aerial vehicle (UAV) networks presents a transformative solution for achieving energy-efficient and reliable communication, particularly within the rapidly expanding low-altitude economy (LAE). As UAVs facilitate diverse aerial services—spanning logistics to smart surveillance—their limited energy reserves create significant challenges. RIS effectively addresses this issue by dynamically shaping the wireless environment to enhance signal quality, blackuce power consumption, and extend UAV operation time, thus enabling sustainable and scalable deployment across various LAE applications. This survey provides a comprehensive review of RIS-assisted UAV networks, focusing on energy-efficient design within LAE applications. We begin by introducing the fundamentals of RIS, covering its operational modes, deployment architectures, and roles in both terrestrial and aerial environments. Next, advanced energy efficiency (EE)-driven strategies for integrating RIS and UAVs. Techniques such as trajectory optimization, power control, beamforming, and dynamic resource management are examined. Emphasis is placed on collaborative solutions that incorporate UAV-mounted RIS, wireless energy harvesting (EH), and intelligent scheduling frameworks. We further categorize RIS-enabled schemes based on key performance objectives relevant to LAE scenarios. These objectives include sum rate maximization, coverage extension, quality of service (QoS) guarantees, secrecy rate improvement, latency blackuction, and age of information (AoI) minimization. The survey also delves into RIS-UAV synergy with emerging technologies like multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), vehicle-to-everything (V2X) communication, and wireless power transfer (WPT). Finally, we outline open research challenges and future directions, emphasizing the critical role of energy-aware, RIS-enhanced UAV networks in shaping scalable, sustainable, and intelligent infrastructures within the LAE.

Unmanned aerial vehicles (UAVs) have become essential in advancing multi-access edge computing (MEC), providing flexible platforms that enhance network capacity, coverage, and efficiency while reducing latency and improving communication quality. Integrating reconfigurable intelligent surfaces (RIS) with UAV-based MEC systems further elevates these capabilities, delivering significant gains in computational power, energy efficiency (EE), and physical layer security (PLS). However, managing the complexity of RIS within UAV networks requires sophisticated optimization strategies. This survey offers a comprehensive analysis of the fundamentals of RIS, UAVs, and MEC, followed by an in-depth examination of RIS configurations in UAV-based MEC systems, including static, dynamic, and hybrid models. We evaluate the benefits and challenges of RIS integration, such as improved communication, enhanced computational efficiency, optimized energy use, better task management, and strengthened security. In addition, the survey explores the latest advancements in RIS-assisted UAVs for MEC, focusing on boosting computational capacity, minimizing delay, maximizing EE, and enhancing security. To provide a thorough exploration of these topics, detailed summary tables are included, offering a comparative analysis of methodologies, performance metrics, and scenarios from recent studies. Furthermore, the survey presents key lessons learned from current research and identifies future research directions crucial for fully realizing the potential of RIS-enhanced UAV-based MEC systems in next-generation networks.

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks.

In this paper, a novel integrated control and communication (ICC) framework is proposed for vehicle platoons, ensuring platoon stability while optimizing energy consumption for communications. To address the high signaling overhead and long delays caused by multi-link competition in radio-frequency-based vehicle-to-vehicle communications, visible light communications (VLCs) are employed, allowing adjacent vehicles to establish collision-free links and simultaneously transmit motion status information (MSI) for platoon control. Besides, age of information (AoI) is utilized to quantify the heterogeneous timeliness of the MSI. Building upon this, a distributed linear control strategy is designed for vehicle platoons with a multi-predecessor-leader-multi-following information topology (IT) and heterogeneous AoIs. Afterward, through stability analysis, the maximum allowable AoI threshold is obtained to guarantee both internal stability and input-state string stability under external disturbances. Accordingly, taking the AoI threshold as a constraint, a long-term energy minimization problem is further formulated to optimize the energy consumed by multi-source, multi-hop, and multicast intra-platoon VLC while maintaining platoon stability. To solve this problem, an online distributed information scheduling policy based on Lyapunov optimization is proposed. Finally, simulation results reveal that the proposed ICC framework effectively maintains platoon stability across various ITs, while significantly reducing the energy consumption for communications to no more than 40% of that achieved by multiple baselines, thus highlighting its substantial potential in vehicle platoons.

In multicell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and intercell interference, leading to unfairness among vehicles. In this article, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application.

The widespread deployment of Internet of Things (IoT) devices has intensified the demand for scalable and secure trust management IoT systems. Existing GNN-based approaches often neglect real-time adaptability and contextual trust in dynamic, heterogeneous networks. This study introduces TrustAware-GNN, a trust-aware graph neural network framework designed to robustly evaluate device trustworthiness in IoT environments. The model integrates a multi-dimensional trust mechanism encompassing direct, indirect, temporal, and contextual trust, computed using localized device parameters: reliability, capability, security posture, reputation, and location awareness. Trust values modulate edge weights within the graph, enabling trust-adaptive message propagation. A trust-threshold-based edge formation mechanism filters unreliable links, while attention-based aggregation refines node embeddings. The model continuously adapts to behavioral shifts via time-decayed trust updates and contextual similarity matching. Simulation was conducted across IoT-23, EDGE-IIoTSET, AutoTrust, and ToN-IoT datasets. TrustAware-GNN achieved 94.83% accuracy on EDGE-IIoTSET and 93.25% on IoT-23, outperforming MGNN, STAR-GCN, and SEGC-PP in both accuracy and adaptability under dynamic trust scenarios.

In the rapidly evolving domain of the Internet of Vehicles (IoV), ensuring robust trust management, privacy, and security presents significant challenges. This article proposes a novel approach integrating generative AI (GAI) and federated learning (FL) to address these challenges. FL allows distributed learning across vehicles without the need to share data, enhancing privacy compared to centralized methods. Our approach enhances trust management by raising the level of accuracy in detecting anomalies and preserving data privacy. As a result, the effectiveness of the proposed approach in practical real-world urban settings is illustrated by comprehensive evaluations using the CityPulse dataset. The results show a 20% improvement in trust scores under normal conditions, a 92% anomaly detection accuracy, and acceptable latency despite the added security measures. Additionally, 3-D visualizations illustrate the system’s robustness and scalability. This solution aligns with the objectives of 6G wireless communications, laying the groundwork for future intelligent, ultrareliable, and secure vehicular networks. Future research will focus on expanding the application of GAI and FL for real-time decision-making in large-scale IoV networks and optimizing cryptographic protocols.

Ensuring data security and privacy in Internet of Things (IoT) is increasingly critical due to the growing interconnectedness of devices and the sensitivity of the data they handle. This paper presents a novel approach to enhancing data security in IoT through the integration of homomorphic encryption and blockchain technology. We conduct simulations using the Kaggle Smart Home Dataset to evaluate the effectiveness of our proposed methodology on smart home devices and wearable technology. Our approach not only secures data transmission but also guarantees data integrity and privacy through decentralized verification and secure aggregation techniques. Specifically, our evaluation demonstrates an encrypted data transmission rate exceeding 99.5%, a complete absence of unauthorized access instances in the simulated environment, and a verified data integrity rate of over 99.8%. Additionally, our method supports real-time processing and scalability, making it suitable for various IoT applications, including smart contract applications in IoT for privacy and security in supply chain transactions. The study highlights the robustness of combining homomorphic encryption and blockchain to protect sensitive data throughout its lifecycle.

Integrated terrestrial-nonterrestrial networks have recently gained much attention because they can bridge the gap between the conventional terrestrial infrastructure and nonterrestrial networks. In addition to seamless connectivity, such networks can offer edge computing services to the users with real-time data processing demand. In this article, an integrated terrestrial-nonterrestrial network with multiaccess edge computing (ITNT-MEC) system is considered in which the aerial users (AUEs) share the resources of terrestrial base stations (TBSs) with their existing terrestrial users (TUEs) and that of low-Earth orbit (LEO) satellites with their neighboring satellites. The goal is to minimize the total energy consumption of AUEs, TUEs, and LEO satellites by jointly optimizing the AUE-TBS/LEO satellite association, AUEs’ trajectories, task allocation, as well as network resource allocation. Due to the dynamic nature of network environment and nonconvex characteristics, it is significantly challenging to solve the formulated optimization problem. Therefore, a block coordinate descent (BCD)-based algorithm that integrates deep reinforcement learning (DRL) methods, such as double deep Q-learning network (DDQN), deep deterministic policy gradient (DDPG), and convex optimization methods, is proposed. Simulation results show that the total energy consumption in the proposed approach is reduced by 14%, 26.9%, 34%, 35.8%, 45.5%, and 55.4%, respectively, when compared to the baselines, such as DDPG-based task offloading (DDPG-TO), DDQN-based task offloading (DDQN-TO), DQN-based task offloading (DQN-TO), equal resource allocation (ERA), random association (RA), and fixed trajectory (FT).

In the Internet of Vehicles (IoV), developing accurate road information models is essential for analyzing perception data gathered from multiple vehicles. However, traditional centralized data-sharing methods can compromise the privacy and security of data providers. federated learning (FL) presents a promising solution as a distributed machine learning approach that balances data privacy protection with efficient utilization by keeping data localized and sharing only model updates. Nevertheless, conventional FL strategies often fail to adequately address differences in resource investment and data quality among participating vehicles while aggregating local training results. This oversight can lead to inequitable model aggregation and distribution, reducing the motivation for vehicles to share their data. This article proposes a reputation evaluation-based, fair, and secure FL scheme for the IoV to address these challenges. In this scheme, the aggregation node utilizes fuzzy comprehensive evaluation to assess the training outcomes of participating vehicles and assigns aggregation weights accordingly. It also calculates reputation values for each vehicle using periodic averaging methods. Subsequently, the node implements differentiated global model compression and distribution based on these reputation scores. Experimental results indicate that the proposed scheme performs comparably to established algorithms while effectively evaluating vehicle reputations. It achieves model compression and equitable distribution, demonstrating an ability to identify and counteract malicious client attacks. Consequently, this approach enhances fairness and security in FL systems designed for the IoV.

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the Industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrates that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

This paper addresses the challenges faced by air-ground vehicle networks (AGVN), with a focus on the rapid mobility of both unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) clusters. UAVs employ the non-orthogonal multiple access (NOMA) transmission technique to efficiently transfer data to UGV, aiming to enhance network performance by increasing spectral efficiency and supporting simultaneous transmissions. The system integrates UAV into the network perception environment, enabling cognitive activities and convenient transmission for UGV beyond base station coverage. We provide closed-form outage probability (OP) expressions for UAVs and UGV in two assist modes, i.e., amplify-and-forward UAV (A-UAV) and decode-and-forward UAV (D-UAV), using real-world situations with double Rayleigh fading (DRF) and outdated and imperfect channel state information (ipCSI). Additionally, in both relaying modes, the asymptotic expression for the OP is provided in the high SNR regime for UAV and UGV links. In addition, the system’s diversity order is derived, and the asymptotic analysis shows an intricate interplay of relative speed, channel outdatedness, and OP. Our research results show that when the relative speed decreases or the power increases, the OP decreases. Under the same conditions, the performance of A-UAV is more stable than that of D-UAV. Additionally, the study underscores the importance of assist mode and the number of UAVs in achieving optimal outage performance. Simulation results confirm our analytical results, emphasizing the trade-offs and performance indicators for AGVN system design. The study demonstrates potential avenues for maximizing system performance even within stringent constraints through strategic parameter adjustments.

Integrating federated learning (FL) with blockchain technology provides an effective solution for enhancing data security and privacy in decentralized IoT ecosystems. However, challenges arise in efficiently achieving consensus and balancing the load across multiple shards, primarily due to the resource constraints and heterogeneity of IoT devices. This paper introduces SynergyMining, a novel collaborative sharding consensus mechanism designed specifically for blockchain-based FL in IoT. SynergyMining leverages reinforcement learning to dynamically optimize consensus group selection, ensuring balanced workloads across shards and efficient resource utilization. Additionally, we propose a freshness and quality-aware FL framework with asynchronous model aggregation called FedFQ that dynamically adjusts aggregation weights based on the recency and quality of local models. This approach mitigates client instability and improves the efficiency of the global model aggregation process. Experimental results demonstrate that SynergyMining outperforms leading algorithms, including Monoxide, HMM, Elastic and ORSP across key performance metrics. Specifically, compared to these algorithms, SynergyMining improves system throughput by 9.97% to 72.16%, final model accuracy by 1.18% to 5.53%, and reduces load imbalance by 6.65% to 16.29%. These advancements, combined with the freshness-aware aggregation, make SynergyMining a robust and scalable solution for IoT-based FL applications, offering significant improvements in efficiency, scalability, and security.

With the rapid development of Internet of Things (IoT), efficient and reliable massive IoT device connections need to be widely supported in the upcoming next-generation communication networks, especially for emerging high-mobility scenarios. In this context, this paper investigates massive grant-free random access (GF-RA) in high mobility scenarios, focusing on active device detection (ADD) and channel estimation (CE) under fast time-varying channels. By exploiting the inherent low-rank structure of the observed pilot-signal-tensor, a tensor-based GF-RA transmission scheme is provided. On this basis, we propose a joint ADD and CE method based on the canonical polyadic (CP) model for both sourced and unsourced RA frameworks. More specifically, by remodelling the observation signal as a third-order tensor, the channel parameters can be grouped in the factor matrices of the CP model. However, the excessive number of potential device connections in massive GF-RA scenarios lead to excessively large dimensions of the factor matrices, thus resulting in severe ill-condition. To solve this problem, the Vandermonde structure of factor matrices is developed, which enables the effective exploitation of the tensor subspace for CP decomposition. Then, by utilizing the pre-allocated training precoders, an effective two-dimensional search method is proposed to jointly detect active devices and initialize the iterative estimation of channel parameters. Finally, due to the grouping situation, independent and coupled channel parameters are estimated by appropriate methods based on maximum likelihood (ML) and iterative updating, respectively. Moreover, the pre-allocation of training precoders can be unified to the unsourced RA scenarios, where the joint ADD and CE can be regard as a simple degenerate method compared to sourced RA. Simulation results demonstrate that the proposed tensor-based GF-RA framework outperforms the state-of-the-art schemes in terms of both ADD and CE performance.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

In this paper, we propose a time-division integrated sensing, communication and control (ISCC) scheme designed to dynamically enhance communication and sensing capabilities on the UAV platform. The UAV is dispatched to track a randomly moving target for capturing and transmitting sensing data to the base station via wireless communication. The goal is to leverage the ISCC framework for maximizing the cumulative sensing mutual information while guaranteeing successful data transmission by optimizing the allocation of the communication and sensing time slots together with the UAV’s control scheme. The formulated problem cannot be straightforwardly solved by off-the-shelf optimization algorithms due to the time-varying environment. To tackle this challenge, a constrained soft actor-critic (C-SAC) algorithm is developed, which dynamically switches between maximizing rewards and minimizing constraint violations to ensure robust performance in changing environments while maintaining the simplicity and efficiency of unconstrained policy optimization. Simulation results demonstrate that the proposed C-SAC algorithm outperforms dual-variable-based methods in handling the constrained problems, while extensive Monte Carlo tests confirm the robustness of the ISCC policy trained by the proposed algorithm, which adapts to varying target speeds and achieves higher cumulative mutual information compared to the point-mass UAV models.

With the rapid advancement of intelligent railway systems, a high-quality train-ground communication system is crucial. However, ensuring reliable wireless communication in ultra-high-speed environments remains a significant challenge due to severe Doppler effects, frequent inter-cell handovers, and diverse QoS demands. Existing solutions, such as soft/hard handover schemes, lack the flexibility to adapt to dynamic conditions, leading to service interruptions and suboptimal performance. In this paper, we propose a dynamic resource allocation strategy based on dual base station coordination, utilizing millimeter-wave (mmWave) technology and real-time train position prediction. This approach dynamically optimizes user association and spectrum allocation to accommodate the rapid movement of trains, reducing service interruptions and ensuring continuous high-quality communication. Simulation results show that our algorithm improves system QoS satisfaction by 37.5%-68.8% and achieves spectrum utilization rates between 76% and 90%, outperforming the comparison schemes. These results validate the effectiveness of our approach in addressing high-speed mobility challenges.

The Ambient Internet of Things (AIoT), recently standardized by the 3rd Generation Partnership Project (3GPP), demands a low-power wide-area communication solution. Ambient Backscatter Communication (AmBC) is a promising approach that enables Backscatter Devices (BDs) to communicate by reusing existing signals without additional spectrum or energy cost. This paper investigates the feasibility of AmBC in the uplink of Long Term Evolution (LTE) cellular systems. We model the received BD signal, derive the optimal Maximum A Posteriori (MAP) detector, and propose simplified receiver architectures that achieve near-optimal performance with reduced complexity. A theoretical Bit Error Rate (BER) analysis is developed, including a Gaussian approximation, and the achievable coverage is evaluated. The results show that AmBC can sustain a BER on the order of 10−2at a reading distance of 1.5 m. Over-the-air experiments using commercial LTE uplink Sounding Reference Signals (SRS) further confirm the theoretical and simulation results. Overall, this work demonstrates that LTE cellular systems can natively support AmBC in the uplink, reinforcing its potential as a symbiotic radio paradigm for future low-power, spectrum-efficient AIoT connectivity.

The deployment of space–air–ground integrated networks (SAGIN) is critical for addressing signal coverage challenges in high-speed railway (HSR) tunnel scenarios. However, when aerial access networks and ground base stations (BSs) simultaneously serve trains, co-frequency interference can severely degrade system performance, preventing the satisfaction of Quality of Service (QoS) requirements for flows. To address this issue, this article formulates an optimization problem aimed at maximizing the number of scheduled flows through transmission optimization for HSR tunnel communications in SAGIN. Subsequently, a link selection algorithm is proposed to identify valid transmitter-receiver associations by filtering potential link combinations based on the signal-to-interference-plus-noise ratio (SINR) threshold, ensuring that only feasible associations are selected to meet each flow’s QoS requirement. To improve the number of successfully scheduled flows, a graph theory-based transmission optimization (GTTO) algorithm is developed. This approach effectively avoids the simultaneous transmission of highly interfering links by introducing an interference factor and reorders the scheduling sequence of flows to prioritize those associated with links that have higher SINR. The proposed method is evaluated through simulations, demonstrating its ability to substantially improve the number of scheduled flows and system throughput under varying conditions, such as different train speeds, QoS requirements, airship altitudes, tunnel lengths, and the number of mobile relays (MRs). The results also demonstrate the superiority of the proposed method over conventional approaches, achieving reliable communication and enhanced performance across HSR tunnel scenarios.

Uncrewed aerial vehicles (UAVs) have gained significant attention due to their low cost and efficient deployment. Specifically, UAV-based communications hold promise to supplement the coverage limitations of terrestrial networks and achieve superior connectivity for the Internet of Things (IoT). Moreover, UAVs with visible light communication (VLC) capability do not interfere with existing radio frequency (RF) communication infrastructure, thereby providing flexible, high-throughput on-demand wireless communications. In this article, considering the limited battery capacity of UAVs, we aim to maximize the energy efficiency (EE) of a multi-UAV downlink VLC system for IoT. To this end, the system model with co-channel interference is first established, followed by the formulation of the joint spatial deployment and resource allocation problem. Besides, the original problem is decomposed into three subproblems, focusing on spatial deployment, subchannel assignment, and power allocation. By employing the block coordinate descent (BCD) method, the three subproblems are solved alternately. Moreover, simulation results demonstrate that the proposed method achieves superior improvements in EE compared to other baselines. In addition, the convergence and computational complexity of the proposed algorithm, as well as the impacts of key parameters on the overall system performance are also investigated.

Optical intelligent reflecting surface (OIRS) is a promising technology in visible light communications (VLCs), which can help VLC overcome the shortcoming of being susceptible to occlusion. It is shown that OIRS has many advantages for nonorthogonal multiple access (NOMA)-based VLC due to its ability to reconfigure optical wireless channels. In this article, we propose an effective OIRS-aided physical layer security (PLS) scheme for NOMA-based VLC networks against multiple eavesdroppers (Eves). By exploiting artificial noise (AN) to jam Eves, the maximization problem of the minimum achievable secrecy data rate is investigated, subject to successive interference cancellation (SIC) decoding conditions and OIRS constraints. Specifically, the original problem is decomposed into the power allocation and OIRS configuration subproblems by a block coordinate descent (BCD) algorithm. Moreover, the semi-definite relaxation and successive convex approximation are employed to solve the subproblems, after which a stochastic probability assignment method is adopted for the integer OIRS constraint. Finally, simulation results demonstrate that AN can enhance the system performance in most scenarios, and the minimum achievable secrecy data rate can be significantly improved by the proposed algorithm, demonstrating the potential of OIRS for enhancing PLS in optical wireless communications.

Low-Earth orbit (LEO) satellite networks can achieve global network coverage without geographical restrictions and are essential to the future communication network. In this article, we study the computing offloading problem in a satellite-terrestrial integrated network for the Internet of Remote Things (IoRT), which aims to reduce the total cost (weighted sum of energy consumption and delay), and jointly offload node selection, offloading ratio, and computational resource allocation to achieve the dynamic management of network resources. First, we propose a hybrid cloud and satellite multilayer multiaccess edge computing (MEC) network architecture that can provide heterogeneous computing resources to terrestrial users. Subsequently, since the problem under consideration is a mixed-integer nonlinear programming problem, we propose a computing offloading algorithm for multiagent reinforcement learning, which is an integration of double deep Q learning (DDQN) and deep deterministic policy gradient (DDPG). The algorithm can learn the optimal policy for actions containing a mixture of discrete and continuous variables. Finally, an optimal computational resource allocation scheme is proposed to improve the task computation efficiency. Simulation results show that the proposed task offloading and resource allocation scheme can achieve reasonable scheduling of computational tasks and optimal allocation of computational resources, reducing the cost of task computation.

In intelligent connected vehicle applications, tasks such as path planning and health management involve numerous matrix operations, particularly matrix multiplication. Due to limited resources, these tasks are often outsourced to the edge server. However, outsourcing these tasks involving matrix multiplication might incur potential risks, such as returning incorrect results to expedite processing or even exposing sensitive data during the computation. Privacy-preserving verifiable matrix multiplication schemes address these concerns. However, it is meaningful in practice only if the verification and decoding time is lower than that of local computation. In this paper, we propose a privacy-preserving verifiable matrix multiplication for intelligent connected vehicles that further reduces the verification and decoding time. To achieve this, we first reduce the length of the ciphertext of linearly homomorphic encryption when encrypting a group of messages. Subsequently, we construct our verifiable matrix multiplication scheme based on the improved linearly homomorphic encryption. It has a lower critical dimension than the state-of-the-art scheme with a similar security level, since the shorter ciphertext and the simpler linearly homomorphic encryption algorithm. Performance analysis and experimental results demonstrate that the critical dimensions of our improved scheme are reduced by 23.3%, while the communication cost is reduced by 68.3%, making it particularly suitable for intelligent connected vehicle applications.

With the rapid development of 6G communication and the wide deployment of high-speed rail (HSR), it becomes essential to enhance the utilization of HSR communication resources while ensuring the requirements of communication-sensitive users for high reliability and low latency. Meanwhile, the development of integrated sensing and communication (ISAC), brings more inspiration for smart HSR. In this background, we model an ISAC and hyper-reliable low-latency communication (HRLLC) system for UAV-assisted HSR. We formulate a mixed integer nonlinear programming problem (MINLP) with the objective of maximizing the fair sum rate while satisfying the minimum radar sensing requirement. To solve this problem of nonconvex and high coupling, we propose a hybrid particle swarm optimization-genetic algorithm (PSO-GA) that combines the fast convergence of PSO-only (PSO) and the strong global search ability of GA, with parameter-free penalty functions. Through careful design, PSO-GA dynamically balances the exploration and development capabilities. It achieves the best overall performance with a faster convergence speed than existing algorithms. An average improvement of 29%, 57%, and 42% has been achieved with different numbers of passengers, total transmission power, and number of resource blocks. This article supports the future development of intelligent HSR communication.

Federated learning (FL) enables privacy-preserving model training across decentralized data. However, significant data heterogeneity, common in domains like the Internet of Things (IoT), hinders generalization. Federated Domain Generalization (FDG) extends the FL paradigm by aiming to train models that generalize effectively to unseen domains, without requiring access to data from those domains during training. Current FDG methods primarily use ResNet backbones pre-trained on ImageNet-1K, limiting adaptability due to architectural constraints and limited pre-training diversity. This reliance has created a gap in leveraging advanced architectures and diverse pre-training datasets to address these challenges. To bridge this gap, we present the first comprehensive investigation into the efficacy of advanced pre-trained architectures such as Vision Transformers, ConvNeXt, and Swin Transformers, in enhancing FDG performance. Unlike ResNet, these architectures capture global context and long-range dependencies, making them well-suited for FDG. Beyond architectural evaluation, we systematically assess the impact of diverse pre-training datasets and compare self-supervised and supervised strategies. Our analysis rigorously investigates the influence of architectural depth, parameter efficiency, and the interplay between diverse model families and dataset characteristics on FDG performance. We find that advanced architectures pre-trained on large datasets significantly outperform ResNet models. Specifically, ConvNeXt architectures outperform all other candidates. We find self-supervised methods using masked image patch reconstruction via discrete token prediction outperform their supervised counterparts. We observe that certain advanced model variants with fewer parameters outperform larger ResNet models. This underscores the need for advanced architectures and scalable pretraining to enable efficient and generalizable FDG.

Non-terrestrial network (NTN) enabled low-altitude economy (LAE) has emerged as a promising economic paradigm that leverages advanced air mobility (AAM) vehicles to revolutionize connectivity in the six-generation (6G) era. By deploying unmanned aerial vehicles (UAVs) as flying edge nodes, wireless caching can significantly alleviate network congestion and reduce latency, enabling the efficient handling of massive terrestrial user requests in LAE applications. However, the limited energy and storage capacity of UAVs pose significant challenges to provide persistent and diverse content delivery services. To address such limitations, this paper proposes a multi-UAV-enabled coded caching scheme for energy-efficient data delivery, in which both the communication coverage and cache hit are satisfied. Taking into account the dynamics of user mobility and user preferences, we design an energy minimization problem with the joint optimization of coding vectors, caching variables, user grouping, and updated UAV locations. We initially deploy UAVs using a constrained K-means clustering algorithm based on user locations, and evaluate the clustering effectiveness with the silhouette coefficient. Then, we solve this problem by proposing a multi-UAV enabled coded caching optimization (MUCCO) scheme, embedded with a novel projected distance-based user grouping method, semidefinite programming (SDP), and matching theory. The simulation results demonstrate that the proposed MUCCO scheme can achieve low energy consumption compared to other schemes, with scalable user density and file library size.

As one of the six usage scenarios of the sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) is regarded as a key enabler for the future Internet of Everything (IoE). While numerous studies have been conducted in radio-frequency (RF)-ISAC, free-space optical (FSO)-ISAC is also laying the foundation for the era of connection and intelligence. In this article, a direct-current (DC)-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) scheme is proposed for FSO-ISAC. To derive the performance metrics for communication and sensing, we model the clipping noise of DCO-OFDM as additive colored Gaussian noise and establish an equivalent frequency-selective channel for FSO-ISAC. In addition, joint power allocation problems are formulated for both communication-centric and sensing-centric scenarios based on the derived performance metrics. Subsequently, these nonconvex joint optimization problems are decomposed into subproblems for DC bias and subcarriers, which can be solved by block coordinate descent algorithms. Furthermore, numerical simulations demonstrate the effectiveness of proposed methods and reveal the tradeoff between communication and sensing functionalities of FSO-ISAC based on DCO-OFDM.

Ultraviolet (UV) communication research has prioritized channel modeling for its critical role in system optimization. Current non-line-of-sight (NLoS) UV modeling mainly addresses obstacle-free scenarios and single-obstacle situations: the former manifests constrained applicability at small transceiver elevation angles with obstacle susceptibility, while the latter suffers from high modeling complexity and can only handle one-obstacle scenarios, which pose critical challenges for Internet of Things applications. To overcome these limitations, we propose a single-collision model for short-range NLoS UV channels incorporating both scattering and reflection effects. Initially, the impact of air scattering on the received pulse energy is presented for diverse obstacle situations, where an obstacle-boundary approximation method (OBAM) is developed to reduce the modeling complexity. Besides, the dimensions, coordinates, shapes, orientation angles, and number of obstacles are considered to emulate practical environments. Subsequently, the impact of obstacle reflection on the received pulse energy is investigated for single, double, and multiple obstacle situations. On this basis, we account for certain scenarios where obstacle surfaces comprise multiple sub-regions, each characterized by distinct reflection coefficients attributed to their varying material compositions. Moreover, we verify the proposed model by comparing it with the Monte-Carlo photon-tracing (MCPT) model and the obstacle-free integral model via simulations. These results demonstrate that the path loss curves obtained by the proposed model exhibit close alignment with those simulated by the MCPT model, while its calculation time is less than 10% that of the MCPT model. Additionally, when obstacle reflection is prominent, the assessment error of the proposed OBAM can be ignored in estimating the path loss of NLoS UV channels containing obstacles.

High reliability is crucial for stable and accurate control in wireless Internet of Things (IoT) systems. Although average outage probability is a widely adopted metric for evaluating the reliability of wireless IoT systems, it does not account for the sparsity of outage occurrences, which can be interpreted as the frequency of outage occurrence within a certain time. Compared to an isolated single outage, clustered outages can significantly impact stability. To address this problem, we introduce the concepts of virtual queue and consecutively effective throughput. The virtual queue treats outage packets as arrivals, with its service rate determined by the desired frequency of the single outage. In contrast to the traditional throughput, consecutively effective throughput measures the effectiveness of consecutive success of transmissions. Based on these concepts, we then consider maximizing the consecutively effective throughput under virtual queue constraints. Theoretical analysis is conducted to derive the optimal rate for this problem. Specifically, we explore two scenarios: 1) outages caused by packet decoding errors and 2) outages due to packet decoding errors and latency violations. Considering the nonasymptotic property of the virtual queue, queueing theory is utilized to provide closed-form expressions for virtual queue constraints. Based on the theoretical analysis, efficient and effective algorithms are proposed to obtain the optimal rate based on the above analysis. Numerical comparisons between grid searches and our algorithms validate the correctness of our theoretical analysis. This study underscores the importance of specific scheduling designs to control clustered outages, rather than merely enhancing throughput in wireless IoT systems.

Data aggregation is essential in near-ground long chain sensing networks, as it ensures data freshness and stability while extending communication range through path planning and traffic scheduling. However, the dynamic and fragile nature of long-chain networks under near-surface interference—particularly the presence of time-varying network links—poses significant challenges, for which no effective aggregation solution currently exists. This paper focuses on a representative ground-based distributed seismic node long-chain network and introduces a Weight Agnostic Structure-Free (WASF) data aggregation method based on artificial neural networks. WASF regulates packet forwarding paths and waiting times via activation functions and employs shared connection weights to identify the globally optimal aggregation node, thereby enabling efficient data aggregation. Simulation results demonstrate that, compared with state-of-the-art aggregation methods, WASF reduces end-to-end latency and packet loss rate by 11.8% and 9.4%, respectively. Field deployment on GEIWSR-III seismic nodes further confirmed its effectiveness, yielding 84.3% lower energy consumption, 41% reduced latency, and 20.2% fewer packet losses. By enabling structure-free aggregation, WASF provides a reliable reference framework for efficient, robust, and scalable communication in long-chain IoT networks, such as tunnels, pipe galleries, rivers, and railways.

Satellite edge computing promises to provide ubiquitous computation services to meet users’ increasing demands for wide range and high quality of experience (QoE) services by leveraging its global coverage capabilities. However, the highly dynamic variations of low earth orbit (LEO) satellite channels and the uneven distribution of satellite computing resources lead to the difficulty of traditional algorithms and basic reinforcement learning methods to meet the requirements of low delay, low energy consumption and few handovers. Therefore, in this paper, we formulate an optimization problem to jointly design the access selection, computation offloading, and resource allocation in LEO ubiquitous edge computing (UEC) networks to minimize the objective function weighted by delay, energy consumption and handover overhead. To solve this formulated challenging problem, we develop an alternating asynchronous dueling deep Q-network with centralized training distributed execution (Alt-ADDQN-CTDE) algorithm. The proposed method considers the multi-user competitive game and optimal allocation of computation resources, and then finds the optimal decision scheme under convergence by alternately updating the network parameters.Extensive simulations demonstrate that our proposed method is superior in performance and reduces the average optimization objective up to approximately 22.18%, compared with other benchmark methods. Therefore, our proposed method can effectively minimize the delay while minimizing the energy consumption and handover rate as much as possible.

Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

Non-orthogonal multiple access (NOMA)-based visible light communication (VLC) is considered a promising technique for next generation high-speed wireless communications. The emerging optical intelligent reflecting surface (OIRS) offers significant benefits to the NOMA-based VLC, since it can mitigate signal blockage issues of VLC and improve its performance by manipulating channel qualities of users. This paper investigates the enhancement of OIRS to the NOMA-based multi-cell VLC system and explores the resource allocation problem for the energy efficiency (EE) maximization. To this end, the channel gains of the system are discussed, considering both the line-of-sight and OIRS-reflected paths. Then, the system model of NOMA-based VLC is established and the optimization problem is formulated to maximize the overall EE of the system. Next, the original optimization problem is decomposed into three sub-problems, which are solved by the proposed algorithms iteratively. Moreover, the simulation results demonstrate the convergence of the proposed algorithm and the enhancement in EE achieved by OIRS, as well as the influence of key parameters on the system performance, which can offer insights on resource allocation for NOMA-based VLC systems with OIRS.

Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), while its estimation problem has not been studied yet. To bridge such a gap, this paper proposes a new and customized OIRS channel estimation protocol with joint space-time sampling under the alignment-based OIRS channel model. First, we unveil the spatial and temporal coherence characteristics and derive OIRS coherence distance and coherence time in closed form. Next, to achieve dynamic beam alignment for pilot transmission within the coherence time, we propose to tune the rotation angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Then, given the beam alignment within the considered coherence time, a sequential OIRS channel estimation method is proposed, where the OIRS is divided into multiple subarrays based on the coherence distance. The CSI for each subarray is estimated sequentially, followed by a space-time interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of the proposed OIRS channel estimation protocol as compared to benchmark schemes.

As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: 1) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and 2) cannot evaluate the channel path loss for the wide beam and wide FoV scenarios with existing simplified single-scattering path loss models. To address these challenges, a UV NLoS scattering model incorporating an obstacle was investigated, where the obstacle’s orientation angle, coordinates, and geometric dimensions were taken into account to approach actual application environments. Then, a UV NLoS reflection model was developed combined with specific geometric diagrams. Further, a simplified single-scattering path loss model was proposed with a closed-form expression. Finally, the proposed models were validated by comparing them with the Monte-Carlo photon-tracing model, the exact single-scattering model, and the latest simplified single-scattering model. Numerical results show that the path loss curves obtained by the proposed models agree well with those attained by related NLoS models under identical parameter settings, and avoiding obstacles is not always a good option for UV NLoS communications. Moreover, the accuracy of the proposed simplified model is superior to that of the existing simplified model for all kinds of transceiver FoV angles.

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality.

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground deployments in both communication and computing domains. In such networks, aerial platforms, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model framework, in which UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes.

Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively through incremental advances to conventional wireless technologies like metasurfaces or holographic antennas. While the 6G concept of artificial intelligence (AI)-native networks promises to overcome some of the limitations of existing wireless technologies, current developments of AI-native wireless systems rely mostly on conventional AI tools such as auto-encoders and off-the-shelf artificial neural networks. However, those tools struggle to manage and cope with the complex, nontrivial scenarios faced in real-world wireless environments and the growing quality-of-experience (QoE) requirements of the aforementioned, emerging wireless use cases. In contrast, in this article, we propose to fundamentally revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. Our envisioned AGI-native wireless systems acquire common sense by exploiting different cognitive abilities such as reasoning and analogy. These abilities in our proposed AGI-native wireless system are mainly founded on three fundamental components: a perception module, a world model, and an action-planning component. Collectively, these three fundamental components enable the four pillars of common sense that include dealing with unforeseen scenarios through horizontal generalizability, capturing intuitive physics, performing analogical reasoning, and filling in the blanks. Toward developing these components, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyperdimensional (HD) computing. Specifically, we propose a concrete definition of a world model, viewing it as an HD causal vector space that aligns with the intuitive physics of the real world—a cornerstone of common sense. In addition,we discuss how this proposed world model can enable analogical reasoning and manipulation of the abstract representations. Then, we show how the world model can drive an action-planning feature of the AGI-native network. In particular, we propose an intent-driven and objective-driven planning method that can maneuver the AGI-native network to plan its actions. These planning methods are based on brain-inspired frameworks such as integrated information theory and hierarchical abstractions that play a crucial role in enabling human-like decision-making. Next, we explain how an AGI-native network can be further exploited to enable three use cases related to human users and autonomous agent applications: 1) analogical reasoning for the next-generation DTs; 2) synchronized and resilient experiences for cognitive avatars; and 3) brain-level metaverse experiences exemplified by holographic teleportation. Finally, we conclude with a set of recommendations to ignite the quest for AGI-native systems. Ultimately, we envision this article as a roadmap for the next generation of wireless systems beyond 6G.

Federated multi-armed bandits extend the multi-armed bandits framework to the federated learning setting where multiple clients in the same exploration space collaboratively identify the optimal arm. While previous studies demonstrated its efficiency like classical federated learning, in this paper, we present the first work that reveals the serious fairness problem in federated multi-armed bandits when clients have heterogeneous and overlapping armsets. The fairness problem happens because clients with different trial requirements should conduct different numbers of trials summing up to a global requirement, but they wish to minimize their exploration effort. To address the novel fairness concern, we formally formulate the fairness-aware trial assignment as a coalitional game. Based on the theoretically derived trial requirements, we devise a Shapley value-based trial assignment mechanism to guarantee fairness. Regardless of the #P-hard complexity when deriving the general Shapley value, we achieve an accurate computation of trial assignment with polynomial complexity by exploiting its unique characteristic. We further carefully control the numbers of trials in each iteration to resolve the communication bottleneck and minimize the wasted trials. Experiment results show that, compared to the naïve federated scheme, our design outperforms with both high fairness metrics and high efficiency in total trials and communication.

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to ‘morph’ its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups.

As an important part of underwater optical technology, underwater imaging plays a crucial role in accurately capturing underwater targets and environmental features. Facing the challenges of complex ocean environments and severe photon attenuation, quantum imaging breaks through the limitations of traditional optical imaging technology by utilizing the characteristics of two-photon entanglement and time-space correlation, thus offering a new perspective on ocean turbulence. To this end, we propose a new underwater entangled-photon quantum imaging system. Specifically, we first construct an entangled photon quantum imaging physical model through ocean long-exposure turbulence and then exploit an entangled light coincidence imaging reconstruction method to image the target object. Furthermore, in response to the problem that ocean environment has a great impact on photons, we develop a photon capture probability method based on entangled photon pairs to reduce the impact of the ocean turbulence noise on imaging and improve the underwater target imaging resolution. We experimentally demonstrate the effectiveness of our method by showing that even in harsh ocean environments, quantum imaging performs superior resolution capabilities over traditional light source imaging techniques.

Mega-satellite constellations have the potential to leverage inter-satellite links to deliver low-latency end-to-end communication services globally, thereby extending connectivity to underserved regions. However, harsh space environments make satellites vulnerable to failures, leading to node removals that disrupt inter-satellite networking. With the high risk of satellite node failures, understanding their impact on end-to-end services is essential. This study investigates the importance of individual nodes on inter-satellite networking and the resilience of mega satellite constellations against node failures. We represent the mega-satellite constellation as discrete temporal graphs and model node failure events accordingly. To quantify node importance for targeted services over time, we propose a service-aware temporal betweenness metric. Leveraging this metric, we develop an analytical framework to identify critical nodes and assess the impact of node failures. The framework takes node failure events as input and efficiently evaluates their impacts across current and subsequent time windows. Simulations on the Starlink constellation setting reveal that satellite networks inherently exhibit resilience to node failures, as their dynamic topology partially restore connectivity and mitigate the long-term impact. Furthermore, we find that the integration of rerouting mechanisms is crucial for unleashing the full resilience potential to ensure rapid recovery of inter-satellite networking.

Cell-free network has emerged as a promising candidate to enhance both sum rate and coverage in wireless communications. Unlike traditional cellular networks, cell-free network deploys multiple access points (APs) to jointly serve all users within a predefined geographic area, offering significant spatial diversity and interference reduction. However, the optimal deployment of APs remains a challenging problem due to its dependence on user distribution, pilot contamination, and channel estimation errors, etc. Since existing methods usually rely on simplistic models, such as Poisson point processes (PPP), they fail to capture real-world user distribution and dynamic environmental conditions. In this paper, we address this challenge by modeling the AP deployment problem as a Markov decision process (MDP). We construct multiple heterogeneous graphs to represent the interactions between APs and users and propose a graph neural network (GNN) with multiple graph inputs and an inter-graph pooling layer, referred to as GNNMG, to extract deployment state features. Subsequently, GNNMG is coupled with proximal policy optimization (PPO) to learn the optimal deployment policy. Our proposed GNNMG with PPO (GNNMG-PPO) is compared against its traditional optimization counterparts, and its superior performance is validated in terms of sum rate. Additionally, GNNMG-PPO exhibits strong generalization ability to different deployment scenarios and varying numbers of APs and users.

Due to the high flexibility and versatility, uncrewed aerial vehicles (UAVs) are leveraged in various fields including surveillance and disaster rescue. However, in UAV networks, routing is vulnerable to malicious damage due to distributed topologies and high dynamics. Hence, ensuring the routing security of UAV networks is challenging. In this paper, we characterize the routing process in a time-varying UAV network with malicious nodes. Specifically, we formulate the routing problem to minimize the total delay, which is an integer linear programming and intractable to solve. Then, to tackle the network security issue, a blockchain-based trust management mechanism (BTMM) is designed to dynamically evaluate trust values and identify low-trust UAVs. To improve traditional practical Byzantine fault tolerance algorithms in the blockchain, we propose a consensus UAV update mechanism. Besides, considering the local observability, the routing problem is reformulated into a decentralized partially observable Markov decision process. Further, a multi-agent double deep Q-network based routing algorithm is designed to minimize the total delay. Finally, simulations are conducted with attacked UAVs and numerical results show that the delay of the proposed mechanism decreases by 13.39%, 12.74%, and 16.6% than multi-agent proximal policy optimal algorithms, multi-agent deep Q-network algorithms, and methods without BTMM, respectively.

Over-the-air computation (AirComp) is a promising technique that addresses big data collection and fast wireless data aggregation. However, in a network where wireless communication and AirComp coexist, mutual interference becomes a critical challenge. In this paper, we propose to employ an unmanned aerial vehicle (UAV) to enable integrated communication and AirComp, where we capitalize on UAV mobility with alleviated interference for performance enhancement. Particularly, we aim to maximize the sum of user transmission rate with the guaranteed AirComp accuracy requirement, where we jointly optimize the transmission strategy, signal normalizing factor, scheduling strategy, and UAV trajectory. We decouple the formulated problem into two layers where the outer layer is for UAV trajectory and scheduling, and the inner layer is for transmission and computation. Then, we solve the inner layer problem through alternating optimization, and the outer layer is solved through soft actor–critic-based deep reinforcement learning. Simulation results show the convergence of the proposed learning process and also demonstrate the performance superiority of our proposal as compared with the baselines in various situations.

In the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.

In this paper, we investigate super-resolution angle estimation (SRAE) for reconfigurable intelligent surface (RIS)-aided mmWave communications. For the RIS-aided narrowband system, based on beam sweeping using a wide-beam codebook, we propose a two-step SRAE (TS-SRAE) scheme. In the first step, the selected optimal wide beam is refined to a narrow beam. In the second step, we develop an angle quantization error correction method. Then, for the RIS-aided wideband system, we propose an adaptive codebook design scheme, where the angle domain is divided into two regions, including the central region and the edge region, regarding the beam squint effect. Based on the beam sweeping using the adaptive codebook, we propose a two-region SRAE (TR-SRAE) scheme. In the central region, we extend the TS-SRAE scheme for angle estimation. In the edge region, we formulate the angle estimation as a maximum-a-posteriori problem, which is then solved by our developed Bayesian inference method. Simulation results demonstrate that both TS-SRAE and TR-SRAE schemes can effectively reduce the training overhead and improve the achievable rate.

In unlicensed spectrum, wireless communication systems are vulnerable to electromagnetic attacks and interference from non-cooperating parties, thus amplifying the significance of wireless communication security. The identification of wireless interference serves as a pivotal technology for spectrum sensing and is crucial to facilitate anti-interference communications, where wireless interference identification adopting deep learning technology has been extensively explored and has exhibited exceptional performance benefits. In this paper, we propose a lightweight transformer network (LTN) for interference identification, which can solve the computational complexity challenge from the conventional transformer networks while preserving their global feature extraction proficiency. LTN comprises three lightweight modules, namely low-complexity linear embedding (LCLE), integral and refined feature extraction (IRFE) and attention matrix reuse (AMR). Firstly, the LCLE module is obtained through the utilization of reparameterization techniques. The incorporation of reparameterization enables the decoupling of the network architecture during the training and testing phases, thereby enhancing the performance and reducing the complexity concurrently. Secondly, we propose the IRFE module, which leverages the discrete wavelet transform to partition the input into integral and refined components. For feature extraction, multi-head self-attention (MSA) is utilized for the refined part while window-based MSA is employed for the integral part, ensuring an optimized allocation of computational resources. Finally, we present a novel AMR mechanism, which takes advantage of the similarity of attention matrices in adjacent MSA layers. AMR can effectively circumvent the computational complexity by saving subsequent attention matrix computations. Simulation results validate that our proposed methodology has higher recognition accuracy.

A space-air-ground integrated network consisting of a satellite, high altitude platforms (HAPs), unmanned aerial vehicles (UAVs), and terrestrial Internet of Things (IoT) devices is constructed to collect wide-area information. The IoT devices sense the environmental information, the UAVs fly to collect data, and the HAPs deliver the computation results to the satellite. In order to improve the information freshness, the age of information (AoI) of the system is minimized by the UAV trajectory design and network configuration under the cost and practical constraints. The optimization is decomposed into two stages, which are jointly conducted by the HAPs and UAVs. In the first stage, each UAV and IoT device cluster are paired, and the UAV obtains the minimum AoI along with the optimal destined position by deep reinforcement learning (DRL). Afterwards, the HAP performs the matching between the UAVs and the IoT device clusters by the Gale-Shapley algorithm. In the second stage, the HAPs complete the configuration of the coverage area and height of the HAPs and UAVs by the soft actor-critic DRL algorithm. The extensive simulation verifies the AoI deduction of the proposed scheme and depicts the regularities of network configuration and UAV trajectory design for the minimum AoI achievement.

Satellite networks hold great promise for future 6G communications because of their benefits such as wide coverage and large capacity. Since the end-to-end (e2e) queuing delay is regarded as one key factor affecting the quality of service (QoS) in satellite networks, accurate delay prediction is a critically important topic. However, the delay prediction is complicated due to the irregular and time-varying features of inter-satellite links (ISLs) and satellite-ground links (SGLs), such as discontinuity and alternation. In this paper, we propose to establish the multi-node satellite-to-ground communication procedure as a multi-hop tandemly queuing model and present a heterogeneous heterogeneous multi-hop martingale model for queuing delay analysis. Due to the unique time-varying characteristics, we propose to model the SGL and ISL services as the stationary Markov processes using the Markov chain Monte Carlo approach. To match the intermittency and burstiness of traffic, the data arrival and service processes are handled using the Markov process. We propose to use a scaling factor for reflecting the heterogeneity of data processing capability, and then present a novel approach to ensure the stability condition requirement of the multi-hop system. Using the multi-hop heterogeneous martingale approach, the tight upper bounds of the delay and backlog in heterogeneous links are derived, and then precise delay prediction can be obtained. Finally, numerous simulations are conducted to demonstrate the effectiveness and accuracy of the proposed martingale method in analyzing the system delay and backlog when compared to the existing stochastic network calculus method.

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature.

Unmanned Aerial Vehicles-based Multiple Targets Tracking (UAV-MTT) has been mainstream in serving mission-critical scenarios for public safety, such as hit-and-run tracking and border patrol. Nonetheless, it is challenging to implement high-efficiency UAV topology control due to the variable moving speeds of targets and the limited sensing and communication resources of UAVs. To address the problem, we propose a terminal-edge cooperative Digital Twin (DT) framework for real-time and accurate MTT. Based on the DT technology, we achieve joint optimization of local and global UAV topologies to track targets with diverse speeds. Explicitly, we construct time-spatial DT models based on temporal and spatial information of targets and UAVs. The DT models can instruct UAVs to dynamically adjust position relations among one-hop neighbors for local topology optimization using our proposed Time Spatial Graph Learning based DT (TSGL-DT) algorithm. UAVs can use the optimization results to invite feasible neighbors to track low-speed moving targets. Our DT models can also allocate feasible UAVs to connect suitable local topologies for global topology optimization. It can achieve cooperative MTT to track high-speed moving targets. The experiment results demonstrate that our solution reduces the MTT latency by 41.2% while improving the successful tracking ratio delivery ratio by 15.6% on average compared to state-of-the-art benchmarks.

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration.

With billions of users worldwide, accurately predicting social media popularity is crucial for assessing user behavior, forecasting trends, and enhancing social interactions and business strategies. However, this task presents significant challenges. Firstly, the extraction of valuable insights is complicated by the presence of tri-modal data (visual, text, structured) and pervasive noise. Secondly, the applicability of knowledge acquired during the pre-training phase is often limited due to discrepancies with downstream prediction tasks during the fine-tuning phase. Existing methods for Social Media Popularity Prediction (SMPP), including traditional models and Visual-and-language Models (VLMs), struggle to overcome these challenges, thereby failing to achieve satisfactory accuracy. To tackle these challenges, we propose a novel approach named Tri-Modal Transformers with Mixture-of-Modality-Experts (TTME) for SMPP. TTME integrates Artificial Intelligence Generated Content to mitigate data noise and incorporate a mix of Modality Experts in pre-training phases to effectively utilize tri-modal data. Moreover, to address training disparity, we explore strategies for downstream task adaptation including the integration of diverse pre-training experts and the implementation of DistillSoftmax. Through empirical evaluation, we demonstrate that the TTME significantly improves the accuracy of social media popularity predictions, effectively utilizes tri-modal data with noise, and enhances transferring knowledge from pre-training to downstream tasks.

Backscatter communication (BC) enables resource-constrained backscatter devices (BDs) to communicate by reflecting signals from external radio frequency sources (RFSs), thereby avoiding active RF components, making it a cutting-edge technology for the ubiquitous Internet of Things (IoT). However, the open nature of BC makes it vulnerable to passive and active attacks, and existing methods fail to offer robust mutual authentication suitable for mobile BC systems while keeping a low computational overhead. To address this issue, we propose AuthScatter, an accurate, robust, and scalable physical-layer mutual authentication scheme between the RFS and multiple BDs by leveraging channel fading and random numbers as a one-time pad to protect the identity key exchange procedure during the authentication. Specifically, AuthScatter constructs shared identity keys as physical-layer fingerprints for efficient identification and employs a challenge-response authentication mechanism to enable secure key exchange between the RFS and the BD. In the authentication, the one-time pad effectively prevents eavesdropping, spoofing, replay, and counterfeiting attacks, while legitimate devices leverage channel reciprocity and random number knowledge to authenticate efficiently without channel estimation or complex processing. It is tailored for high-mobility scenarios by completing the exchange within the channel coherence time while incorporating a key-update mechanism to ensure sustained security in the long term. Additionally, it includes a re-authentication mechanism to enhance resistance against wireless attacks and a batch authentication framework leveraging time-division duplexing (TDD) to enable scalability in large-scale BC deployments. Comprehensive security analysis demonstrates the resistance of AuthScatter to various threats, including eavesdropping, identity spoofing, replay, and counterfeiting attacks. Extensive simulations further validate its high authentication accuracy across diverse channel conditions, robustness against various attack vectors, and scalability with a large number of BDs, highlighting its superiority over state-of-the-art schemes.

Federated Edge Learning (FEL) emerges as a pioneering distributed machine learning paradigm for the 6 G Hyper-Connectivity, harnessing data from the IoT devices while upholding data privacy. However, current FEL algorithms struggle with non-independent and non-identically distributed (non-IID) data, leading to elevated communication costs and compromised model accuracy. To address these statistical imbalances, we introduce a clustered data sharing framework, mitigating data heterogeneity by selectively sharing partial data from cluster heads to trusted associates through sidelink-aided multicasting. The collective communication pattern is integral to FEL training, where both cluster formation and the efficiency of communication and computation impact training latency and accuracy simultaneously. To tackle the strictly coupled data sharing and resource optimization, we decompose the optimization problem into the clients clustering and effective data sharing subproblems. Specifically, a distribution-based adaptive clustering algorithm (DACA) is devised basing on three deductive cluster forming conditions, which ensures the maximum sharing yield. Meanwhile, we design a stochastic optimization based joint computed frequency and shared data volume optimization (JFVO) algorithm, determining the optimal resource allocation with an uncertain objective function. The experiments show that the proposed framework facilitates FEL on non-IID datasets with faster convergence rate and higher model accuracy in a resource-limited environment.

Low Earth Orbit (LEO) satellite networks are emerging as crucial components of space-air-ground integrated networks (SAGINs), extending beyond terrestrial capabilities to provide global data transmission services for the Internet of Things (IoT) and mobile devices. The proliferation of connected devices has led to increased data volumes and highly variable, bursty traffic patterns, thus posing significant challenges for network stability and necessitating effective routing control mechanisms. Traditional Lyapunov optimization methods have been fundamental in network optimization, offering stability guarantees under the assumption that traffic flows remain strictly within the network's capacity region. However, this assumption is often violated in LEO satellite networks due to their dynamic and bursty nature, thereby rendering conventional approaches inadequate for ensuring stability. To address this challenge, we propose a robust Lyapunov optimization framework tailored for LEO satellite networks. Our method relaxes the strict requirements of traditional Lyapunov optimization by allowing the network to tolerate finite violations of the capacity region while still ensuring overall system stability. This approach demonstrates that, for a stabilizable network system, it is not necessary for traffic to remain within the capacity region at every time slot. We validate the effectiveness of the proposed robust Lyapunov optimization through extensive simulations under various traffic conditions and LEO satellite network configurations. The results confirm that LEO satellite networks can maintain stability despite finite violations of the capacity region, ensuring reliable performance amid dynamic and bursty traffic demands.

With an extensive increment of computation demands, the aerial multi-access edge computing (MEC), mainly based on unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), plays significant roles in future network scenarios. In detail, UAVs can be flexibly deployed, while HAPs are characterized with large capacity and stability. Hence, in this paper, we provide a hierarchical model composed of an HAP and multi-UAVs, to provide aerial MEC services. Moreover, considering the errors of channel state information from unpredictable environmental conditions, we formulate the problem to minimize the total energy cost with the chance constraint, which is a mixed-integer nonlinear problem with uncertain parameters and intractable to solve. To tackle this issue, we optimize the UAV deployment via the weighted K-means algorithm. Then, the chance constraint is reformulated via the distributionally robust optimization (DRO). Furthermore, based on the conditional value-at-risk mechanism, we transform the DRO problem into a mixed-integer second order cone programming, which is further decomposed into two subproblems via the primal decomposition. Moreover, to alleviate the complexity of the binary subproblem, we design a binary whale optimization algorithm. Finally, we conduct extensive simulations to verify the effectiveness and robustness of the proposed schemes by comparing with baseline mechanisms.

In the paper, we propose a joint optimization algorithm based on the block coordinate descent (JOABCD) algorithm for reflective intelligent surface (RIS) assisted MEC-MIMO systems. First, we define the delay minimization function for both single user with multi-antenna and multiple users with single-antenna scenarios. Since the optimization function is an NP-hard problem, we decompose it into two subproblems: computing setting and communication setting using the block coordinate descent (BCD) iterative algorithm. The subproblem of resource allocation is solved using a bisection method, while the subproblem of transmit power and phase shift matrix is solved alternately. The optimal simulation results show that the JOABCD algorithm can realize a lower time latency and a higher sum achievable rate compared with the existing methods.

Multi-access edge computing (MEC) is emerging as a promising paradigm to provide flexible computing services close to user devices (UDs). However, meeting the computation-hungry and delay-sensitive demands of UDs faces several challenges, including the resource constraints of MEC servers, inherent dynamic and complex features in the MEC system, and difficulty in dealing with the time-coupled and decision-coupled optimization. In this work, we first present an edge-cloud collaborative MEC architecture, where the MEC servers and cloud collaboratively provide offloading services for UDs. Moreover, we formulate an energy-efficient and delay-aware optimization problem (EEDAOP) to minimize the energy consumption of UDs under the constraints of task deadlines and long-term queuing delays. Since the problem is proved to be non-convex mixed integer nonlinear programming (MINLP), we propose an online joint communication resource allocation and task offloading approach (OJCTA). Specifically, we transform EEDAOP into a real-time optimization problem by employing the Lyapunov optimization framework. Then, to solve the real-time optimization problem, we propose a communication resource allocation and task offloading optimization method by employing the Tammer decomposition mechanism, convex optimization method, bilateral matching mechanism, and dependent rounding method. Simulation results demonstrate that the proposed OJCTA can achieve superior system performance compared to the benchmark approaches.

While unmanned aerial vehicles (UAVs) with flexible mobility are envisioned to enhance physical layer security in wireless communications, the efficient security design that adapts to such high network dynamics is rather challenging. The conventional approaches extended from optimization perspectives are usually quite involved, especially when jointly considering factors in different scales such as deployment and transmission in UAV-related scenarios. In this paper, we address the UAV-enabled multi-user secure communications by proposing a deep graph reinforcement learning framework. Specifically, we reinterpret the security beamforming as a graph neural network (GNN) learning task, where mutual interference among users is managed through the message-passing mechanism. Then, the UAV deployment is obtained through soft actor-critic reinforcement learning, where the GNN-based security beamforming is exploited to guide the deployment strategy update. Simulation results demonstrate that the proposed approach achieves near-optimal security performance and significantly enhances the efficiency of strategy determination. Moreover, the deep graph reinforcement learning framework offers a scalable solution, adaptable to various network scenarios and configurations, establishing a robust basis for information security in UAV-enabled communications.

Wireless sensing systems have been employed in the field of healthcare, environment monitoring, and smart agriculture, etc. Since the freshness and accuracy indicators of the sensing data are critical to wireless sensing systems, it is of great significance to ensure their performances simultaneously, i.e., the Age of Information (AoI) and information entropy of the sensing data should be jointly optimized. In this regard, we first establish the wireless sensing system models, including AoI and information entropy expressions. Next, from the information entropy viewpoint, we theoretically analyze an impact of the AoI on data accuracy. Then, we formulate the joint optimization problem of AoI, information entropy, and sensing energy consumption. Furthermore, we propose two numerical algorithms to solve the formulated problem in the known or unknown transmission environment, respectively. Finally, we evaluate the correctness and effectiveness of our proposals under various parameter settings, where the proposed scheme can obtain a better sum-weighted performance on AoI, information entropy, and sensing energy consumption than baselines in the literature.

The surge in smartphone and wearable device usage has propelled the advancement of the Internet of Things (IoT) applications. Among these, e-healthcare stands out as a fundamental service, enabling the remote access and storage of patient-related data on a centralized medical server (MS), and facilitating connections between authorized individuals such as doctors, patients, and nurses over the public Internet. However, the inherent vulnerability of the public Internet to diverse security threats underscores the critical need for a robust and secure user authentication protocol to safeguard these essential services. This research presents a novel, resource-efficient user authentication protocol specifically designed for healthcare systems. Our proposed protocol leverages the lightweight authenticated encryption with associated data (AEAD) primitive Ascon combined with hash functions and XoR, specifically tailored for encrypted communication in resource-constrained IoT devices, emphasizing resource efficiency. Additionally, the proposed protocol establishes secure session keys between users and MS, facilitating future encrypted communications and preventing unauthorized attackers from illegally obtaining users' private data. Furthermore, comprehensive security validation, including informal security analyses, demonstrates the protocol's resilience against a spectrum of security threats. Extensive analysis reveals that our proposed protocol significantly reduces computational and communication resource requirements during the authentication phase in comparison to similar authentication protocols, underscoring its efficiency and suitability for deployment in healthcare systems.

Advanced AI-Generated Content (AIGC) technologies have injected new impetus into teleoperation, enhancing its security and efficiency. Edge AIGC networks have been introduced to meet the stringent low-latency requirements of teleoperation. However, the inherent uncertainty of AIGC service quality and the need to incentivize AIGC service providers (ASPs) make the design of a robust incentive mechanism essential. This design is particularly challenging due to uncertainty and information asymmetry, as teleoperators have limited knowledge of the remaining resource capacities of ASPs. To this end, we propose a distributionally robust optimization (DRO)-based contract theory to design robust reward schemes for AIGC task offloading. Notably, our work extends the contract theory by integrating DRO, addressing the fundamental challenge of contract design under uncertainty. In this paper, we employ contract theory to model information asymmetry while utilizing DRO to capture the uncertainty in AIGC service quality. Given the inherent complexity of the original DRO-based contract theory problem, we reformulate it into an equivalent, tractable bi-level optimization problem. To efficiently solve this problem, we develop a Block Coordinate Descent (BCD)-based algorithm to derive robust reward schemes. Simulation results on our unitybased teleoperation platform demonstrate that the proposed method improves teleoperator utility by 2.7% to 10.74% under varying degrees of AIGC service quality shifts and increases ASP utility by 60.02% compared to the SOTA method, i.e., Deep Reinforcement Learning (DRL)-based contract theory. The code and data are publicly available at https://github.com/Zijun0819/DROContract-Theory

Integrating low-light image enhancement techniques, in which diffusion-based AI-generated content (AIGC) models are promising, is necessary to enhance nighttime teleoperation. Remarkably, the AIGC model is computation-intensive, thus necessitating the allocation of AIGC tasks to edge servers with ample computational resources. Given the distinct cost of the AIGC model trained with varying-sized datasets and AIGC tasks possessing disparate demand, it is imperative to formulate a differential pricing strategy to optimize the utility of teleoperators and edge servers concurrently. Nonetheless, the pricing strategy formulation is under information asymmetry, i.e., the demand (e.g., the difficulty level of AIGC tasks and their distribution) of AIGC tasks is hidden information to edge servers. Additionally, manually assessing the difficulty level of AIGC tasks is tedious and unnecessary for teleoperators. To this end, we devise a framework of AIGC task allocation assisted by the Vision Language Model (VLM)-empowered contract theory, which includes two components: VLM-empowered difficulty assessment and contract theory-assisted AIGC task allocation. The first component enables automatic and accurate AIGC task difficulty assessment. The second component is capable of formulating the pricing strategy for edge servers under information asymmetry, thereby optimizing the utility of both edge servers and teleoperators. The simulation results demonstrated that our proposed framework can improve the average utility of teleoperators and edge servers by$10.88 \sim 12.43\%$and$1.4\! \sim \!2.17\%$, respectively.

Network function virtualization (NFV)-enabled space information network (SIN) has emerged as a promising method to facilitate global coverage and seamless service. This paper proposes a novel NFV-enabled SIN to provide end-to-end communication and computation services for ground users. Based on the multi-functional time expanded graph (MF-TEG), we jointly optimize the user association, virtual network function (VNF) deployment, and flow routing strategy (U-VNF-R) to maximize the total processed data received by users. The original problem is a mixed-integer linear program (MILP) that is intractable for classical computers. Inspired by quantum computing techniques, we propose a hybrid quantum-classical Benders’ decomposition (HQCBD) algorithm. Specifically, we convert the master problem of the Benders’ decomposition into the quadratic unconstrained binary optimization (QUBO) model and solve it with quantum computers. To further accelerate the optimization, we also design a multi-cut strategy based on the quantum advantages in parallel computing. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm and U-VNF-R scheme.

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders’ decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

Due to its advantage of protecting data privacy and reducing communication overhead, Federated Learning (FL) is becoming a promising machine learning paradigm. However, resource limitations and unstable communication connections on the participating client end can lead to unintentional failures that degrade FL performance. Moreover, as FL systems scale and interconnect increasingly, they face growing exposure to intentional network risks. Furthermore, the assumption of continued trust in historically benign clients introduces vulnerabilities to potential internal betrayal within FL systems. In this paper, we enhance the robustness of FL by incorporating the zero-trust principle, which eliminates implicit trust in clients and mitigates unintentional failures, intentional attacks, and strategic betrayal risks. The framework incorporates dynamic client selection and aggregation weight allocation through trustworthiness evaluation and sustained skepticism toward each potential betrayal behavior. Specifically, a Dirichlet-based trust evaluation technique is presented to update clients' trustworthiness with evolving observations. Then, to reduce potential betrayal loss, we formulate a min-max optimization problem that minimizes the worst-case betrayal loss. Next, we transform the formulation into a convex programming problem for solution. Extensive simulations are conducted to demonstrate the efficacy of the zero-trust based FL in the accurate trust assessment and the system's betrayal-aware robustness enhancement.

With the growth of the magnitude of multiagent networks, distributed optimization holds considerable significance within complex systems. Convergence, a pivotal goal in this domain, is contingent upon the analysis of infinite products of stochastic matrices (IPSMs). In this work, the convergence properties of inhomogeneous IPSMs are investigated. The convergence rate of inhomogeneous IPSMs toward an absolute probability sequence $\pi $ is derived. We also show that the convergence rate is nearly exponential, which coincides with existing results on ergodic chains. The methodology employed relies on delineating the interrelations among Sarymsakov matrices, scrambling matrices, and positive-column matrices. Based on the theoretical results on inhomogeneous IPSMs, we propose a decentralized projected subgradient method for time-varying multiagent systems with graph-related stretches in (sub)gradient descent directions. The convergence of the proposed method is established for convex objective functions and extended to nonconvex objectives that satisfy Polyak-Lojasiewicz (PL) conditions. To corroborate the theoretical findings, we conduct numerical simulations, aligning the outcomes with the established theoretical framework.

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature.

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on ground infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, uncrewed aerial vehicle (UAV)-assisted MEC is emerging as a promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory control optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy and resource constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory control approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results show that the proposed OJTRTA outperforms various benchmark approaches and achieves at least a 10% improvement in the QoE of UDs compared to deep reinforcement learning (DRL)-based algorithms, thereby validating the superiority of the proposed approach.

Federated learning (FL) has emerged recently as an appealing and promising technique to deal with distributed learning issues in the sixth generation (6G) communication systems. Recent studies focus on developing FL schemes for terrestrial radio networks, where the variation in transmission data rates caused by transmission distance changes is negligible over one communication round. However, this variation has considerable influences for underwater acoustic channels. In this paper, we propose an underwater dynamic federated learning (UDFL) scheme by jointly considering characteristics of underwater acoustic channels and moving behavior of autonomous underwater vehicles. Moreover, an energy consumption minimization problem is formulated based on the scheme. To meet the challenges of transmission latency and FL performances, we consider them separately and provide closed-form solutions to the two individual problems. Specifically, we theoretically characterize the connections between transmission power and FL performances, and derive the optimal transmission policy given transmission latency constraints. Based on the two solutions, a dynamic programming based online power control algorithm is proposed to determine the transmission power across all time slots. Numerical simulations are conducted to demonstrate that the designed scheme is effective and the proposed online algorithm can achieve latency constrained energy-efficient UDFL.

Federated learning (FL) is a promising artificial intelligence framework that enables clients to collectively train models with data privacy. However, in real-world scenarios, to construct practical FL frameworks, several challenges have to be addressed, including statistical heterogeneity, constrained resources, and fairness. Therefore, we first investigate anaggregation gapcaused by statistical heterogeneity during local model initialization, which not only causes additional computational overhead for clients but also leads to the degradation of fairness. To bridge this gap, we proposepFedCal, a novelpersonalizedfederated learning with lightweight adaptivecalibration strategy that performs calibration compensation through the prior knowledge of clients. Specifically, we introduce compensation for each client at the model initialization, with the compensation derived from the global gradient and the latest gradient bias. To enhance the calibration effect, we introduce a smoothing-based calibration strategy, and we design an adaptive calibration strategy. A representative example demonstrates that the proposed calibration and smoothing strategies improve fairness for clients. The theoretical analysis indicates that with an appropriate learning rate, pFedCal converges to a first-order stationary point for non-convex loss functions. Comprehensive experimental results show that pFedCal achieves faster convergence, higher accuracy, and improved fairness than the state-of-the-art methods.

Timely updating of Internet of Things (IoT) data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers i) the freshness and accuracy of the local model, and ii) the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

This paper proposes a fast and robust graph-based wavenumber-domain approach for channel estimation in holo-graphic MIMO (HMIMO) systems. Unlike conventional angulardomain methods—prone tomutual coupling, power leakage, andsampling redundancy—our framework resolves HMIMO’s high-dimensional challenges by introducing a wavenumber-domain basis via orthogonal Fourier harmonics (FHs), eliminating dependencies on antenna density. By reformulating channel estimation as its sparse recovery counterpart, we model clustered sparsity using an elliptic Markov random field (EMRF), upon which a graph-cut swap expansion (GCSE) algorithm is developed, leveraging graph-theoretic optimizations for fast convergence and low complexity. Simulations demonstrate that our method achieves robust performance against mutual coupling, varying SNRs, and antenna density with drastically less computing time.

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments.

The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmission efficiency and address these issues. In this paper, we explore the potential of SC as a bandwidth-efficient, latency-minimizing strategy specifically suited to 6G satellite communications. The existing SC methods have demonstrated efficacy in direct satellite-terrestrial transmissions; however, they still encounter certain limitations. Specifically, some ground users (GUs) experience poor signal-to-noise ratios (SNR), making direct satellite communication challenging. To address these issues, we propose a novel framework that optimizes gateway hop-relay selection for GUs with low SNR and integrates gateway-based denoising mechanisms to ensure high-quality-of-service (QoS) in satellite-based SC networks. This approach directly mitigates distortion, leading to significant improvements in satellite service performance by delivering customized services tailored to the unique signal conditions of each GU. Our findings represent a critical advancement in reliable and efficient data transmission from the Earth observation satellites, thereby enabling fast and effective responses to urgent events. Simulation results demonstrate that our proposed strategy significantly enhances overall network performance, outperforming conventional methods by offering tailored communication services based on specific GU conditions.

Over-the-air computation (AirComp) has emerged as a promising technology that enables simultaneous transmission and computation through wireless channels. In this paper, we investigate the networked AirComp in multiple clusters allowing diversified data computation, which is yet challenged by the transceiver coordination and interference management therein. Particularly, we aim to maximize the multi-cluster weighted-sum AirComp rate, where the transmission scalar as well as receive beamforming are jointly investigated while addressing the interference issue. From an optimization perspective, we decompose the formulated problem and adopt the alternating optimization technique with an iterative process to approximate the solution. Then, we reinterpret the iterations through the principle of algorithm unfolding, where the channel condition and mutual interference in the AirComp network constitute an underlying graph. Accordingly, the proposed unfolding architecture learns the weights parameterized by graph neural networks, which is trained through stochastic gradient descent approach. Simulation results show that our proposals outperform the conventional schemes, and the proposed unfolded graph learning substantially alleviates the interference and achieves superior computation performance, with strong and efficient adaptation to the dynamic and scalable networks.

Satellite-terrestrial communications based on mega low-Earth orbit (LEO) constellations enable extensive coverage and high data rates. However, the communication performance is significantly impacted by the non-uniform traffic distribution and the substantial interference caused by dense satellites. Therefore, in this paper, a multi-satellite cooperation architecture for satellite-terrestrial communications is proposed in LEO satellite constellations. Specifically, beam hopping (BH) and resource allocation enable a flexible solution for the non-uniform geographical distribution of communications, and are optimized to improve communication performance and avoid both intra- and inter-satellite interference in satellite-terrestrial communications. Moreover, load balancing via inter-satellite link (ISL) is implemented to further enhance the communication performance. Consequently, the overall problem for maximizing the network throughput and ensuring the latency metric is formulated, and then decomposed into three sub-problems: deep reinforcement learning (DRL) based BH scheduling, resource allocation with multi-satellite cooperation, and load balancing via ISLs. Specifically, DRL is utilized to determine the real-time BH pattern, and the resource allocation among beams is implemented by the majorization-minimization algorithm. Furthermore, for varying input traffic loads, different objectives for load balancing are established and solved by quadratic transformation and hybrid block successive approximation algorithm. Simulation results demonstrate that the proposed method outperforms other existing methods. Meanwhile, it obtains an 18.45% improvement in throughput compared with the benchmark without optimization, while the latency metric for satellite-terrestrial communications is also reduced by the proposed method.

Integrated sensing and communication (ISAC) is regarded as one key enabler in the future sixth-generation (6G) mobile communication network. While considerable attention has been paid to radio-frequency (RF)-ISAC, optical wireless (OW)-ISAC is also rapidly developing as a powerful complement to its RF counterpart. In this paper, an enhanced asymmetrically clipped direct-current-biased optical orthogonal frequency division multiplexing (EADO-OFDM) scheme is proposed for flexible waveform design and resource allocation in OW-ISAC. A generalized OW-ISAC framework is first established to describe the working principles of both communication and sensing (C&S). Then, the signal model is introduced for EADO-OFDM, where the Price theorem is adopted to model the colored clipping noise and elicit a generalized frequency-selective channel. In addition, a joint optimization problem of resource allocation for EADO-OFDM is formulated to adaptively balance C&S performance metrics, and the solution to the problem is obtained by the block coordinate descent algorithm. Finally, extensive numerical simulations demonstrate the flexibility of the proposed EADO-OFDM scheme, while the C&S trade-off is also revealed during the resource allocation.

Optical wireless integrated sensing and communication (OW-ISAC) is emerging as a crucial technology to complement and augment its radio-frequency counterpart. In this paper, we propose an optical phased array (OPA)-based OW-ISAC framework to enable concurrent multi-user communication and environment imaging. The optical beamforming and atmospheric propagation are first elaborated to introduce the principles of OPA-based OW-ISAC. In addition, the investigation into the multi-beam property, direct detection scheme, and sensing task of imaging for OW-ISAC yields dedicated signal-to-interference-plus-noise ratio and contrast metrics for communication and sensing sub-systems, respectively. Moreover, the precoding matrices and photodiode orientations are jointly optimized to achieve optimal beamforming. Subsequently, numerical simulations illustrate the relationships between communication and sensing performance metrics. Furthermore, the proposed OW-ISAC scheme is substantiated in a realistic scenario with the optimized beamforming. The demonstrated high-precision sensing and reliable communication capabilities of OPA-based OW-ISAC can serve plentiful future applications in the era of connection and intelligence.

Existing deep learning-based semantic communication (DeepSC) systems are typically trained for specific single-channel condition, which restricts the overall adaptability and resilience to interference. To address this limitation, we propose an innovative semantic adaptive feature extraction (SAFE) network that dynamically generates and fuses multiple sub-semantics, each characterized by unique features that can be tailored to different channel conditions. This paper also introduces three advanced learning algorithms to refine and enhance the generated sub-semantics, optimizing the semantic successive refinement performance of the SAFE network. Furthermore, we integrate a novel interference-aware semantic transmission method based on non-orthogonal multiple access (NOMA) into this framework. This approach enables users to adaptively select appropriate subsets for efficient transmission and image reconstruction, tailored to the prevailing channel interference conditions. Through extensive simulation experiments, we demonstrate the framework’s capability to generate and transmit semantics under diverse channel interference scenarios adaptively, and verify the effectiveness through both objective and subjective quality evaluations.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm’s effectiveness.

Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm.

This paper investigates a novel network architecture – the 6G integrated terrestrial-non-terrestrial network (ITNTN) with multi-access edge computing (ITNT-MEC). This system aims to bridge the connectivity gap between terrestrial infrastructure and non-terrestrial networks while offering real-time data processing through edge computing. We consider a scenario where aerial user equipments (AUEs) share resources of terrestrial base stations (TBSs) with terrestrial UEs (TUEs). We formulate an optimization problem to minimize the total energy consumption of both AUEs and TUEs. This problem involves joint optimization of AUE association (i.e., TBS or low Earth orbit (LEO) satellite), AUE trajectories, and TBS bandwidth allocation. Due to the dynamic network environment and non-convex optimization characteristics, solving this problem presents a significant challenge. To address this, we propose a novel algorithm that combines block coordinate descent (BCD) with deep deterministic policy gradient (DDPG) and a convex optimization method. Simulation results demonstrate the significant reductions in total energy consumption compared to baseline approaches, achieving improvements of 23%, 36.6%, and 46.5% against DQN-TO, RA, and FT, respectively.

Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality for adopting SC is its inherent ability to reduce communication costs and make spectrum efficient for 6G non-terrestrial networks (6G-NTNs). We focus on the critical satellite imagery downlink communications latency optimization for Earth observation through SC techniques. We formulate the latency minimization problem with SC quality-of-service (SC-QoS) constraints and address this problem with a meta-heuristic discrete whale optimization algorithm (DWOA) and a one-to-one matching game. The proposed approach for captured image processing and transmission includes the integration of joint semantic and channel encoding to ensure downlink sum-rate optimization and latency minimization. Empirical results from experiments demonstrate the efficiency of the proposed framework for latency optimization while preserving high-quality data transmission when compared to baselines.

With the rapid increment of multiple users for data offloading and computation, it is challenging to guarantee the quality of service (QoS) in remote areas. To deal with the challenge, it is promising to combine aerial access networks (AANs) with multi-access edge computing (MEC) equipments to provide computation services with high QoS. However, as for uncertain data sizes of tasks, it is intractable to optimize the offloading decisions and the aerial resources. Hence, in this paper, we consider the AAN to provide MEC services for uncertain tasks. Specifically, we construct the uncertainty sets based on historical data to characterize the possible probability distribution of the uncertain tasks. Then, based on the constructed uncertainty sets, we formulate a distributionally robust optimization problem to minimize the system delay. Next, we relax the problem and reformulate it into a linear programming problem. Accordingly, we design a MEC-based distributionally robust latency optimization algorithm. Finally, simulation results reveal that the proposed algorithm achieves a superior balance between reducing system latency and minimizing energy consumption, as compared to other benchmark mechanisms in the existing literature.

This paper focuses on an intelligent reflecting surface (IRS)-assisted lossy communication system with correlated Rayleigh fading. We analyze the correlated channel model and derive the outage probability of the system. Then, we design a deep reinforce learning (DRL) method to optimize the phase shift of IRS, in order to maximize the received signal power. Moreover, this paper presents results of the simulations conducted to evaluate the performance of the DRL-based method. The simulation results indicate that the outage probability of the considered system increases significantly with more correlated channel coefficients. Moreover, the performance gap between DRL and theoretical limit increases with higher transmit power and/or larger distortion requirement.

Considering that optical intelligent reflecting surface (OIRS) can change the optical channel, the enhancement performance of OIRS on the energy efficiency (EE) of the non-orthogonal multiple access (NOMA)-based visible light communication (VLC) system is investigated. Specifically, a multi-cell VLC system is modelled with both line-of-sight (LoS) and OIRS-reflected paths, and then the problem is formulated to optimize the OIRS configuration and power allocation for the maximization of the overall EE while adhering to various constraints. Moreover, the original task is decomposed into two sub-problems, and is solved iteratively by the proposed algorithm. In addition, the convergence of the proposed algorithm and enhancement in EE achieved by OIRS are illustrated by simulation results, which offer beneficial insights on resource allocation for the NOMAbased VLC systems with OIRS.

In this paper, a multi-satellite cooperation architecture for satellite-terrestrial communications is studied with the dense constellation of low Earth orbit (LEO) satellites. Moreover, beam hopping (BH) and frequency allocation enable a flexible solution for the non-uniform geographical distribution of communications, which are optimized to improve the performance and avoid interference in satellite-terrestrial communications. The total problem for maximizing the networking throughput and ensuring the delay fairness is decomposed into two sub-problems, in which the deep reinforcement learning is utilized to determine the BH pattern, and the frequency allocation among beams is implemented by the majorization-minimization algorithm. Simulation results demonstrate that the proposed method obtains a 13.0% improvement in throughput compared with the original benchmark, while the delay fairness can be ensured by the iterative solution of the two sub-problems.

Integrated sensing and communication (ISAC) is regarded as a key enabler in the upcoming era of connectivity and intelligence, where the optical spectrum emerges as a promising candidate for ISAC. This paper presents an optical wireless (OW)-ISAC scheme based on asymmetrically clipped direct-current-biased optical orthogonal frequency division multiplexing (ADO-OFDM). The Bussgang theorem is adopted to model the clipped OFDM signal and analyze the clipping noise. In addition, the adaptive resource allocation for ADO-OFDM is formulated as a joint optimization problem, which is decomposed into two sub-problems for DC-biased optical (DCO)asymmetrically clipped optical (ACO) power distribution and subcarrier power allocation. Then, the optimal resource allocation can be achieved by iteratively solving these sub-problems. Consequently, numerical results demonstrate the effectiveness of the proposed ADO-OFDM scheme and reveal the trade-off between communication and sensing functionalities in OW-ISAC.

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems.

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients’ data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model’s robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients’ trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system’s betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model’s robustness to betrayal.

Future wireless networks targeted for improving spectral and energy efficiency, are expected to simultaneously provide sensing functionality and support low-power communications. This paper proposes a novel net-zero integrated sensing and communication (ISAC) model for backscatter systems, including an access point (AP), a net-zero device, and a user receiver. We fully utilize the backscatter mechanism for sensing and communication without additional power consumption and signal processing in the hardware device, which reduces the system complexity and makes it feasible for practical applications. To further optimize the system performance, we design a novel signal frame structure for the ISAC model that effectively mitigates communication interference at the transmitter, tag, and receiver. Additionally, we employ distributed antennas for sensing which can be placed flexibly to capture a wider range of signals from diverse angles and distances, thereby improving the accuracy of sensing. We derive theoretical expressions for the symbol error rate (SER) and tag’s location detection probability, and provide a detailed analysis of how the system parameters, such as transmit power and tag’s reflection coefficient, affect the system performance.

The 6G wireless communication networks need an intelligent networking system to meet the ever-increasing de-mands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cell-free (CF) network is suggested to allocate desired power for beamforming by activating the required number of grids from the serving HMIMOs for serving the users. An optimization problem is developed to ensure effective power allocation that maximizes the EE of the system. A Transformer-based AI framework is proposed to solve the formulated NP-hard problem that distributes desired power for serving the users by activating the required number of grids from the required number of serving HMIMOs in the CF network. Finally, simulation results represent that the proposed power allocation framework outperforms the gated recurrent unit and long short-term memory-based mechanisms, achieving a combined power savings of 12.5% and 4.06%, and a combined EE improvement of 14.68% and 8.93%, correspondingly. Therefore, our suggested AI-based framework guarantees effective power allocation for beamforming to serve the users.

Motivated by the ability of optical intelligent reflective surface (OIRS) to alter optical channels, the effectiveness of OIRS on aggregated visible light communication (VLC)-radio frequency (RF) systems is explored, and the system model is established in detail. With the aim of improving the energy efficiency (EE) of the aggregated system with OIRS, the EE maximization problem is formulated with diverse constraints, followed by the proposed block coordinate descent (BCD)-based resource allocation algorithm. Particularly, the EE maximization problem is partitioned into two subproblems, which are OIRS configuration and joint power allocation, and then are solved in the iteration process. Additionally, the substantial enhancement in EE brought by OIRS for the aggregated VLC-RF system, as well as the convergence and efficacy of the proposed BCD-based algorithm are demonstrated by comprehensive simulation results.

The rapid development of the Internet of Medical Things (IoMT) has brought about an enormous amount of healthcare data. Effectively and securely processing this sensitive data has become a significant challenge for green communication and privacy protection of the IoMT. As a decentralized learning framework, Federate learning (FL) enables model training without directly aggregating users' raw data, thus ensuring user privacy protection. Moreover, numerous studies have put forth various approaches to enhance the efficiency of FL by minimizing communication costs, yet they may not fully account for the unique characteristics of IoMT. Specifically, the efficiency and performance of model training are closely related to patient life and health. Meanwhile, existing research has indicated that reducing communication costs can result in a decline in training accuracy, which may be critical to patient health. Therefore, aimed at green communication and ensuring the model accuracy, we design a communication-efficient personalized federated learning framework, namely pFedCAS. Specifically, we introduce a control unit, which enables adaptive sparsity of local models, to reduce training costs. Furthermore, a selection unit based on communication quality is added into the global aggregation, which can select suitable clients for model updating. Simulation results validate that the proposed method can significantly reduce communication costs while ensuring the model accuracy. Additionally, The simulation results also validate the excellent robustness of our method to non-iid healthcare data.

With the advances of Virtual Reality (VR) / Augmented Reality (AR), there arises a compelling need for transmission of point clouds over lossy channels (e.g., a 5G millimeter wave (mmWave) link that tends to be easily blocked). In this paper, we revisit the traditional Multiple Description Coding (MDC) concept and propose a simple point cloud MDC scheme that takes advantage of voxelization and is built upon a typical geometric point cloud compression codec. Our simulation study demonstrates the efficacy of the proposed scheme, as well as the tradeoff between compression efficiency and point cloud quality gain offered by MDC.

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially -stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence.

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

Joint communications and sensing (JCS) provides an effective approach to enhance the spectral efficiency of wireless systems. When integrating these historically independent functions in the same waveform, both communication and sensing may suffer from performance degradation, thus resulting in a trade-off between their performances. A fundamental question is how to obtain bounds for the communication- sensing trade-off in JCS. In this paper, a geometric approach is adopted, namely evaluating the volume of a feasible waveform set given the tolerable performance degradation of sensing and then bounding the number of possible communication codewords using the sphere packing methodology. In particular, mathematical tools in high-dimensional geometry are leveraged for the volume calculation in the first of this paper. Applications for concrete sensing performance metrics will be left to the second part of the paper.

The technology of joint communications and sensing (JCS) is expected to prevail in 6G wireless networks. There exists a performance tradeoff between the functions of communications and sensing in JCS. One effective approach to analyze the tradeoff in JCS is to consider the level sets of a given performance metric of sensing as the signaling space of communication codewords. Then, performance bounds can be obtained for communications using the approach of sphere packing. The principle for generic sensing performance metric has been studied in the first part of this paper. In the second part of this paper, the concrete cases of sensing performance metrics, namely the signal-to-noise ratio (SNR) and integrated sidelobe level (ISL), are studied. The problems are turned into the volume evaluation for the intersection of a (elliptic) sphere (the quadratic approximation of the level set) and a hyperplane (the constraint on the total transmit power). They are solved by using the theory of Fourier-transform-based volume evaluation of convex sets. It is found that the optimal waveform is not unique, thus providing free lunch (although not plenty of) for communications. Another finding is that the communication data rate increases logarithmically with respect to the sensing performance metric degradation.

The rapid advancement of machine learning (ML) and the growing need for computational power have led to the exploration of quantum computing, which offers significant potential for faster complex calculations. However, Quantum Machine Learning (QML) faces challenges due to the limited number of qubits and noise of quantum circuits, particularly with Noisy Intermediate-Scale Quantum (NISQ) devices. These challenges severely limit the current capacity to train accurate and stable Quantum Machine Learning Models. In this paper, we propose a novel framework for QML that employs the knowledge distillation method to harness the power of well-trained classical machine learning (CML) models and enhance the training performance of QML models. In this framework, we utilize the well-trained CML as a teacher model to assist the training of the student QML model using the knowledge distillation method. By distilling knowledge from the robust CML model, our framework can potentially address the problem of the barren plateau which hinders effective model training. Knowledge distillation is well suited for this framework through the transfer of knowledge without parameter sharing. Through empirical tests, our framework has demonstrated not only an increase in the accuracy of QML models but also a notable improvement in training stability.

Optical intelligent reflecting surfaces (OIRS) can be deployed in visible light communication (VLC) systems to provide OIRS-reflected paths for multiple light-emitting diodes (LEDs) and users via adjustable reflective characteristics, thereby significantly mitigating the signal blockage challenges in VLC. To achieve this goal, the effectiveness of OIRS in the non-orthogonal multiple access (NOMA)-based multiple-input single-output (MISO) VLC system is explored, while the system model is established considering both the line-of-sight (LoS) and the OIRS-reflected paths. Subsequently, the problem is formulated with the objective of optimizing the OIRS configuration to maximize the achievable sum data rate with the diverse constraints. To address the highly intractable and non-convex problem, a relaxed iterative optimization algorithm based on the Taylor expansion is proposed in this paper. Moreover, numerical results are provided to show the improvement of the achievable sum data rate and the effects of the proposed relaxed iterative algorithm.

Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of- Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR), and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods.

Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and mitigation of such threats. First, a new zero trust system model of PGSC is designed and formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by realizing and defending against GenAI-driven cyber attacks. Second, in which a domain-specific generative adversarial networks (GAN)-based attack generation mechanism is developed to create a new vulnerability cyberspace for further understanding that threat. Third, tail-based risk realization metrics are developed and implemented for quantifying the extreme risk of a potential attack while leveraging a trust measurement approach for continuous validation. Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to detect the attacks that are generating synthetic identities with convincing user and distributed energy resources device profiles. Experimental results show the efficacy of the proposed zero trust framework that achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.

The scheduling of the laser inter-satellite links (LISLs) can effectively improve the network performance but is challenging in mega-constellations due to the large amount of satellites. In this paper, inter-OP LISL scheduling is realized by federated reinforcement learning, which reduces complexity by decomposing global scheduling into independent decisions for each satellite and solved by multi-agent reinforcement learning. Based on the constellations collaboration, federated learning (FL) with partial and global aggregations is utilized to reduce the overhead for the model update, where the geostationary orbit (GEO)-GEO collaboration for global aggregation can be performed infrequently since the indirect associations among the GEO satellites according to the motion of low Earth orbit (LEO) satellite. Meanwhile, simulation results indicate that the proposed method reduces the average hop count by over two hops and decreases the number of LISLs by over 25% compared to the fixed LISLs, and the model update overhead is reduced to 46.6% of that in the centralized method.

Reconfigurable intelligent surface (RIS) technology can enhance the performance of wireless systems, but an ad-versary can use such technology to deteriorate communication links. This paper explores an RIS-based attack on multi-user wireless systems that require channel reciprocity for time-division duplexing (TDD). We demonstrate that deploying an RIS with a non-diagonal phase shift matrix can compromise channel reciprocity and lead to poor TDD performance. The attack can be achieved without transmission of signal energy, without channel state information (CSI), and without synchronization with the legitimate system, and thus it is difficult to detect and counteract. We provide an extensive set of simulation studies on the impact of such an attack on the achievable sum rate of the legitimate system, and we design a heuristic algorithm for optimizing the attack in cases where some partial knowledge of the CSI is available. Our results demonstrate that this channel reciprocity attack can significantly degrade the performance of the legitimate system.

Integrated sensing and communication (ISAC) is one of the six usage scenarios of the sixth generation (6G) mobile communication system. Since an optical system can also provide communication and sensing abilities, optical ISAC has become a potential complement to radio-frequency (RF) ISAC. As orthogonal frequency division multiplexing (OFDM) has gained increasing interest in optical systems, we propose a direct-current-biased optical OFDM (DCO-OFDM) scheme for free space optical (FSO) ISAC in this paper. To derive the spectral efficiency for communication and the Fisher information for sensing, we model the clipping noise of DCO-OFDM with the Bussgang theorem to obtain the expression of the signal-to-noise ratio. In addition, based on the derived performance metrics, an optimization problem for power allocation is formulated, and an iterative algorithm is proposed to solve the problem efficiently. Meanwhile, numerical simulations demonstrate the effectiveness of the proposed algorithm and reveal the trade-off between communication and sensing functionalities of the OFDM-based FSO-ISAC system.

With the development of dual-function radar communication (DFRC) systems, waveform design has received increasing attention. At the same time, subcarrier superposition can lead to the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM). To solve the problem, in this paper, we propose an alternating direction method of multipliers (ADMM)-based low-PAPR OFDM waveform design algorithm for DFRC systems, which minimizes the signal PAPR with the constraint of the zero integrated sidelobe level (ISL). Moreover, we compare our algorithm with a recently proposed benchmark algorithm. Simulation results demonstrate that our algorithm has better performance compared to the$l$- norm cyclic algorithm.

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming method of solving mixed-integer linear programming (MILP) problems with a specific block structure. Nevertheless, BD still needs to solve an NP-hard quasi-integer programming master problem (MAP), which motivates us to harness the popular variational quantum algorithm (VQA) to assist BD. More specifically, we choose the popular quantum approximate optimization algorithm (QAOA) of the VQA family. We transfer the BD's MAP into a digital quantum circuit associated with a physically tangible problem-specific ansatz; and then solve it with the aid of a state-of-the-art digital quantum computer. Next, we evaluate the computational results and discuss the feasibility of the proposed algorithm. The hybrid approach advocated, which utilizes both classical and digital quantum computers, is capable of tackling many practical MILP problems in communication and networking, as demonstrated by a pair of case studies.

Numerous optimization scenarios such as industrial production planning, network communication routing, and logistic scheduling can be modeled as large-scale integer linear programming problems. However, due to the NP-Hardness of these problems, it is very challenging to optimally solve these problems in a short time on classical computers. Quantum computers have emerged as a new computing platform to provide new computing paradigms to tackle these problems. However, the scalability and efficiency of current quantum computers pose significant challenges in practical implementations of quantum optimization algorithms. In this paper, we propose a novel hybrid quantum-classical approach, termed Hybrid quantum-classical Dantzig-Wolfe Decomposition (HyDWD), aimed at solving these problems. In this framework, the subproblems can be solved in parallel on quantum computers. Our results demonstrate the benefits of integrating parallel quantum computing with the proposed hybrid quantum-classical framework via Dantzig-Wolfe decomposition, paving the way for advancements in optimization and decision-making processes.

Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models.

The emerging Web 3.0 paradigm aims to decentralize existing web services, enabling desirable properties such as transparency, incentives, and privacy preservation. However, current Web 3.0 applications supported by blockchain infrastructure still cannot support complex data analytics tasks in a scalable and privacy-preserving way. This paper introduces the emerging federated analytics (FA) paradigm into the realm of Web 3.0 services, enabling data to stay local while still contributing to complex web analytics tasks in a privacy-preserving way. We propose FedWeb, a tailored FA design for important frequent pattern mining tasks in Web 3.0. FedWeb remarkably reduces the number of required participating data owners to support privacy-preserving Web 3.0 data analytics based on a novel distributed differential privacy technique. The correctness of mining results is guaranteed by a theoretically rigid candidate filtering scheme based on Hoeffding’s inequality and Chebychev’s inequality. Two response budget saving solutions are proposed to further reduce participating data owners. Experiments on three representative Web 3.0 scenarios show that FedWeb can improve data utility by ∼25.3% and reduce the participating data owners by ∼98.4%.

The rapid integration of augmented reality (AR) and virtual reality (VR) technologies into contemporary automotive development has led to unprecedented opportunities and challenges. This work addresses the integration of edge computing and AR/VR applications within connected autonomous vehicles, focusing on the pivotal role of object detection. The edge-assisted object detection problem is formulated as a constrained optimization problem, aiming to minimize the adverse effects on the object detection process. To solve the problem, we introduce an innovative edge-assisted algorithm, transmitting live camera frames to an edge server for detailed processing. Only essential detection data is then relayed to AR/VR devices, marking a significant advancement over existing strategies. Notable outcomes include a reduction in latency (averaging between 37.06% and 44.76%), enhanced data throughput (ranging from 27.66% to 41.18%), improved freshness loss (between 36.36% and 69.57%), and a frame loss reduction to 7.5%, surpassing baseline methods by 6.5% to 36%. These findings underscore the potential of this methodology for optimizing AR/VR applications in vehicular environments.

Physical layer authentication (PLA) is a promising solution to address the security issue raised due to malicious jamming or spoofing. However, accurate and diversified channel state information is required to implement the PLA schemes. In this regard, reconfigurable intelligent surface (RIS) has the potential to quickly reshape the communication environment at a cheap cost, and thus has great potential to enhance the PLA. In this paper, we propose a RIS-assisted channel impulse response (CIR)-based dynamic PLA scheme. Specifically, the receiver exploits the geographic location information of the transmitters embedded in CIR to identify the message. In order to reduce the impact of the components representing environmental changes in CIR on the authentication, the method of regularly updating CIR database is adopted. In addition, with RIS enriched CIR information, we can achieve a high authentication rate by constructing a classification neural network. Experiments are conducted based on the communication system with DeepMIMO datasets, and the simulation results demonstrate that the proposed authentication scheme is effective for the identification of both first-attack and non-first-attack spoofers.

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incor-porates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

Over-the-air computation (AirComp) emerges as a promising method to integrate computation and communication in 5G and beyond network architecture. Nevertheless, the performance of AirComp, measured by mean-square error (MSE), can be severely bottlenecked by the availability of channel information. In this paper, we investigate the AirComp design in presence of channel uncertainties. Particularly, we consider the case that only the first and second moments of the channel, which can be easily obtained through actual measurement, are available, without the exact statistical information. Then, we establish the chance-constrained AirComp with a thresholded MSE under a given outage probability. Correspondingly, we address the distributionally robust AirComp design to guarantee the intended threshold regardless of the channel distribution. By leveraging conditional value-at-risk (CVaR), we reformulate the probabilistic-form constraint into its deterministic counterpart to facilitate the analysis. Then, the reformulated problem is decomposed to optimize the transmit and receive scaling factors alternatively. Simulation results demonstrate that our proposal rigorously ensures robustness amid uncertainties and effectively reduces computation distortion when compared to the baseline methods.

Sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency (EE), and mass connectivity. To accomplish these goals, in this article, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) network is proposed while leveraging integrated sensing and communication (ISAC). The proposed AI-based framework allocates the desired power for beamforming by activating the required number of grids from the serving HMIMO base stations (BSs) in the CF network to serve the users. An optimization problem is formulated that maximizes the sensing utility function, which in turn maximizes the signal-to-interference-plus-noise ratio (SINR) of the received signal, the sensing SINR of the reflected echo signal, and EE, ensuring efficient power allocation. To solve the optimization problem, an AI-based framework is proposed to enable a decomposition of the NP-hard problem into two subproblems: 1) a sensing subproblem and 2) a power allocation subproblem. Initially, a variational autoencoder (VAE)-based scheme is utilized to solve the sensing subproblem that identifies the current location of the users with the sensing information. Then, a transformer-based mechanism is devised to allocate the desired power to users by activating the required grids from the serving HMIMO BSs in the CF network based on the sensing information achieved with the VAE-based scheme. Simulation results demonstrate that the proposed AI-based framework outperforms the long short-term memory and gated recurrent unit-based mechanisms, with cumulative power savings of 8.64% and 16.02%, and cumulative EE of 14.49% and 16.61%, accordingly, considering the ground truth values.

Vehicle-to-everything (V2X) communications is pivotal for modern transportation systems, but the challenges arise in scenarios with buildings, leading to signal obstruction and limited coverage. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Researchers are exploring on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360° coverage while utilizing the advantages of RIS technology. In this article, an STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitude and phase shift values of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two subproblems. For the first subproblem, we model the control variables as a Markov Decision Process and propose a combined double deep$Q$-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Numerical results demonstrate that our solution approach outperforms the vanilla DDQN approach by 5.2%, and our proposed system outperforms the conventional RIS by 39%.

The increasing popularity of electric passenger vehicles (EPVs) has significant implications for transportation networks and power grids. We aim to tackle the routing and charging dispatching problem for EPVs while considering charging station (CS) power limits. We formulate the problem using a clustered rolling framework and introduce an energy criterion to determine EPV availability for shuttle services. The EPV charging dispatching is modeled as a constraint programming problem under CS power limits, with a fixed charging rate assumed at the start of charging. The RCLBD algorithm, based on logic-based benders decomposition, effectively handles binary and continuous variables. The EPV routing model serves as the master problem, while the charging model acts as the sub-problem. Simulation experiments demonstrate the RCLBD algorithm’s performance and efficiency. The algorithm successfully provides efficient pickup and delivery services, minimizing waiting times for customers. It ensures successful routing and charging solutions for all arriving EPVs. The electricity cost of our proposed RCLBD algorithm is 2.13%; 10.68% lower than that of MIP and MIPC method when the number of EPVs is 750. Our proposed routing and charging algorithm shows good performance and efficiency, addressing the challenges posed by the increasing popularity of EPVs.

Since fog nodes are resource-constrained and imperfectly trusted heterogeneous devices, guaranteeing a real-time response to Internet of Things (IoT) tasks while optimizing system energy consumption remains a significant challenge. To overcome this, we first propose a acrlong DBC-enabled cloud–fog collaborative task scheduling architecture. Second, a task scheduling model is constructed to optimize system energy consumption and task deadline violation time while adhering to the IoT task response time restriction. Finally, two blockchain-enabled task scheduling algorithms are developed: 1) the reputation-based priority-aware algorithm (DB_RP) and 2) the accelerated ant colony system algorithm (DB_AACS). Extensive experiments are conducted to assess the proposed algorithm in four dimensions: 1) task completion rate; 2) system makespan; 3) system energy consumption; and 4) task deadline violation time. The experimental results demonstrate that the proposed algorithm is superior to the existing literature, and the acceleration strategy in DB_AACS is effective.

The introduction of satellite Internet resource-slicing technology can efficiently allocate satellite network resources and meet the personalized needs of different users. This article proposes a trust-based satellite Internet resource-slicing access authentication scheme, which solves the efficient and secure access requirements in situations where satellite communication and service resources are relatively limited. The working idea of this article is to provide users with access authentication protocols with different efficiencies through trust as a standard. Firstly, The user’s trust value is calculated by establishing a trust metric model based on Beta function, communication byte fluctuations, and centralized trend measurements. Drawing on the requirements of the security policy function in the resource slicing technology standard, assigning different security policies to users can both improve the fast access ability of high-trust users and reduce the priority of low trust users’ access. After that, based on the results of trust metrics, this paper proposes a two-factor-based no certificate satellite Internet slicing access authentication protocol for users with moderate trust levels. This protocol achieves the ability for users to access slicing services anonymously and efficiently through the use of resource-slicing credentials and managers. Final, this article verify the correctness and security of the protocol. Through communication cost comparison, it is shown that this protocol has fewer costs. Through trust simulation, the effectiveness of the trust scheme is analyzed and compared.

Autonomous underwater vehicles (AUVs) play a crucial role in data collection for underwater acoustic sensor networks (UWASNs). The limited capacity of individual AUV and the need for low-latency data collection necessitate the deployment of AUV swarms to achieve efficient and secure cooperative data collection. However, most existing works assume prior knowledge of sensor node locations, which is impractical in real-world AUV networks. Additionally, continuous data collection needs to be considered due to the sustained operation of sensors and cluster head replacement. To address these challenges, we propose a target uncertainty map assisted data collection scheme for AUV swarms based on the multiagent proximal policy optimization (MAPPO) algorithm. Specifically, the target uncertainty map is established by leveraging current and past search and collection results, guiding the AUV swarm to prioritize areas with higher probabilities of containing sensor nodes. Moreover, a digital pheromone mechanism incorporating repulsive and attractive pheromones is designed to establish an artificial potential field for adjusting the target uncertainty map. To further enable a comprehensive exploration of unknown environments, we introduce the Age of Information (AoI) as an indicator. Additionally, we consider the energy consumption associated with data collection to strike a balance between collection and energy efficiency, and derive a lower bound on the policy improvement achieved by the MAPPO algorithm. Simulation results have validated that the proposed scheme has a superior performance compared to the baselines, achieving an approximately 15% increase in the collection rate while reducing the energy consumption of data collection and AoI as well.

In contrast to methods relying on a centralized training, emerging Internet of Things (IoT) applications can employ federated learning (FL) to train a variety of models for performance improvement and improved privacy preservation. FL calls for the distributed training of local models at end-devices, which uses a lot of processing power (i.e., CPU cycles/sec). Most end-devices have computing power limitations, such as IoT temperature sensors. One solution for this problem is split FL. However, split FL has its problems, including a single point of failure, issues with fairness, and a poor convergence rate. We provide a novel framework, called hierarchical split FL (HSFL), to overcome these issues. On grouping, our HSFL framework is built. Partial models are constructed within each group at the devices, with the remaining work done at the edge servers. Each group then performs local aggregation at the edge following the computation of local models. End devices are given access to such an edge aggregated model so they can update their models. For each group, a unique edge aggregated HSFL model is produced by this procedure after a set number of rounds. Shared among edge servers, these edge aggregated HSFL models are then aggregated to produce a global model. Additionally, we propose an optimization problem that takes into account the relative local accuracy (RLA) of devices, transmission latency, transmission energy, and edge servers’ compute latency in order to reduce the cost of HSFL. The formulated problem is a mixed-integer nonlinear programming (MINLP) problem and cannot be solved easily. To tackle this challenge, we perform decomposition of the formulated problem to yield subproblems. These subproblems are edge computing resource allocation problem and joint RLA minimization, wireless resource allocation, task offloading, and transmit power allocation subproblem. Due to the convex nature of edge computing, resource allocation is done so utilizing a convex optimizer, as opposed to a block successive upper-bound minimization (BSUM)-based approach for joint RLA minimization, resource allocation, job offloading, and transmit power allocation. Finally, we present the performance evaluation findings for the proposed HSFL scheme.

As research on integrated sensing and communication (ISAC) progresses, it has been discovered that ISAC can be effectively utilized to enhance the security of wireless communications. Its sensing function can assist in both eavesdropping detection and physical-layer security techniques. In this article, our focus lies on addressing the security challenges associated with high-speed train-to-ground communication using ISAC technology. We explore a novel secure communication scheme. Specifically, we exploit the sensing capabilities of ISAC to detect eavesdropping at the receiving end and subsequently establish a signal blind zone at the location where eavesdropping occurs through beamforming and waveform optimization techniques. This approach ensures the achievement of secure wireless communication. Mathematically modeling the problem as an optimization problem, we derive a lower bound for simplification purposes. Subsequently, we employ an alternating optimization algorithm to iteratively find suboptimal solutions for the optimization variables. Through extensive simulation experiments and comparative analysis, we demonstrate that our proposed algorithm not only guarantees communication security but also outperforms existing algorithms in terms of efficiency.

The burgeoning technology of optical intelligent reflecting surfaces (OIRS) offers significant potential within the realm of visible light communication (VLC) systems. This is primarily attributed to the capability of OIRS to exploit reflected propagation paths, which in turn mitigates the inherent signal blockage challenges encountered in VLC. In this paper, a non-orthogonal multiple access (NOMA)-based multiple-input single-output (MISO) VLC system with the aid of OIRS is investigated, in which multiple light-emitting diodes (LEDs) serve multiple users equipped with a photodetector (PD) simultaneously. To this end, an analysis of the VLC channel gains is undertaken, encompassing the evaluation of both the line-of-sight (LoS) and the OIRS-reflected paths. Subsequently, the problem is formulated with the objective of jointly optimizing the active beamforming at LEDs and the passive beamforming at the OIRS to maximize the achievable sum data rate, while considering the constraints of OIRS and the successive interference cancellation (SIC) process. However, the formulated problem is non-convex. To address this challenge, a block coordinate descent (BCD) algorithm is introduced to decompose the original joint beamforming problem into two sub-problems. Specifically, relaxed iterative algorithms based on semi-positive definite relaxation and Taylor expansion are employed to solve these sub-problems. Additionally, a permutation-based genetic algorithm is proposed to tackle the decoding order problem. Meanwhile, the simulation results illustrate the improvement in the sum data rate achieved by the proposed algorithm, providing valuable insights for future research on OIRS-aided VLC systems.

In the space–air–ground integrated emergency communication network, unmanned aerial vehicles (UAVs) have become ideal candidates for expanding traditional base stations through the air–ground Line of Sight (LoS) link, providing more comprehensive and efficient support for emergency communication. To ensure timely information transmission among all the ground users (GUs) involved in rescue, utilizing fair communication can reduce communication conflicts caused by resource competition and ensure that the GUs can obtain the necessary communication resources to improve rescue efficiency. Therefore, this article investigates the joint optimization of trajectory planning and communication design of multiple UAV base stations (UAV-BSs), as well as the access control of GUs in intelligent collaborative air–ground communication systems. The optimization problem is modeled as a hybrid cooperative competition model, where GUs compete for limited UAV-BS resources to maximize their own long-term throughput, while UAV-BSs collaborate to provide maximum fair throughput for GUs in need. This model belongs to heterogeneous agent collaboration, where the goals of GUs and UAV-BSs are inconsistent, and the UAV-BS has inconsistent goals at different stages with or without GU requests. Therefore, a trajectory planning and communication design algorithm for intelligent collaborative air–ground communication (TPCD-ICAGC) algorithm is designed. By introducing a multihead attention mechanism to quickly determine the target correlation with other agents in a complex state space, so as to improve the adaptability of agents to the model and make more effective decisions. The simulation results show that TPCD-ICAGC outperforms other benchmark algorithms in terms of the fair communication services of UAV-BSs and the accumulative throughput of GUs.

This article considers a two-hop wireless-powered relay network consisting of multiple sources, multiple destinations, and one relay. The relay can receive energy from the sources and forward data to the destinations. We focus on the source selection problem during the energy transfer process and the resource allocation problem during the data transmission process. First, the relay can choose among all sources based on the transferred energy from the sources. A credit mechanism is introduced for the relay to achieve optimal selection. Second, a Stackelberg differential game-based model is adopted for the resource allocation problem in the data transmission process, using the differential equation to describe the dynamic variation of energy, and the Stackelberg game to describe the relationships between the sources and the relay. In the proposed approach, both sources and relays consider energy consumption and energy revenue. To find the optimal solutions, an adaptive dynamic programming-based algorithm is utilized. The Lyapunov-based stability analysis shows that the system has uniform ultimate boundedness and convergence. Finally, the trained neural networks can achieve optimal resource allocation strategies. Through extensive simulation experiments, the effectiveness of the proposed algorithm is verified.

Unmanned aerial vehicle (UAV) communications have become a prominent technology for emergency communications to enhance network services. This article investigates computing offloading and resource allocation in nonorthogonal multiple access (NOMA)-based UAV emergency communication scenarios. To minimize the computational overhead of the terminal device, a joint task offloading and resource allocation problem is investigated, where the computation overhead of the marine Internet of Things (IoT) device is measured as a weighting of the task completion time and the energy consumption of the device. The optimization of the transmission of IoT devices, the allocation of computing resources to UAVs, task offloading, and carrier allocation are formulated in the considered problem, which is an NP-hard mixed integer nonlinear programming problem. To reduce the complexity, we decompose it into two parts from the property of the problem: 1) the resource optimization problem and 2) the task offloading problem. To solve the resource allocation problem, we first decouple the problem and then use the proposed quasi-convex and convex optimization methods. Meanwhile, a low-complexity task offloading algorithm is designed to achieve a Nash-stable solution by introducing a coalition game approach based on this. Numerical results verify the algorithm’s effectiveness and are compared with other schemes in the literature.

Underwater acoustic covert communication (UACC) is a promising technology for improving the security of sensitive data transmission in the Ocean Internet of Things (OIoT). Compared with traditional UACC, autonomous underwater vehicles (AUV) are used to improve the performance of UACC by transmitting interference signals and relaying information. However, the UACC process of the covert transmitter is vulnerable to the detector’s detection, and the AUV-assisted UACC countermeasure process lacks theoretical analysis. Hence, we propose an AUV-assisted underwater acoustic covert communication game (AUACCG) model to study the countermeasure process between the AUV-assisted covert communicator and the detector in OIoT. Specifically, we utilize AUV transmitting the interference signals to reduce the detector’s capability, analyze the utility functions and strategy set of both sides and derive the equilibrium strategies. In addition, we analyze the concealment probability and the outage probability in UACC. Then, we derive the specific Nash equilibrium strategy in AUACCG. Simulation results show that our proposal can improve the underwater acoustic covert communication performance compared with the state-of-art works.

Unmanned aerial vehicles (UAVs)-assisted wireless communication will play an important role in the next-generation mobile communication network. However, the inherent open nature of the signal propagation environment may cause illegal eavesdropping and surveillance from adversaries. In addition, the intergroup co-channel interference among different cells further degrades the system performance. Hence, we consider a generic scenario of multiple UAV base stations (UAV-BSs) and ground users, where multiple terrestrial wardens attempt to detect the transmissions from UAV-BSs to users and a UAV-mounted jammer is employed to generate artificial noise to assist the covert communications. To ensure fairness, we formulate an optimization problem to maximize the minimum of the average rate lower bounds of all users by jointly optimizing user association, bandwidth allocation, UAV transmit power control, and UAV 3-D deployment, subject to the constraints of the detection error probability of each warden. To solve this mixed-integer nonconvex problem, we propose a suboptimal algorithm by applying block coordinate descent (BCD) method to solve three subproblems iteratively. Specifically, in each iteration, the subproblem of user association and bandwidth allocation is solved by a customized genetic algorithm (GA) first, where a closed-form expression for bandwidth allocation is obtained. Second, the subproblem of UAV transmit power control is solved by using successive convex approximation (SCA) techniques. Finally, suboptimal 3-D positions of the UAVs are obtained through particle swarm optimization (PSO)-based algorithm. Extensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm compared to benchmark schemes in terms of improving the minimum of the average rate lower bounds of all users.

The rapid development of the acrlong IoV has placed a heavy burden on the edge server. Conversely, many idle vehicles parked near the vehicle in demand are not utilized. Opportunistic computing of vehicles can organize these idle vehicles to provide computing services, but this also requires a secure data-sharing scheme to offer support. Although the existing ciphertext policy attribute-based encryption (CP-ABE) can provide a secure fine-grained data sharing, they either map the data to an algebra element that cannot be applied in practice due to the limited length, or they are hybrid schemes that cannot satisfy the forward security. In addition, they require a trusted authority (TA), which may be unrealistic in implementation. To cope with these issues, we propose a dual hybrid CP-ABE scheme without a TA for vehicular opportunistic computing (VOC) in this article. We exploit the dual hybrid mechanism to solve the problem of forward security in hybrid schemes and eliminate the TA by combining the characteristics of VOC. Then, we prove the acrlong IND-sCPA and forward security of the scheme. Finally, we evaluate the computation, storage, and communication cost from theoretical and simulation perspectives and compare them with other typical schemes. These results illustrate that the proposed scheme has better efficiency and lower storage and communication cost in general.

With the rapid development of autonomous driving and edge computing, vehicular edge computing (VEC) has become an emerging paradigm that allows vehicles with abundant computational resources to work as edge nodes. By introducing vehicles as infrastructures, VEC has the potential to improve users’ quality of experience and decrease operator’s deployment expenditure, especially for hot spots. In this article, a novel VEC-based resource reservation framework is designed to handle the time-varying computation requests. To articulate realistic scenarios, the online durations of provider vehicles (PVs) are assumed to be different. Besides, the PVs will not always be online to wait for the reservation assignment for the limited revenue, i.e., the PVs are dynamic and the computational resource reservation points (CRRPs) are static. In this way, dynamic matching is leveraged to model the interaction between the PVs and CRRPs. To prevent the CRRPs from manipulating their preferences for better partners, a strategy-proof and stable resource reservation algorithm is proposed to ensure all CRRPs are truthful during the resource reservation procedure. Finally, numerical simulation results are presented to validate the proofness, truthfulness, and performance of our proposed resource reservation algorithm.

The promising visible light communication (VLC) technology, which performs superior for Internet of Things (IoT) networks, can alleviate the spectrum congestion of current radio frequency communications. To overcome the drawbacks of VLC such as blockages and high path loss, we propose a multiple-input single-output (MISO) VLC system equipped with optical intelligent reflecting surface (OIRS), to maximize the asymptotic capacity in the high signal-to-noise-ratio regime. Specifically, the characteristics of the OIRS-reflected channel are discussed for the developed OIRS-assisted MISO VLC system, based on which the OIRS optimization can be transformed into an association problem between the OIRS reflecting elements and the transmitter antennas. Next, considering different emission power on antennas, the capacity lower and upper bounds are derived for three different cases and the asymptotic capacities are obtained accordingly, thus giving rise to the objective function of the capacity maximization problem. To solve this problem, we propose a priori-assisted alternating optimization algorithm to jointly optimize the OIRS element alignment and transmitter emission power, which not only can achieve the globally optimal result but also has a low complexity since the solution to each subproblem is given in closed form. Finally, extensive numerical results are provided to show the performance of the proposed algorithm and offer beneficial insights for the design of the proposed OIRS-assisted MISO VLC.

The deployment of mobile edge computing services in LEO satellite networks achieves seamless coverage of computing services. However, the time-varying wireless channel conditions between satellite–terrestrial channels and the random arrival characteristics of ground users’ (GUs) tasks bring new challenges for managing the LEO satellite’s communication and computing resources. Facing these challenges, a stochastic computation offloading problem of joint optimizing communication and computing resources allocation and computation offloading decisions is formulated for minimizing the long-term average total power cost of the GUs and the LEO satellite, with the constraint of long-term task queue stability. However, the computing resource allocation and the computation offloading decisions are coupled within different slots, thus making it challenging to address this problem. To this end, we first employ the Lyapunov optimization to decouple the long-term stochastic computation offloading problem into the deterministic subproblem in each slot. Then, an online algorithm combining deep reinforcement learning and conventional optimization algorithms is proposed to solve these subproblems. Simulation results show that the proposed algorithm can achieve the superior performance while ensuring the stability of all task queues in LEO satellite networks.

Autonomous underwater vehicles (AUVs) can function as sensor nodes in Internet of Underwater Things (IoUT), contributing to ocean exploration and monitoring by collecting and transmitting data to the base station. Most of the routing algorithms applied to IoUT require the participation of stationary nodes and seldom consider the fluctuations of network topology, which cannot be directly applied to the IoUT composed of AUVs. The focus of this research is to examine the problem of packet routing in a dynamic AUV-assisted IoUT, with the ultimate goal of ensuring the effective transmission of underwater information. We analyze the transmission pattern of underwater acoustic signals and the consequent communication disruption between AUVs, which helps establish the Age of Information (AoI) and bit error rate (BER) of data through modeling. A routing algorithm that utilizes the branch-and-bound (BB) technique has been suggested, alongside the introduction of the Value of information (VoI) to enable the joint optimization of the AoI and BER. We describe two nearly optimal heuristic algorithms for networks with a high number of AUVs. The AFA-ACO-BB strategy is designed based on the above algorithms and the influence of AUV motion on link reliability is considered. Moreover, we have developed a power regulation mechanism that can effectively minimize the occurrence of network packet loss and energy waste. The simulation results demonstrate that the proposed scheme outperforms certain classically related schemes in terms of AoI and BER, while simultaneously maintaining superior packet loss rate (PLR) and energy consumption.

With the development of the Internet of Things (IoT) and 5G/6G technologies, there has been significant interest in the applications of the Internet of Vehicles (IoV) and multiaccess edge computing (MEC) in intelligent transportation systems. The significant increase in the number of vehicles currently accessing the Internet has highlighted the inability of some existing resource-constrained vehicles to adequately meet the demands of computationally intensive and latency-sensitive applications. There is a significant challenge in designing efficient task offloading strategies to enhance the utilization of computational resources and deliver high-quality services to vehicle users. In this article, we propose a four-tier computing architecture with local computing, vehicle-to-vehicle (V2V) computing, MEC computing, and mobile cloud computing (MCC), which can provide heterogeneous computing resources for multiple task vehicles and flexible offloading options of different types of vehicle tasks. We optimize the offloading decision and resource allocation with the objective function of minimizing the system cost. The nonconvex objective function and constraints both contain binary variables, which leads to NP-hard property. To solve this critical problem, we propose an alternating direction method of multipliers (ADMM)-based multivehicle task offloading scheme for IoV-MEC (AMTOS), to transform the nonconvex problem into a convex one by relaxing the binary variables, and provide an approximate optimal solution. Afterward, a binary variable recovery algorithm is used to recover the binary variables. Simulation results show that the algorithm can significantly reduce the system cost, compared with existing literature.

Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication, and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this article, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean-field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose an MFG-based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.

Dense low-power small cell base station (SBS)-based heterogeneous wireless cellular networks (HetNets) have attracted much attention to achieving high-traffic density and peak rate performance. However, the serious energy consumption problem is still a challenge for HetNets. The use of renewable energy (RE) has been considered as one promising solution for the above problem. This article proposes an energy trading scheme among base stations in RE-based HetNets. All SBSs in HetNets are considered as either the energy demander (SBS-ED) or the energy supplier (SBS-ES) based on their abilities in producing RE, and the macro base station (MBS) works as the energy trading manager to control the trading price. A dynamic evolutionary game-based energy trading model between SBS-ES and SBS-ED is established to achieve cooperative energy trading, and the evolutionary stable strategy (ESS) of the proposed model is analyzed. The pricing mechanism of MBS is also investigated, which can effectually affect the EES performance of the proposed model. It is concluded that the MBS’s strategy in the trading price can affect the energy trading strategies of the SBSs. An energy transmission model is proposed, and the minimum energy loss is considered as the goal to obtain the optimal solutions. Numerical results are given to prove the validity and correctness of our proposed method.

In this article, we investigate the pursuit-evasion game of multiple autonomous underwater vehicles (AUVs) in a complex ocean environment. The pursuer AUVs need to optimize their trajectories to avoid obstacles and dangerous vortex regions in the environment in order to pursue the escaper AUV. Both the pursuer and escaper can sense each other with limited detection capabilities for further pursuit or escape. As the underwater pursuit-evasion (UPE) game is a high-dimensional NP-hard problem, we innovatively transform it into a finite-horizon Markov game process and propose a decentralized training and decentralized execution efficient training framework based on the offline reinforcement learning. During the training process, we propose multiagent independent soft actor–critic to facilitate policy improvement and generate the offline data set, and propose multiagent independent decision transformer for model training in the UPE game. Extensive simulations demonstrate the scalability and generalization ability of our proposed training framework, which can achieve excellent performance in the UPE games under different conditions and environments with only a few AUVs participating in policy improvement to generate the high-quality offline data set.

In this article, based on martingale theory, we investigate the problem of maximum throughput in hybrid energy harvesting wireless communication systems (EH-WCSs) under energy storage and delay (or backlog) constraints. Specifically, the energy supply and data transmission of the hybrid EH-WCS are modeled as two queuing systems. For the first energy supply queueing system, we construct corresponding martingales for each type of energy harvesting (EH) process and the system’s energy consumption process. Leveraging the multiplicativity of martingales, the stochastic characteristics of the hybrid EH process are described in the martingale domain. On this foundation, a closed-form expression for the energy depletion probability bound (EDPB) under various energy storage constraints is derived. In the second data transmission queueing system, to capture the impact of channel fading on the system’s service, we map the arrival and service processes to the signal-to-noise ratio (SNR) domain and construct the corresponding martingales. A martingale parameter is proposed that connects the martingales of the arrival and service processes with the system’s EDPB. Based on this, the closed-form expressions for the delay violation probability bound and backlog violation probability bound are derived. Utilizing these derived performance bounds, we address the maximum throughput optimization problems under the energy storage and delay (or backlog) constraints. Furthermore, we instantiate a scenario and provide guidance on the impact of resource allocation on maximum throughput through simulation and validation, offering insights for achieving green communication networks.

With the proliferation of cellular vehicle-to-everything (C-V2X), connected and automated vehicles (CAVs) are gradually being commercialized. CAVs can interact with road infrastructure and human-driven vehicles (HDVs) to acquire relevant traffic information, thereby altering the characteristics of the traditional traffic flow. The emergence of CAVs is widely believed to bestow benefits to the traffic system in terms of safety, efficiency, and energy consumption. Nevertheless, as with most phenomena, there are two sides to the coin. Further exploration is necessary to determine whether the emergence of CAVs will trigger adverse effects and the underlying factors that may induce adverse effects. To be specific, this article first delves into how selfish driving behaviors (egoism CAV control strategy) can have an unfavorable impact on the performance of the traffic systems, thereby lowering the traffic efficiency. Subsequently, we develop an unselfish (altruism) CAV control strategy that aims to achieve the global optimization and improve the overall road operational capacity. Based on the simulation results obtained at different inflow and outflow rates on highway, it is evident that egoism driving behavior leads to a 11.55% decrease in average speed performance as compared to the noncontrol strategy, while altruism driving behavior results in a 20.14% improvement. Furthermore, we compare the proposed strategy with the current road infrastructure control, which only improves the average speed performance by 11.6%. This indicates that controlling CAVs has the potential to replace the deployment of the traditional road infrastructure, thereby optimizing the social and economic benefits. This article can provide insightful guidance for the future policy formulation in the transportation authorities, wherein the emergence of CAVs needs to be effectively regulated based on the altruism, thus fostering the establishment and development of a safe and efficient mixed traffic ecosystem.

Nowadays, vehicular networks have emerged as a boosting technology to enhance traffic efficiency and safety within transportation systems. As the amount of onboard data increases and data privacy concerns grow, federated learning (FL) has gained popularity for harnessing the data for intelligent transportation operations. To satisfy the strict latency criteria in vehicular networks, a quantization scheme is employed within FL to reduce the size of local models before uplink transmission. In this paper, considering the nature of vehicles’ high mobility, we aim to optimize both the learning performance and latency simultaneously by jointly considering the communication resource budget and quantization strategies. Specifically, we first analyze the convergence performance of the quantized FL, which demonstrates the effects of both quantization error and the number of clients on the convergence rate. Then, we formulate a multi-objective optimization problem (MOP) to maximize the number of participating clients and minimize the overall latency, by jointly optimizing the quantization level, wireless resource allocation and client selection. To deal with the MOP, we decompose the MOP into a set of scalar optimization subproblems, each formulated as a Markov Decision Process (MDP). To solve the MDP in high-mobile vehicular networks, we propose a novel deep reinforcement learning-based vehicle heterogeneous quantization FL (DRL-VQFL) method, which leverages a DRL framework built upon the proximal policy optimization algorithm. Then, a parameter transfer strategy is employed to solve the neighboring subproblems efficiently. Our extensive simulations demonstrate the effectiveness and efficiency of the DRL-VQFL approach, showcasing its superiority over other benchmark methods.

The Internet of Things (IoT) ecosystem is undergoing a significant evolution through its integration with satellite networks, empowering remote and computation-intensive IoT tasks to leverage computing services via satellite links. Current research in this field predominantly focuses on minimizing latency and energy consumption in computation offloading, yet overlooks the substantial costs incurred by satellite resource utilization. To address this oversight, we introduce a cost-effective hybrid computation offloading (CE-HCO) paradigm in satellite-terrestrial integrated networks (STINs) in this article. First, we propose the 5G-based system framework facilitates gNB and user plane function functionalities on satellites and fosters collaboration between public cloud providers and satellite operators. The framework is in line with the latest 3GPP activities and business models in satellite computing. Then, we formulate the CE-HCO problem, aiming to minimize total computation offloading costs while satisfying diverse user latency requirements and adhering to satellite energy constraints. To tackle this NP-hard problem, we develop an algorithm employing the penalty method and successive convex approximation to simplify the complex mixed-integer nonlinear programming into tractable convex iterations. Simulation results show that our approach outperforms existing baselines in balancing performance and cost, and offer guidance on pricing policies for satellite computing services to promote future commercial growth.

A combination of Industrial Internet of Things (IIoT) and federated learning (FL) is deemed as a promising solution to realize Industry 4.0 and beyond. However, scheduling more IIoT devices engaged in FL contributes to accelerated learning speed, but resulting in increased learning cost in terms of energy consumption and model accuracy reduction. In this article, we investigate the tradeoff between learning speed and cost in a three-layer FL-enabled IIoT system. Particularly, a weighted learning utility function is designed by capturing such a tradeoff. We aim to maximize the weighted learning utility in an FL training round by jointly optimizing the edge association as well as the allocations of resource block, computation capacity, and transmit power of the IIoT device. The resulting problem is a nonconvex and mixed-integer optimization problem, and consequently, it is difficult to solve. We thereby decompose the original problem into three subproblems, and then propose an overall alternating optimization algorithm to solve the subproblems iteratively until convergence. Via experimental results, it is demonstrated that the proposed scheme significantly improves the system-wide learning utility as compared to other baseline schemes. It is also shown that the proposed scheme can achieve the optimized tradeoff between learning speed and learning cost.

Recent years have witnessed an increasing interest in leveraging reconfigurable intelligent surfaces (RISs) to enhance the capabilities of integrated sensing and communication (ISAC) systems. RISs are advantageous in improving detection and communication performance, especially in challenging environments characterized by nonLine of Sight (NLOS) conditions and dense urban settings. In this article, a hybrid RIS, comprising passive reflecting elements and active sensors, and multiple fully passive RISs are deployed to enhance an ISAC system, where the direct paths between the base station (BS) and users/targets are blocked. The signal sent from the BS and reflected by RISs is received by the communication user, and simultaneously scattered by the target toward the sensors of the hybrid RIS. A joint optimization of the transmit covariance matrix at the BS and phase-shifting matrices at RISs is formulated, which considers the tradeoff between the communication and sensing performance. The optimization is based on the derived closed-form communication achievable rate by leveraging the free probability theory and positioning error bound (PEB) via the Cramér-Rao lower bound (CRLB) analysis. The block coordinate descent (BCD) algorithm is utilized to tackle the nonconvex problem, where the transmit covariance matrix and phase-shifting matrices are optimized iteratively. Therein, the Riemannian gradient descent algorithm is exploited for optimizing the phase-shifting matrices. Numerical results verify the effectiveness of the proposed algorithm, and both communication and sensing performance gains increase with the number of RIS panels and RIS elements.

Considering the wide-area distribution and limited transmission power of sensing devices in the Internet of Underwater Things (IoUT), employing autonomous underwater vehicles (AUVs) to collect data is considered a promising solution. While most existing AUV-assisted data collection schemes primarily focus on enhancing data collection throughput and identifying the shortest path, they often overlook the influence of the underwater environment on AUV and the timeliness of data collection. In this article, we design a multi-AUV-assisted data collection system, in which AUVs select their own target devices to collect data according to the data upload urgencies of IoUT devices. Considering the disturbance of turbulent ocean environment and the limited energy of AUV, we propose an environment- and energy-aware AUV-assisted data collection scheme. This scheme aims to conduct path planning for multiple AUVs based on perceived environmental information, including turbulent fields and device statuses. The primary goals are to maximize the sum data collection rate and total data throughput, minimize AUV energy consumption, reduce the average data overflow times. To solve this high-dimensional NP-hard problem, we first model the problem as a Markov decision process, and propose a multiagent independent soft actor–critic to solve it. Extensive simulations validate the effectiveness and adaptability of our approach.

Leveraging the concept of the electromagnetic signal and information theory, holographic multiple-input multiple-output (MIMO) technology opens the door to an intelligent and endogenously holography-capable wireless propagation environment, with their unparalleled capabilities for achieving high spectral and energy efficiency. Less examined are the important issues such as the acquisition of accurate channel information by accounting for holographic MIMO’s peculiarities. To fill this knowledge gap, this paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO’s electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.

Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted by notable propagation attenuation, molecular absorption, and space losses in sub-terahertz (THz) frequencies. This paper introduces a novel approach to maximize LEO satellite coverage by leveraging reconfigurable intelligent surface (RIS) within 6G sub-THz networks. Optimization objectives include improving end-to-end (E2E) data rate, optimizing satellite-remote user equipment (RUE) associations, data packet routing within satellite constellations, RIS phase shift, and ground base station (GBS) transmit power (i.e., active beamforming). The formulated joint optimization problem poses significant challenges because of its time-varying environment, non-convex characteristics, and NP-hard complexity. To address these challenges, we propose a block coordinate descent (BCD) algorithm that integrates balanced K-means clustering, multi-agent proximal policy optimization (MAPPO) deep reinforcement learning (DRL), and whale optimization algorithm (WOA) techniques. The performance of the proposed approach is demonstrated through comprehensive simulation results, demonstrating its superiority over existing baseline methods in the literature.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the human-centric Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no longer isolated physical spaces but interfaces that extend the virtual worlds to the physical world. Accessing the human-centric Metaverse via autonomous vehicles (AVs), drivers and passengers can immerse in and interact with 3D virtual objects overlaying views of streets on head-up displays (HUD) via augmented reality (AR). The seamless, immersive, and interactive experience rather relies on real-time multi-dimensional data synchronization between physical entities, i.e., AVs, and virtual entities, i.e., Metaverse billboard providers (MBPs). However, mechanisms to allocate and match synchronizing AV and MBP pairs to roadside units (RSUs) in a synchronization service market, which consists of the physical and virtual submarkets, are vulnerable to adverse selection. In this paper, we propose an enhanced second-score auction-based mechanism, named EPViSA, to allocate physical and virtual entities in the synchronization service market of the vehicular Metaverse. The EPViSA mechanism can determine synchronizing AV and MBP pairs simultaneously while protecting participants from adverse selection and thus achieving high total social welfare. We propose a synchronization scoring rule to eliminate the external effects from the virtual submarkets. Then, a price scaling factor is introduced to enhance the allocation of synchronizing virtual entities in the virtual submarkets. Finally, rigorous analysis and extensive experiments demonstrate EPViSA can achieve at least 96% of the social welfare compared to the omniscient benchmark while ensuring strategy-proof and adverse selection free through a simulation testbed.

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network (NN) models, the complexity of multiple access (MA) for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in sixth-generation (6G) systems. Traditional MA design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high quality-of-service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning (ML) techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state of the art and further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

The Internet of Things (IoT) finds applications across diverse fields but grapples with privacy and security concerns. Blockchain offers a remedy by instilling trust among IoT devices. The development of blockchain in IoT encounters hurdles due to its resource-intensive computation processing, notably in PoW-based systems. Cloud and edge computing can facilitate the application of blockchain in this environment, and the IoT users who want to mine in blockchain need to pay the computation resource rent to the Cloud Computing Service Provider (CCSP) for offloading the mining workload. In this scenario, these IoT miners can form groups to trade with CCSP to maximize their utility. In this paper, a mixed model of the Stackelberg game and coalition formation game is embraced to address the grouping and pricing issues between IoT miners and CCSP. In particular, the Stackelberg game is utilized to handle the pricing problem, and the coalition formation game is employed to tackle the best group partition problem. Moreover, a coalition formation algorithm is proposed to obtain a nearoptimal solution with very low complexity. Simulation results show that our proposed algorithm can obtain a performance that is very near to the exhaustive search method, outperforms other existing schemes, and requires only a small computation overhead.

Underwater covert communication (UCC) technology can prevent legitimate transmission from being intercepted upon by potential eavesdroppers while ensuring a certain rate at the receiver under the condition of underwater acoustic channels. Previous studies have focused on UCC designs that rely on fixed transmitters and receivers, with limited attention given to dynamic moving senders, such as the widely-used autonomous underwater vehicle (AUV). Therefore, the establishment of a secure link between the mobile AUV and the receiver remains unexplored. In this paper, we construct an AUV-aided UCC architecture. Specifically, leveraging the unique characteristics of the underwater environment i.e., time-variant channel, severe attenuation, and ambient noise, the AUV plans its trajectory from the settled start point to the destination, adjusting its transmission power for covert communications. Accounting for both green energy consumption and communication security, we develop a novel multi-objective deep deterministic policy gradient (MODDPG) framework for jointly optimizing AUV’s diving energy consumption as well as effective throughput under the covertness constraint. Moreover, we propose an active-trust mechanism at the receiving side to pose an extra safe guard. To handle this, an evolutionary game model between the receiver and eavesdropper is built. Simulations and numerical results demonstrate that our proposed method can achieve a Pareto-optimal solution for covert communications with rapid convergence speed. The evolutionary stable strategy (ESS) enables the receiver to attain superior benefits and security compared to other strategies.

Satellite-ground integrated heterogeneous networks can relieve network congestion, release network resources and provide ubiquitous intelligence services for terrestrial users. Furthermore, digital twin technology can enable nearly-instant data mapping from the physical world to digital systems. The integration between satellite-ground integrated heterogeneous networks and digital twin alleviates the gap between data analyses and physical unities. However, the current challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, severely affect the improvement of quality of service. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing privacy overhead, which is able to perform computation offloading, decrease channel interference, and improve privacy protection. Due to the long-term task queue in Stackelberg model, we propose a Lyapunov stability theory-based model-agnostic meta-learning aided multi-agent deep federated reinforcement learning framework to transfer the long-term task queue into the single time slot, and then optimize the central processing unit frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, several performance analyses show that the proposed learning framework can strengthen the privacy protection, approach the optimal time average function, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.

Collaboration between low Earth orbit (LEO) and geostationary Earth orbit (GEO) satellites in space communication networks has the advantages of wider coverage and higher communication capacity. However, effective resource allocation in the space communication network faces significant challenges due to incomplete information introduced by the highly dynamic communication environment. In this work, we focus onMatchingGame-based resource allocation strategy withIncomplete information in the spaceCommunication network, called MAGIC. Specifically, we formulate the multi-dimensional resource allocation with incomplete information as the revenue maximization problem of access satellite, which is the sum priorities of the successfully accessed users. The revenue maximization problem is a mixed integer nonlinear programming problem, and a three-sided matching game is employed to solve it. Meanwhile, we apply a model-free reinforcement learning framework to pre-train the historical network data to compensate for the shortcomings caused by incomplete information. Furthermore, user-optimal and access satellite-optimal resource allocation algorithms are designed to achieve optimal resource scheduling. Simulation results demonstrate the effectiveness and convergence of proposed algorithms from the single time slot and multiple time slot perspectives of different network parameters.

The development of integrated satellite-terrestrial networks has gained significant attention from both industry and academia in recent years, owing to their potential for delivering low latency, high dependability, strong resilience, ubiquitous connectivity and global broadband coverage services. However, due to the ever-changing nature of satellite topology and the complexity of diverse integrated satellite-terrestrial networks, routing requests is challenging. In this paper, the vehicle movement is uncertain introducing the intermittent connectivity related to vehicles. Therefore, we propose a distributionally robust optimization (DRO) model to minimize, under uncertain latency probability distributions, the expected worst-case overall task routing delay from source to target user equipment through satellite constellation. The model addresses undetermined uploading and downloading latency between automobiles, satellites, and user equipment by employing the Wasserstein ambiguity set, allowing for unpredictable vehicle mobility and intermittent connections. By reformulating the problem into a tractable form, we determine the optimal routing path for task uploading, satellite constellation, and task downloading. Ultimately, the performance of the proposed DRO model demonstrates the model’s ability to address the challenges of integrated satellite-terrestrial network routing.

Laser inter-satellite links (LISLs) have greatly extended communication distance between satellites, allowing for establishment of dynamic links to reduce communication delay. However, a closed-loop control is required for LISL, which causes high energy consumption. Proper scheduling of dynamic LISLs can effectively reduce energy consumption and communication delay. In this study, a satellite link mode with three fixed LISLs and one dynamic LISL is designed, and its feasibility is analyzed. The optimization problem is formulated and transformed into a Markov decision process (MDP) by modeling it as a sequential decision. By decomposing states, actions, and reward functions, the MDP is divided into the proposed multi-agent deep reinforcement learning (MADRL). Moreover, compressed sensing is utilized to cut down state information to reduce communication, storage, and computation overhead. Furthermore, network parameters and experience sharing, and prioritized experience replay have been adopted to improve stability and convergence speed of network training with a large number of agents. Experimental results show that under different routing strategies, the proposed MADRL can reduce energy consumption by over 15% and delay by approximately two hops compared to fixed LISLs scenario within several iterations.

This paper considers an unmanned aerial vehicle (UAV)-assisted communication system for data collection in urban areas, where multiple UAVs are dispatched to harvest data from multiple ground user equipments (UEs). We adopt a blockage-aware channel model to characterize the practical blockage effects for air-to-ground (A2G) links caused by buildings. Aiming to minimize the mission completion time while satisfying the data collection requirements of UEs, we formulate a problem by jointly optimizing the UAV three-dimensional (3-D) trajectory and resource allocation, including the UE scheduling and subcarrier assignment. To solve the formulated non-convex combinatorial programming problem, we propose a suboptimal algorithm that solves two subproblems iteratively. Specifically, in each iteration, the trajectory design subproblem jointly optimizes the UAVs’ waypoints and time slot length to decrease the mission completion time, which is solved by employing block successive convex approximation (BSCA). For the resource allocation subproblem, we develop a heuristic algorithm for UE scheduling and subcarrier assignment to increase the collected data volume for a given time duration. Simulation results demonstrate the superior performance of the proposed algorithm in terms of mission completion time compared to benchmark schemes.

In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub-6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.

The distributed subgradient (DSG) method is a widely used algorithm for coping with large-scale distributed optimization problems in machine-learning applications. Most existing works on DSG focus on ideal communication between cooperative agents, where the shared information between agents is exact and perfect. This assumption, however, can lead to potential privacy concerns and is not feasible when wireless transmission links are of poor quality. To meet this challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of the data. Compared with perfect data, quantization poses fundamental challenges to maintaining data accuracy, which further impacts the convergence of the algorithms. To overcome this problem, we propose a DSG method with random quantization and flexible weights and provide comprehensive results on the convergence of the algorithm for (strongly/weakly) convex objective functions. We also derive the upper bounds on the convergence rates in terms of the quantization error, the distortion, the step sizes, and the number of network agents. Our analysis extends the existing results, for which special cases of step sizes and convex objective functions are considered, to general conclusions on weakly convex cases. Numerical simulations are conducted in convex and weakly convex settings to support our theoretical results.

The rapid development of indoor location-based services (LBS) has raised concerns about location privacy protection in the 3-dimensional (3D) space. The existing 2-dimensional (2D) location privacy protection mechanisms (LPPMs) cannot effectively resist attacks in 3D environments. Furthermore, users may have various sensitive attributes at different locations and times. In this paper, we first formally study the relationship between two complementary notions of geo-indistinguishability and distortion privacy (i.e., expected inference error) in the 3D space and develop a two-phase personalized 3D LPPM (P3DLPPM). In Phase I, we search for neighboring locations to formulate a protection location set (PLS) for hiding the actual location based on the above-mentioned relationship. To realize this, we develop a 3D Hilbert curve-based minimum distance searching algorithm to find the PLS with minimum diameter for each location while guaranteeing differential privacy. In Phase II, we put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. It generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that the proposed P3DLPPM can significantly improve personalized privacy protection while meeting the user's QoS needs.

Efficient data processing and computation are essential for the Industrial Internet of Things (IIoT) to empower various applications, which can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this article, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

This article offers a novel perspective on identifying the critical branches under load redistribution (LR) attacks. Compared to the existing literature that is largely disruption-driven and based on dc state estimation, we propose to address the threat from LR attacks on a more fundamental level by modeling and analyzing the circulation of false data within the cyber network resulting from the coordinated branch and node measurement manipulation based on ac state estimation. We reveal the underlying mechanism that disturbing the coordinated and reconciled interactions among false data injections can effectively sever the completeness and consistency of the LR attack, thus reducing its damaging effect. We then develop a scalable and computationally efficient critical branch identification approach that evaluates and ranks branches in terms of their criticality according to the graph model of the false data circulation. Case studies are conducted on IEEE 14-, 39-, 118-bus systems and several large-scale models to validate the effectiveness and computational efficiency of the proposed approach. Simulation results show that the proposed approach scales well with the size of the system and can effectively mitigate the damaging effects of the LR attack in terms of operation cost and load shedding.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

Compared with terrestrial networks, unmanned aerial vehicles (UAVs) have the characteristics of flexible deployment and strong adaptability, which are an important supplement to intelligent transportation systems (ITS). In this paper, we focus on the multi-UAV network area coverage problem (ACP) which require intelligent UAVs long-term trajectory decisions in the complex and scalable network environment. Multi-agent deep reinforcement learning (DRL) has recently emerged as an effective tool for solving long-term decisions problems. However, since the input dimension of multi-layer perceptron (MLP)-based deep neural network (DNN) is fixed, it is difficult for standard DNN to adapt to a variable number of UAVs and network users. Therefore, we combine Transformer with DRL to meet the scalability of the network and propose a Transformer-based deep multi-agent reinforcement learning (T-MARL) algorithm. Transformer can adapt to variable input dimensions and extract important information from complex network states by attention module. In our research, we find that random initialization of Transformer may cause DRL training failure, so we propose a baseline-assisted pre-training scheme. This scheme can quickly provide an initial policy model for UAVs based on imitation learning, and use the temporal-difference(1) algorithm to initialize policy evaluation network. Finally, based on parameter sharing, T-MARL is applicable to any standard DRL algorithm and supports expansion on networks of different sizes. Experimental results show that T-MARL can make UAVs have cooperative behaviors and perform outstandingly on ACP.

Unmanned aerial vehicle (UAV) relay networks with flexible and controllable characteristics are expected to complement the capacity of the gNB. This paper studies the multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) in a multi-UAV relay network, where the strict latency requirement of URLLC can be achieved by the preemptive multiplexing of eMBB resources. However, this may affect eMBB reliability due to the transmission interruptions. Moreover, given the limited energy resources of UAVs, there is an inherent tradeoff among reliability, delay, spectral efficiency, and energy efficiency. To address these challenges, this paper develops a hierarchical UAV-assisted eMBB/URLLC multiplexing scheduling framework. For the eMBB scheduler, we first utilize multiple UAVs to assist the gNB in relaying eMBB traffic and formulate the eMBB resource allocation problem as an optimization problem. Then, we propose a decomposition-relaxation-optimization algorithm to maximize eMBB data rates while considering the personalized fairness of resource allocation and UAV power consumption. For the URLLC scheduler, we further consider the multiplexing of eMBB/URLLC traffic based on the optimization of eMBB resources. To reduce the performance fluctuations of eMBB, we propose a novel cross-slot strategy to schedule URLLC within two time slots rather than one time slot as in existing works. With this strategy, a deep reinforcement learning-based algorithm is proposed to obtain the optimal strategy for the preemption of URLLC on eMBB. Simulation results show that the proposed algorithms outperform the benchmark schemes in terms of convergence rate, eMBB reliability, personalized resource fairness, UAV consumption, and URLLC satisfaction.

Unmanned Air Vehicle (UAV) has been widely used as the flying edge server to support ground vehicles’ Onboard-Unit (OBU) applications. In this work, we address the challenges of training an adaptive learning model which can be deployed on distributed energy-limited UAVs for making highly-reliable low-latency vehicle-to-UAV (V2U) computation offloading. Firstly, we formulate a two-objective mixed integer programming (MIP) problem for optimizing the energy consumption and offloading utility under the robust reliability constraints. The generalized Chebyshev inequality is applied to transform the chance constraints, and then, the minimum transmission power which satisfies the reliability threshold under the worst case is derived. Then, we decompose the primal problem into the IP subproblem while guaranteeing the Pareto optimality. An adaptive Q-value adjustment based deep reinforcement learning (ADRL) model is proposed, which calculates the expected return in theoretic via the heuristic algorithm, and uses it to replace the Q-value from the target network. The replacement is conducted at an adaptive frequency for saving training time and improving learning results. Comprehensive studies demonstrate the advantages of the proposed ADRL in improving the offloading utility, energy efficiency and convergence rate, when comparing with other classical DRL models and optimization algorithms.

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions.

The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication.SipDeep, proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments,SipDeepdemonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

With the advancement of intelligent transportation systems, a series of diversified V2X applications come into being, which have different key performance indicators (KPIs) and transmission features. Moreover, multi-tier computing as a new system-level architecture distributes computing and communication capabilities anywhere between the cloud and the end-user. Unfortunately, the existing network paradigm for V2X services adopts a one-shot allocation of resources ignoring the inherent differences of V2X service. To cope with these problems, three types of refined network slices for V2X services are first proposed to simultaneously support heterogeneous service characteristics without excessively splitting resources. Considering the spatiotemporal correlation between service traffic and physical resources, a jagged slicing in multi-tier dynamic resources, which forms a “slice sandwich” brightly, is realized by a dual timescale intelligent resource management scheme. The inter-slice resource configuration is based on neural bandits with upper confidence bounds at each large-time period, while the exclusive resources are managed elastically by deep Q-learning in terms of the real-time changing network state in the small slot. We developed a simulation environment by Simulation of Urban Mobility (SUMO) including real-world road conditions and traffic models. The experiment results demonstrate that the proposed scheme can effectively guarantee KPIs of V2X services and improve the system revenue compared with benchmark algorithms.

Network traffic classifiers of mobile devices are widely learned with federated learning(FL) for privacy preservation. Noisy labels commonly occur in each device and deteriorate the accuracy of the learned network traffic classifier. Existing noise elimination approaches attempt to solve this by detecting and removing noisy labeled data before training. However, they may lead to poor performance of the learned classifier, as the remaining traffic data in each device is few after noise removal. Motivated by the observation that the data feature of the noisy labeled traffic data is clean and the underlying true distribution of the noisy labeled data is statistically close to the clean traffic data, we propose to utilize the noisy labeled data by normalizing it to be close to the clean traffic data distribution. Specifically, we first formulate a distributionally robust federated network traffic classifier learning problem (DR-NTC) to jointly take the normalized traffic data and clean data into training. Then we specify the normalization function under Wasserstein distance to transform the noisy labeled traffic data into a certified robust region around the clean data distribution, and we reformulate the DR-NTC problem into an equivalent DR-NTC-W problem. Finally, we design a robust federated network traffic classifier learning algorithm, RFNTC, to solve the DR-NTC-W problem. Theoretical analysis shows the robustness guarantee of RFNTC. We evaluate the algorithm by training classifiers on a real-world dataset. Our experimental results show that RFNTC significantly improves the accuracy of the learned classifier by up to 1.05 times.

High Definition (HD) maps have become core supporting components for autonomous driving. To date, their updates heavily depend on the vehicle fleets of the map vendors, which cannot scale and timely reflect the highly dynamic environment. To ensure the HD map quality, it is advocated social vehicles should be used. Nevertheless, there are privacy concerns and a lack of incentives for social vehicles to contribute data. In this paper, we leverage federated analytics (FA), a newly developed collaborative data analytics paradigm, where raw data are kept local and only the insights generated from local analytics are sent to a server for aggregation. We present a new Federated Analytics based HD map Updating model (FAUMap) to protect the privacy of social vehicles. To motivate social vehicles to contribute data and improve the HD map quality, we formulate an overlapping coalition formation game, OCFUMap, and develop an algorithm to find feasible coalitions. Simulations show that our approach can improve the quality of the updated HD map by 1.56 times. To study an end-to-end operation of the FAUMap model and OCFUMap game, we present a case of HD map updates of the Powell street in San Francisco using the autonomous driving simulator CarLA.

In emergency scenarios, strong mobility and serious interference cause unstable transmission of on-site information such as close-up photos and high resolution videos, which requires a robust temporary communication network. In this paper, we focus on a UAV-assisted wireless cooperative communication and coded caching network, where emergency command vehicles and a UAV serve as content providers (CPs) to cache and transmit coded fragments or complete files for rescuers regarded as content requesters (CRs). The delivery success probability and content hit ratio are theoretically derived by incorporating the physical connectivity and social relationship between CPs and CRs. Aiming at maximizing the overall content hit ratio, we propose a multiagent two-timescale deep reinforcement learning (MA2T-DRL) algorithm to jointly optimize the transmission power and caching strategies for CPs. Specifically, we develop a two tier deep-Q networks (DQNs) framework integrating a slow-timescale DQN (ST-DQN) and a fast-timescale DQN (FT-DQN) for caching decision-making and power decision-making respectively, and then the QMIX framework is leveraged to aggregate all the outputs from local ST-DQNs. Considering the cooperative characteristics of coded caching, we further propose a novel clustering method for CPs such that CPs in the same cluster have the same willingness to serve CRs, and each cluster is regarded as the agent for training which further reduces the aggregation scale of the mixing network. Simulation results show that the proposed MA2T-DRL algorithm is efficient in model training, and presents the advantages in performance and complexity compared with the single-agent centralized training and the multiagent independent distributed training.

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study agoal-directedclient selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively.

The advancements in information and communication technology have led to the emergence of innovative edge computing models that incorporate the computing power of vehicles into the energy sector. Electric vehicles (EVs), functioning as edge computing nodes, offer flexible computing offloading services for charging stations (CS). However, coordinating EV computing and charging should consider the interdependence with CS's specific computing requirements due to information asymmetry. Additionally, it is crucial to consider EV's charging demands and their social distance to computing tasks. In this context, it is natural to view EVs and CSs as self-interested prosumers who prioritize their individual utilities. To address the integration of strategic EV-CS interactions and uncertainties into the joint computing scheduling and energy trading, this paper proposes a parameterized deep Q-network-based reinforcement contract design framework, which employs a hybrid action space to design contracts that facilitate CSs in pairing computing tasks and charging resources with EVs. The objective is to incentivize EV participation and maximize long-term social welfare by incorporating incentive compatibility, individual rationality constraints, and capacity constraints into the contract design. Experimental results demonstrate that the proposed framework surpasses parameterized deep deterministic policy gradient-based and greedy-based contract designs, and achieves near-optimal solutions by solving deterministic optimizations.

With the fierce global competition on satellite networks, the building of satellite constellations grows explosively. Sharply increasing on-orbit data will face the challenge of satellite-ground data transmission. GEO satellites become the top choice for satellite data relay due to their stable satellite-ground link. Most existing spectrum resource management for GEO relays is equipment-oriented and benefit priority, which may lead to a waste of spectrum resources. In this paper, we propose a real-time task-oriented resource allocation strategy for GEO relay systems. We model the spectrum allocation problem as a distributed non-cooperative Stackelberg game process. We prove that when both sides of the game pursue the maximization of personal revenue, the system will enter a Nash equilibrium state, whereas spectrum resources are not fully used. Based on the maximization of individual utilities (U-prior) and spectrum utilization (S-prior) methods, we design an adaptive dual-mode pricing mode to maximize the spectrum resources within a certain loss of revenue. The simulation results show that the S-prior and U-prior have better performance than the baseline method and existing optimization methods. Our proposed dual-mode strategy is making more throughputs and has less delay with little loss of utility values than that of individual utility maximization.

By utilizing intelligent mobile terminals, mobile crowdsensing (MCS) can realize the sensing data collection effectively and economically. However, the privacy security and freshness quality of the obtained sensing data are two major concerns to be addressed in MCS, as they directly impact the system security and timeliness performance. In this regard, we focus on improving the data freshness performance and protecting sensing data content, sensing terminals' identification, and location information simultaneously. Accordingly, based on the artificial noise (AN)-based differential privacy and covert communication technologies, we aim to jointly minimize the Age of Information (AoI) metric and weighted privacy preservation budget in the single terminal scenario. Besides, we achieve the goal of average AoI optimization with data computing requirements in multiple terminal systems, where the privacy preservation budget is treated as the critical constraint. Furthermore, by using the backward induction (BI) method and block successive upper-bound minimization (BSUM) approach, we solve the above two optimization problems, respectively. Finally, compared with the listed baselines, the results evaluate the proposed schemes' effectiveness under various simulation settings.

With the fast development of multi-agent reinforcement learning, communication among agents has become a new research hotspot for its significant role in promoting the cooperation of automated devices. However, in real-world scenarios, agents such as unmanned vehicles and robots are likely to suffer from communication resource constraints, making designing efficient communication protocols essential. In this paper, we propose to quantize messages and reduce discrete entropy to achieve effective multi-agent communication under bandwidth limits. Achieving this goal requires solving two challenges: The first one is that the gradients of discrete entropy remain zero except for several discontinuous points wherein the gradients are undefined, making it hard to reduce discrete entropy via gradient-based training. To overcome it, we design Surrogate Entropy Minimization (SEM) scheme and confirm its effectiveness theoretically. The second challenge is maximizing cooperation performance under a given bandwidth limit. We model it as a constrained optimization problem and design Soft Barrier Method (SBM). Our proposed scheme is evaluated alongside four other methods in six environment settings and five different bandwidth limits, and demonstrates outstanding performance. Specifically, it manages to reduce bandwidth consumption by up to 90% with little or no loss of cooperation performance.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.

The advancement of satellite-based quantum networks shows promise in transforming global communication infrastructure by establishing a secure and reliable quantum Internet. These networks use optical signals from satellites to ground stations to distribute high-fidelity quantum entanglements over long distances, overcoming the limitations of traditional terrestrial systems. However, the complexity of satellite-based entanglement distribution and terrestrial quantum swapping in the integrated network requires joint optimization with satellite assignment, resource allocation, and path selection. To address this challenge, we introduce a hybrid quantum-classical algorithm to solve the optimization problem by leveraging the strengths of both quantum and classical computing. The original problem is decomposed into a master problem and several subproblems using Dantzig-Wolfe decomposition and linearization techniques. Through experiments, this study demonstrates the effectiveness and reliability of the proposed methods in optimizing large-scale networks and managing qubit usage compared to the classical optimization techniques. The findings provide valuable insights for designing and implementing satellite-based entanglement distribution in quantum networks, paving the way for a secure global quantum communication infrastructure.

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

Intelligent reflecting surface (IRS) alters the wireless channel by dynamically adjusting the propagation of wireless signals, thereby having the ability to enhance communication performance. Due to its low power consumption, IRS is expected to play an important role in improving energy efficiency (EE). In this paper, both optical IRS (OIRS) and radio frequency (RF) IRS are employed to assist aggregated visible light communication (VLC)-RF systems, and then a resource allocation algorithm is proposed to improve EE. To this end, a model for the IRS-assisted aggregated VLC-RF system is first established, followed by the formulated EE maximization problem. Furthermore, the EE maximization problem is decomposed into four subproblems, focusing on RF IRS configuration, optical subchannel assignment, OIRS arrangement, and joint power allocation. By employing block coordinate descent (BCD), the four subproblems are solved iteratively. Moreover, simulation results show the significant EE improvement brought by the IRS for aggregated VLC-RF systems. In addition, the convergence and effectiveness of the proposed BCD-based resource allocation algorithm, as well as the influence of key system parameters on EE are also demonstrated.

Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

To satisfy the requirements of content distribution in computation-intensive and delay-sensitive services, this paper presents a novel joint task offloading and content caching (JTOCC) scheme in multi-cell multi-carrier non-orthogonal multiple-access (MCMC-NOMA)-assisted cloud-edge-terminal cooperation networks. Based on queuing theory, we formulate a delay minimization model that aggregates users’ requests to reduce repeated content delivery. To minimize network latency, the model is decomposed into three subproblems: task offloading, user clustering and communication resource allocation, and cache state updating. In each slot, the task offloading subproblem is solved utilizing deep reinforcement learning (DRL) under a resource-constrained cloud-edge-terminal setting. During a transition between slots, mobile terminals are grouped using K-means-based user clustering, and the allocations of the subchannels and transmit power are optimized utilizing matching theory and successive convex approximation (SCA), respectively. Contents cached at the network nodes are updated, according to long-short-term memory (LSTM)-based predicted popularity. Simulations show that the proposed JTOCC model achieves lower-delay content distribution than its existing counterparts in cloud-edge-terminal cooperation environments, and converges fast in heterogeneous networks.

Driven by the widespread applications of Space-Air-Ground Integrated Networks (SAGINs) in a number of practical fields, the volume of space data grows rapidly. However, the large volume of space data in SAGINs is typically intractable to be offloaded from space to the ground under the high dynamic network topology and the stochastic data arrivals. Furthermore, most nodes in SAGINs are battery-powered and energy-constrained, thereby implying that energy consumption becomes one major bottleneck for data offloading. Towards this end, this paper studies online joint data offloading and power control in SAGINs to maximize long-term time-averaged data offloaded amount under the constraints of average energy consumption. First, we propose a novelty Two-timescale Time-Expanded Graph (TTEG) to characterize the rapid change of the network topology in large-timescale slots and capture the stochastic data arrivals in small-timescale slots. Based the TTEG model, we formulate a stochastic optimization problem and transform it into a series of per-time-slot subproblems to obtain an efficient online solution. Through theoretical analyses, we show that the performance gap with optimal solution is bounded. Finally, extensive simulations demonstrate that the maximum performance gap of our proposed online solution to the optimal solution is less than 2% in a low computation cost.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution-linked signal transformer (ClST) and a novel knowledge distillation method named signal knowledge distillation (SKD). The ClST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel attention mechanism named parallel spatial-channel attention (PSCA) mechanism and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The SKD is a knowledge distillation method to effectively reduce the parameters and complexity of neural networks. We train two lightweight neural networks using the SKD algorithm, KD-CNN and KD-MobileNet, to meet the demand that neural networks can be used on miniaturized devices. The simulation results demonstrate that the ClST outperforms advanced neural networks on all datasets. Moreover, both KD-CNN and KD-MobileNet obtain higher recognition accuracy with less network complexity, which is very beneficial for the deployment of AMR on miniaturized communication devices.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, but also pose a huge risk for physical layer security. Existing works have illustrated that a disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties (like a “disco ball”), can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS can be employed to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by DIRS-based fully-passive jammers (FPJs), an anti-jamming precoder is proposed that requires only the statistical characteristics of the DIRS-based ACA channels instead of their CSI. The statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is derived based on the statistical characteristics. Furthermore, we prove that the anti-jamming precoder can achieve the maximum signal-to-jamming-plus-noise ratio (SJNR). To acquire the ACA statistics without changing the system architecture or cooperating with the illegitimate DIRS, we design a data frame structure that the legitimate access point (AP) can use to estimate the statistical characteristics. During the designed data frame, the LUs only need to feed back their received power to the legitimate AP when they detect jamming attacks. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJs and the feasibility of the designed data frame used by the legitimate AP to estimate the statistical characteristics.

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge arising from scarce wireless communication resources, we propose a semantic-aware multi-modal task offloading system that facilitates the extraction and offloading of semantic task information to edge servers. To cope with the different tasks with multi-modal data, a unified quality of experience (QoE) criterion is designed. Furthermore, a proximal policy optimization-based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource management for wireless communications and computation in a distributed and low computational complexity manner. Simulation results verify that the proposed MAPPO algorithm outperforms other reinforcement learning algorithms and fixed schemes in terms of task execution speed and the overall system QoE.

To deal with the challenges in mobility management of mega low-Earth-orbit (LEO) satellite constellations with long management delays and high signaling overheads, especially under the existing fixed and limited deployments of ground mobility management entities, the cooperative mobility management mode of medium-Earth-orbit (MEO) satellites and ground stations (GSs) has become an attractive tendency. In this paper, considering with the global non-uniform user distribution and constrained satellite storage resources, we propose a dynamic satellite-ground integrated mobility management strategy (DSG-MMS) to cope with the relative mobility among users, GSs, and satellites, which can dynamically decide the optimal GS/MEO management node with the minimal handover and migration delays. Specifically, the DSG-MMS optimization problem is modeled as distributed Markov decision processes, and a reinforcement learning (RL)-based management node selection method is presented to solve them, where each LEO satellite agent dynamically decides its own management node. To further implement the RL algorithm on the resource-limited LEO satellite agents, a novel tensor-based RL algorithm for DSG-MMS is proposed by means of the streamed low-rank tensor decomposition, where only the small-sized core tensor and factor matrices are kept and updated in the strategic optimization so as to realize low storage and computing overheads as well as fast convergence. We perform simulations for the proposed DSG-MMS with parameter configurations of actual Telesat, Kuiper, and Starlink satellite systems to evaluate the mobility management delay and overhead performances as well as the required satellite storage size for mobility management. Moreover, a case study and an architectural comparison are given to demonstrate the superiority of the proposed DSG-MMS than existing methods.

The development of Unmanned Aerial Vehicles (UAVs) offers new prospects for emerging applications in the Industrial Internet of Things (IIoT) networks. With the assistance of Digital Twin (DT), a real-time understanding of physical entities can be constructed for dynamic perception and decision-making. However, DT modeling requires distributed data aggregation, resulting in privacy disclosure and communication burden. Therefore, we propose the digital twin edge network by integrating the DT technology and edge computing, which leverages an over-the-air computation enabled federated learning architecture for an efficient and secure DT model construction. Specifically, we propose a heterogeneity-aware and energy-conscious device scheduling mechanism, considering the update importance, channel condition, and computation capacity based on a probabilistic scheduling framework. To enhance energy efficiency, we introduce a virtual queue to track the difference between the cumulative energy consumption and budget. Additionally, we design a low-complexity scheduling algorithm to solve the optimization problem. Simulation results validate the superiority of our proposed mechanism in improving the test accuracy and energy efficiency in a heterogeneous and energy-constrained environment. Moreover, the proposed mechanism demonstrates significant advantages when employed to highly heterogeneous datasets, and exhibits a certain level of robustness to mapping errors arising from the utilization of DT technique.

In this article, a joint design of communication sensing and control (JDCSC) scheme is developed which focuses on a scenario where a cellular-connected unmanned aerial vehicles (UAV) senses a moving target. The goal is to maximize the sensing mutual information via jointly optimizing the transmit power, the trajectory of the UAV and the task completion time, while meeting the onboard energy, the communication service quality, and the UAV flight safety constraints. In particular, UAV dynamics are considered, which are usually ignored in the existing design and inferior communication and sensing quality of service might be resulted. The formulated problem is dynamic optimization problem, which is difficult to be solved. The control parameterization method and exact penalty function scheme are utilized to transform the problem into a static nonlinear program which can be solved by gradient-based methods. The effectiveness of the JDCSC approach is verified by carrying out some numerical examples.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

With the 5G systems being highly developed, the urge of the next generation networks is increasingly necessary, which demands extremely high data rates and low latency. As an emerging technology capable of reflecting and refracting the incident signals on both sides simultaneously, recently the intelligent omnidirectional surface (IOS) has been used to enhance the capacity of wireless networks. However, it is challenging to design an IOS-enabled beamforming scheme that can respond quickly in a varying mobile environment due to its high complexity. In this paper, we aim to maximize the sum rate in an IOS-aided multi-user system given dynamically changing channel states and user mobility. A novel meta-critic reinforcement learning framework named meta-critic deep deterministic policy gradient algorithm is proposed to design the IOS-enabled beamforming scheme. We propose a meta-critic network that can recognize the environment change and automatically perform the self-renewal of the learning model. A stochastic explore-and-reload procedure is also tailored to reduce the high-dimensional action space problem. Simulation results demonstrate that our proposed method outperforms other benchmarks including the state-of-the-art reinforcement learning method in both achievable sum rate and convergence speed.

By creating multipath backscatter links and amplify signal strength, reconfigurable intelligent surfaces (RIS) and decode-and-forward (DF) relaying are shown to degrade the latency of the ultrareliable low-latency communications (URLLCs) and enhanced mobile broadband (eMBB) multiplexing system. This study investigates the delay and backlog violation behavior of URLLCs and eMBB multiplexing systems supported by different technologies, e.g. RIS and DF relay, for different scheduling policies of static priority, nonpreemption, and earliest deadline first. A tight analysis approach based on the Martingale theory was proposed to evaluate the serviceability of URLLC and eMBB multiplexing systems. On this basis, the Martingale theory analyzes the delay and backlog bounds by transforming the arrival and service processes into exponential forms of the moment generating function. Furthermore, this study derives the closed-form expression of delay and backlog bound for the URLLCs and eMBB multiplexing in two-hop heterogeneous communication networks. Numerical results demonstrate that the proposed Martingale-based tightly analytical method outperforms the state-of-the-art classic stochastic network calculus for evaluating delay and backlog violation in URLLC and eMBB multiplexing systems.

Unmanned aerial vehicles (UAVs) are viewed as a key component of 5G, 6G and beyond wireless networks to receive, store and forward information. Benefiting from swift deployment, low cost and high mobility, it has become a promising trend to leverage the UAVs to assist the covert communicator (Alice) against the detector’s (Willie’s) detection in a covert communication network. Nevertheless, once Willie notices the covert transmission process, the covert information is exposed to the risk of being cracked. In this paper, we propose a UAV-assisted covert communication asymmetric information game model (UCCAIG) that focuses on the covert communication countermeasure process based on information asymmetry. We introduce the UAV into covert communication relay assistant Alice and interference assistant Willie respectively, analyze the interaction between Alice and Willie, and derive the optimal strategies for both sides. We further prove the existence of a Bayesian Nash equilibrium (BNE) in the UCCAIG. In addition, we evaluate our proposals on a covert communication testbed with software radio, and the numerical results show that our proposed strategy increases the payoffs of Alice, reduces the payoffs of Willie in a variety of scenarios, and improves the covert communication rate compared with UAV-relayed game (URG).

Preemptive scheduling efficiently addresses the coexistence of enhanced Mobile Broad Band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC). While URLLC puncturing influences eMBB performance, further investigation is necessary to study the trade-offs between stability, delay, and efficiency. However, existing studies overlook the imbalance in eMBB/URLLC load distribution and personalized fluctuations in eMBB performance, leading to sub-optimal results. To tackle this, we propose an unmanned aerial vehicle (UAV) relay-assisted eMBB/URLLC multiplexing framework. Specifically, considering the utilization of UAVs for connecting separated next-generation Node Bs (gNBs) and the individual subject experience of services, we first formulate the multiplexing problem as an optimization problem. The objective is to maximize eMBB throughput and minimize personalized fluctuations in eMBB performance and UAV consumption, subject to URLLC constraints. Then, the challenging problem is decomposed into the eMBB problem and the URLLC problem. For the former, we further decompose it into three sub-problems and solve them using optimization methods. For the latter, we propose a deep reinforcement learning-based algorithm to obtain an optimal strategy for relaying and puncturing URLLC into eMBB intelligently. Simulation results demonstrate that our proposals outperform benchmark schemes regarding eMBB throughput, UAV consumption, eMBB performance fluctuation, URLLC satisfaction, and learning efficiency.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

In large-scale multi-access edge computing (MEC) networks, each device should make the computation offloading decision distributively. In this paper, we target on a delay-optimal computation offloading problem in large-scale MEC systems, where each task has two properties: data size and computation amount. Because the detailed state information of massive devices are huge in large-scale systems, we propose a distributed computation offloading algorithm using the mean field game (MFG). To design the distributed computation offloading algorithm, we first formulate the delay-optimal computation offloading problem as a Markov decision process (MDP) and derive the Hamilton-Jaccobi-Bellman (HJB) equation with the unknown task allocation proportion, where the combined influence from other devices and MEC servers should be estimated. Based on MFG, we obtain the Fokker-Planck-Kolmogorov (FPK) equation to describe the evolution of the system’s collective behavior, with the influence from other devices and MEC servers formulated as the mean field. To solve the large-scale problem with the unknown allocation proportion, we propose a optimal computation offloading algorithm based on the generative adversarial networks (GAN) structure. For the generator, we generate the unknown task allocation proportion due to its non-calculability and insufficient dataset. For the discriminator, we train the value function, and propose a water-filling algorithm to prioritize the task offloading. Finally, the simulation results evaluate the performance of the proposed algorithm and show the performance gain compared to conventional algorithms.

The scheduling of the laser inter-satellite links (LISLs) can effectively decrease the energy consumption by deactivating idle LISLs and reduce the network latency by decreasing the average hop count through establishing appropriate dynamic LISLs, but is challenging in mega-constellations due to a large amount of satellites involved. In this paper, a federated multi-agent deep reinforcement learning (MADRL) method for LISL scheduling is proposed, where the global scheduling is decomposed into independent decisions for each satellite. To reduce the substantial communication overhead attributable to MADRL, asynchronous hierarchical federated learning with partial and global aggregations is utilized, transmitting the model parameters rather than status information among low Earth orbit (LEO) satellites and geostationary Earth orbit (GEO) satellites. The movement of LEO satellites maintains the consistency of local sample distribution and the indirect association among different GEO satellites. Therefore, the global aggregation with infrequent occurrence is asynchronous with the partial aggregation. Moreover, during the partial aggregation, the utility of each layer in the local model of the LEO satellite is also evaluated to minimize the uncertainty, allowing the adaptive upload to ensure efficiency and fairness within a limited power budget. The simulation results show that the proposed method reduces the average hop by about two hops and decreases the LISLs number by over 25%. Meanwhile, the communication overhead of LEO satellites is 46.6% of that in the centralized approach, with the training performance ensured.

The massive multi-input multi-output (mMIMO) systems are vulnerable to both pilot contamination and pilot spoofing attack (PSA), which jeopardize the uplink channel estimation and cause information leakage in the downlink transmissions. Motivated by the canonical large-scale fading precoding (LSFP) [1], a secure LSFP is proposed to eliminate the impact of the coexistence of pilot contamination and PSA. The proposed framework enables multi-cell coordinated transmissions leveraging the large-scale fading coefficients, thus is suitable to be implemented in mMIMO systems with limited fronthaul. Specifically, the presence of the eavesdropper is first identified by designing a distributed mixture-of-experts neural network (D-MoENN)-based PSA detector, which combines the detected results of distributed nodes to improve the detection accuracy. Subsequently, an optimal jamming base station (BS) is selected by designing a D-MoENN-based localizer, which estimates the locating cell of the eavesdropper and selects the nearest jamming BS to the eavesdropper. Numerical results show that the proposed D-MoENN-based PSA detector outperforms the existing detectors in the low SNR regime. Moreover, the average secrecy rate achieved by the secure LSFP with the jamming BS selected by the D-MoENN-based localizer is close to the upper bound achieved by the genie-aided selector that perfectly knows the location of the eavesdropper.

The existing works on ultraviolet (UV) channel modeling primarily focus on non-line-of-sight (NLoS) communication scenarios, where the UV transceiver does not need to be aligned and can communicate around obstacles. However, NLoS scenarios also face problems such as long channel delay spread and severe path loss, and consequently, these phenomena will be exacerbated as the amount and dimension of obstacles increase. To tackle these problems, we investigate the channel models for UV diffused line-of-sight (LoS) communication scenarios comprehensively. First, a UV diffused-LoS channel model with an obstacle is put forward, where the radiation intensity distributions of UV light sources, the height difference between UV transceivers, as well as the obstacle dimension and orientation are incorporated to approach practical application scenarios. Besides, the channel modeling framework for diffused-LoS scenarios incorporating obstacles is investigated, where we take two obstacles as an example to illustrate the entire modeling process. Further, we validate the proposed models by comparing them with associated LoS and Monte-Carlo photon-tracing (MCPT) models via numerical calculations. The path loss results manifest that the proposed integration models agree well with the existing channel models, while their calculation time is much shorter than that of the MCPT model. Apart from that, the channel path loss and bit-error rate performance of diffused-LoS scenarios are superior to those of NLoS scenarios when obstacle reflection is apparent, and channel delay spreads of diffused-LoS scenarios are shorter than those of NLoS scenarios regardless of circumstances with one or two obstacles.

With the rapid development of maritime activities, efficient and reliable maritime communications have attracted ever-increasing attention, and mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, can provide flexible and adaptable services for maritime communications. In this paper, we investigate a UAV-RIS-assisted maritime communication system under a malicious jammer, where a UAV-RIS is deployed to jointly adjust its placement and RIS surface elements to maximize the system energy efficiency (EE) and guarantee quality of service requirements against jamming attacks. In addition, an adaptive energy harvesting scheme is developed for information transmission (IT) and energy harvesting (EH) simultaneously to enhance the endurance of the UAV by deploying different IT times for each RIS element. Considering the non-convex optimization problem and highly complex maritime environments, an intelligent resource management approach based on deep reinforcement learning is proposed to jointly optimize the base station’s transmit power, placement of UAV-RIS, and RISs reflecting beamforming. Furthermore, hindsight experience replay is adopted to improve the learning efficiency and performance. The simulation results demonstrate that the proposed approach achieves the better EE and EH performances under different real-world settings compared with existing popular approaches.

In developing the sixth-generation (6G) system, integrated sensing and communication technology is becoming increasingly essential, especially for applications like autonomous driving. This paper develops an architecture for integrated sensing, communication, and computation (ISCC) in the vehicular network, where vehicles perform environment sensing, sensing data computation, and transmission. To support low-latency cooperation between vehicles and extend vehicles’ sensing range, over-air-computation federated learning is employed. The optimization problem of joint beamforming design and power resource allocation in the ISCC scenario is formulated to maximize the achievable data rate while ensuring sensing and computing performance. However, solving this joint optimization problem is a great challenge due to the high coupling resource and time-varying channel environment. Therefore, a hybrid reinforcement learning scheme is proposed in this work. First, the semidefinite relaxation and Gaussian randomization techniques are leveraged to obtain the approximate solution of the aggregation beamformer. Then, the deep deterministic policy gradient algorithm is proposed to tackle the transmit beamforming design and resource allocation problem in continuous action space. Extensive simulation results validated the admirable performance of the proposed scheme in convergence and achievable sum rate compared with the benchmark schemes. In addition, the impact of variables on the optimization performance is demonstrated via numerical results.

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.

Holographic multiple-input-multiple output (HMIMO) technology, which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this area focus on single-polarized RIS-enabled HMIMO, where the RIS can only reflect signals in one polarization towards users and signals in the other polarization cannot be received by intended users, leading to degraded data rate. To improve multiplexing performance, in this paper, we consider a dual-polarized RIS-enabled single-user HMIMO network, aiming to optimize power allocations across polarizations and analyze corresponding maximum system capacity. However, due to interference between different polarizations, the dual-polarized system cannot be simply decomposed into two independent single-polarized ones. Therefore, existing methods developed for the single-polarized system cannot be directly applied, which makes the optimization and analysis of the dual-polarized system challenging. To cope with this issue, we derive an asymptotically tight upper bound on the ergodic capacity, based on which the power allocations across two polarizations are optimized. Potential gains achievable with such dual-polarized RIS are analyzed. Numerical results verify our analysis.

To meet the growing demand for joint data and energy transmission, research on wireless information and power transfer is being promoted. The resonant beam enabled information and power transfer (RBIPT), which supports long-distance, high-power, and wide-bandwidth information and power transfer, has sparked widespread interest. The point-to-multipoint RBIPT system shows great promise for enabling simultaneous RBIPT for multiple receivers. However, the enabling system architecture has not been well studied in the literature, which is holding back the system implementation. To solve this problem, we propose a time division multiplexing RBIPT (TDM-RBIPT) system for multiple access, and constract a novel metric to evaluate the information and power transfer performance. We explore the TDM-RBIPT mechanism and design the architectures of the transmitter and the receiver. For the information transfer performance evaluation, we take system latency and throughput into consideration. We propose to estimate the system delay with the martingale theory by modeling the dynamic data processing procedures as Markovian processes with the markov chain monte carlo (MCMC) method. To evaluate the power transmission performance, we consider the transmitter’s power costs and the receivers’ power benefits. Numerical results reveal the effectiveness of the proposed TDM-RBIPT system and validate the accuracy of the proposed metric.

This paper considers reconfigurable intelligent surface (RIS)-enabled federated learning (FL) system, where the FL users communicate with the access point (AP) via RIS. To reveal the impact of RIS and learning rate on FL aggregation, the theoretical result of minimum global communication rounds and local iteration rounds are derived. Based on the obtained convergence results of FL, we formulate an optimization problem to minimize the energy consumption of the proposed RIS-assisted FL system by jointly optimizing the passive beamforming of RIS, the CPU computing frequency, the bandwidth, and the transmit power of users. To solve the non-convex problem, we propose a block coordinate descent (BCD) optimization algorithm based on successive convex approximation (SCA) to decompose the original problem into four sub-problems. Specifically, the closed-form solutions are derived for the CPU frequency, RIS reflection matrix, and communication bandwidth. For the transmit power sub-problem, we propose a linear approximation algorithm based on the first-order Taylor expansion to ensure solution accuracy. Finally, simulation results show that: 1) the energy consumption of the proposed RIS-assisted FL system can be greatly reduced compared to that without optimizing the passive beamforming of RIS and the transmit power; 2) The learning performance of the proposed RIS-enabled FL system is closed to the FL without wireless communication interference; and 3) The proposed algorithm can not only significantly reduce energy consumption, but also fast convergence in terms of the FL model training and testing.

Reconfigurable metasurfaces integrating numerous elements are one promising solution for empowering high-accuracy positioning applications, benefiting from their high spatial resolution, low power consumption, and low cost. In this paper, we investigate two typical types of metasurfaces, i.e., reconfigurable holographic surfaces (RHSs) and reconfigurable intelligent surfaces (RISs), for target detection and positioning. Specifically, an RHS is a leaky-wave surface antenna with an embedded feed, while an RIS is a type of reflective metasurface whose feed is positioned outside the surface. Due to their distinct structures and working principles, RHSs and RISs may be suitable for different scenarios for target detection and positioning. To determine their best working scenarios, we first design the beamformers of both RIS-enabled and RHS-enabled radar systems to improve their performance. We then characterize the target detection and positioning performance analytically, and finally compare their performance in different scenarios. Theoretical and numerical results both reveal that: 1) in the one-dimensional linear array case, in general the performance of the RHS-enabled system is better than that of the RIS-enabled system; 2) in the two-dimensional planar array case, lower frequencies and larger physical sizes can contribute to a better performance of RIS-enabled systems than RHS-enabled systems, and vice versa.

With the rapid development of intelligent vehicles, heterogeneous in-vehicle networks (HetIVNets) applying heterogeneous access technologies in terms of cellular and in-vehicle WiFi, are widely employed to provide stable and ubiquitous network environments for intelligent vehicles and their passengers. Most existing studies on the optimization of heterogeneous networks (HetNets) focus on user association, channel and power allocation. Additionally, these studies typically employ a single task metric to characterize the quality of service (QoS) requirements of the devices. In order to achieve green and energy-efficient intelligent vehicles, our work considers a HetIVNet composed of WiFi and cellular networks, and further optimizes the bandwidth allocation and energy consumption of WiFi access point (AP). Furthermore, to achieve more accurate resource allocation for different tasks in HetIVNet, we establish the QoS requirement model of various tasks for in-vehicle devices. Since the proposed optimization problem is non-convex and NP-hard, we formulate the objectives and constraints of user association and resource allocation (UARA) in HetIVNets as a markov decision process (MDP) and propose a proximal policy optimization (PPO) algorithm for in-vehicle intelligent resource allocation to uniformly schedule network resources. Simulation results validate that the proposed algorithm can achieve high task success rates under low-energy consumption conditions for WiFi AP. We also compare our algorithm with the state-of-art baselines to highlight its efficiency and stability.

At present, in order to make use of sufficient spectrum resources to provide even better quality of service and with the development of millimeter wave (mm-wave) communications technology, high speed railway (HSR) communication systems have also taken mm-wave frequency band into consideration. However, since the train runs with high speed as well as the operating environment is complex and dynamic, which may cause the communication link blockage issue for a while. To solve the problem, we adopt the emerging innovative intelligent reflecting surface (IRS) technology to enhance the robustness of mm-wave HSR communication system by introducing a reflection link. Therefore, when the direct communication link is blocked in some case, the reflection link can ensure that the communication is not interrupted. In this paper, we focus on maximizing the number of flows meeting their QoS requirements. A robust IRS-assisted scheduling scheme is proposed under the constraints of half duplex transmission, transmit power, IRS phase shift and limited time slots. As the formulated problem is non-convex, it is difficult to solve directly. Therefore, we divide it into four subproblems, and utilize alternative optimization method to get a sub-optimal solution. The simulation results show that compared with the other three baseline schemes, the IRS-assisted transmission scheduling algorithm proposed in this paper can improve the system performance effectively.

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments is critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a novel neural network named convolution signal transformer (CST). The CST is accomplished through three primary modifications: a hierarchy of transformer containing convolution, a novel signal-specific self-attention mechanism to replace the multi-headed self-attention mechanism in Transformer, and a novel convolutional transformer block named convolution-transformer projection (CTP) to leverage a convolutional projection. The simulation results demonstrate that the CST outperforms advanced neural networks on all datasets, which is very beneficial for the deployment of AMR in complicated channel environments.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security. A disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties, can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS-based fully-passive jammer (FPJ) can be applied to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by the DIRS-based FPJ, an anti-jamming strategy is proposed that requires only the statistical characteristics of DIRS-jammed channels instead of their CSI. Statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is given based on the derived statistical characteristics. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJ.

In various cyber physical systems (CPSs), communications and sensing are conducted simultaneously. Therefore, the mechanism of joint communications and sensing (JCS) is envisioned to integrate both functions in the same waveform, frequency band and hardware. It is expected to be one of the major features of 6G wireless communication networks. A major challenge to the design and analysis of JCS is a unified framework that incorporates the distinct functions of communications and sensing. In the first par of this paper, the framework of broadcast channel that has been intensively studied in data communications and information theory is adopted for JCS, in which communication and sensing signals are broadcast to the concrete communication users and virtual sensing users. Such a broadcast channel framework benefits the applications of existing multiplexing schemes, such as dirty paper coding (DPC) or frequency division multiplexing (FDM). Based on the framework, the feasible performance region bound is derived, based on the broadcast-multiaccess duality. The design of dedicated sensing signal is studied for the scenarios of communication-first (or sensing-first) priority, based on the ambiguity function (AF) of radar sensing. The proposed scheme is numerically demonstrated using typical short-range communication and sensing setups. The scheme based on superposition coding will be discussed in the second part of this paper.

The technology of joint communications and sensing (JCS) integrates both functions in the same waveform and thus the same frequency band. It is expected to be a distinguishing feature in 6G wireless networks. A major challenge to JCS is how to seamlessly integrate the two historically distinct functions of communications and radar sensing. In the second part of this paper, a framework of superposition coding, motivated by the similarity to broadcast channels, is proposed for the functional multiplexing in JCS, which is motivated by the studies on broadcast channels in data communications. In this framework, communications and sensing are considered as genuine and virtual users, respectively. Sensing is considered as the bottom user in the layered structure of superposition coding; thus a sensing waveform is generated according to a certain criterion of sensing, which plays the role of cloud in superposition coding. Then, the communication message is superimposed on top of the cloud. Different superposition schemes are proposed, each corresponding to one type of mathematical operation on vectors in linear spaces. Moreover, the waveform diversity recently proposed in the radar community, which prepares a set of waveforms for handling the variance of environment, is taken into account. The cases of sensing waveform known/unknown to the communication receiver are discussed. The performance of the proposed JCS schemes is demonstrated using numerical simulations.

Laser inter-satellite links (LISLs) enable longer-range communication and cross-satellite dynamic links that bypass intermediate satellites. However, the utilization of narrow laser beams necessitates closed-loop control for alignment and consumes substantial energy even during idle periods. Therefore, we propose a dynamic LISL scheduling algorithm and a satellite link pattern with one dynamic LISL and three fixed LISLs to optimize energy consumption and reduce communication delay. To simplify the computation complexity of the optimization problem, a Markov decision process (MDP) is constructed, and the problem is divided into the independent decision process of each agent by breaking down the state space, action space, and reward function. Experimental results indicate that the proposed method reduces communication delay by approximately 2 hops and saves over 15% of energy consumption compared to fixed link patterns.

With the rapid development of artificial general intelligence (AGI), various multimedia services based on pretrained foundation models (PFMs) need to be effectively deployed. With edge servers that have cloud-level computing power, edge intelligence can extend the capabilities of AGI to mobile edge networks. However, compared with cloud data centers, resource-limited edge servers can only cache and execute a small number of PFMs, which typically consist of billions of parameters and require intensive computing power and GPU memory during inference. To address this challenge, in this paper, we propose a joint foundation model caching and inference framework that aims to balance the tradeoff among inference latency, accuracy, and resource consumption by managing cached PFMs and user requests efficiently during the provisioning of generative AI services. Specifically, considering the in-context learning ability of PFMs, a new metric named the Age of Context (AoC), is proposed to model the freshness and relevance between examples in past demonstrations and current service requests. Based on the AoC, we propose a least context caching algorithm to manage cached PFMs at edge servers with historical prompts and inference results. The numerical results demonstrate that the proposed algorithm can reduce system costs compared with existing baselines by effectively utilizing contextual information.

High-definition (HD) map is an essential building block in the autonomous driving era, which enables fine-grained environmental awareness, exact localization, and route planning. However, because HD maps include rich, multidimensional information, the volume of HD map data is enormous, making it expensive and time-consuming to transmit on vehicular networks. Therefore, in this paper, we propose a data optimization scheme for effective HD map updates in vehicular named data networking (NDN) scenarios. We formulate the HD map data optimization problem as a convex optimization problem and solve it with modified convolutional neural networks (CNNs) from YOLOX's real-time object detection system. Specifically, we modify the YOLOX object detection algorithm to detect and compress redundant pixels in local map data before transmission to the MEC server. To deploy our proposed scheme, we construct a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Extensive simulations show that our proposed scheme can significantly reduce the transmission data size and time by 48.25% - 65.78% and 46.85% - 78.84% compared with state-of-the-art HD map update techniques like RLSS, Pro-RTT, and Loss-based systems.

In recent years, autonomous underwater vehicles (AUVs) have been widely applied to collect data in underwater acoustic sensor networks (UWASNs). Limited by the capacity of a single AUV, as well as the low-latency requirement of data collection, the intelligent swarm consisting of multiple AUVs is expected to execute the secure and efficient data collection tasks in a cooperative manner. However, most of the existing works assumed that the locations of sensor nodes are already known, which is impractical in a real AUV network. In addition, the security issues are not well considered in underwater searching and transmission tasks. To improve the searching efficiency in an unknown underwater area where locations of sensor nodes cannot be obtained precisely, this work proposes a data collection scheme via a target uncertainty map based multi-agent reinforcement learning algorithm for AUV swarms. Specifically, the target uncertainty map is established based on the current and past searching and collection results, which can guide the AUV swarm to search the areas with higher probabilities to find sensor nodes waiting for data collection. Moreover, to mitigate the potential security risk of data leakage, we design a multi-agent deep deterministic policy gradient (MADDPG) algorithm for each AUV in the swarm to make its searching and data collection strategies through a manner of centralized training with distributed execution. Simulation results validate that the proposed scheme can achieve a high collection rate with low energy consumption. In addition, the security referring to data protection can be also guaranteed in AUV swarms.

Due to the ability to provide low latency, high dependability, and worldwide broadband coverage services, the development of integrated satellite-terrestrial networks has attracted significant interest from both industry and academia over the past few decades. However, the dynamic satellite topology, heterogeneous, expansive, and intricate properties of the integrated satellite-terrestrial network make routing tasks difficult. In this research, we design the distributionally robust optimization (DRO) model with the objective of minimizing the estimated worst-case total task routing delay from the source mobile devices to the matching target mobile devices under an uncertain probability distribution. Taking into account the unpredictable vehicle movement and discontinuous connection between vehicles and mobile devices, the indeterminate offloading and downloading from automobiles to satellites and mobile devices, respectively, are captured by the Wasserstein ambiguity set. Then, we are able to determine the optimal route for task uploading, routing throughout the satellite constellation, and downloading. Finally, experimental results demonstrate that our proposed model has a lower and more robust latency than that of robust optimization (RO) strategy.

This paper proposes to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users collaboratively in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channel at different locations. Based on the proposed channel model, we formulate a joint design problem of UAV three-dimensional (3-D) positioning and resource allocation, including the user association and subcarrier allocation, to maximize the minimum achievable rate among users. We propose a suboptimal iterative algorithm to solve the mixed-integer non-convex optimization problem. Specifically, the UAV positioning and resource allocation are alternately optimized in each iteration by employing the successive convex approximation (SCA) and matching theory, respectively. Simulation results reveal that the proposed algorithm outperforms several benchmark schemes in terms of the minimum achievable rate.

The scheduling problem for federated learning (FL) with multiple models in a distributed network is challenging, as it involves NP-hard mixed-integer nonlinear programming. Moreover, it requires optimal participant selection and learning rate determination among multiple FL models to avoid high training costs and resource competition. To overcome those chal-lenges, in literature the Benders' decomposition algorithm (BD) can deal with mixed integer problems, however, it still suffers from limited scalability. To address this issue, in this paper, we present the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm, which combines the power of quantum and classical computing to solve the joint participant selection and learning scheduling problem in multi-model FL. HQCBD decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables. This collaboration maximizes the potential of both quantum and classical computing, and optimizes the complex joint optimization problem. Simulation on the commercial D-Wave quantum annealing machine demonstrates the effectiveness and robustness of the proposed method, with up to 18% improvement of iterations and 81% improvement of computation time over BD algorithm on classical CPUs even at small scales.

Integrated sensing and communications (ISAC) is a promising technology to integrate both functions in the same waveform, and is expected to be a feature in 6G wireless communication networks. One effective approach to ISAC is leveraging existing communication signals (such as orthogonal frequency division multiplexing (OFDM) signals) to sense the environment. Since there are many base stations in densely deployed cellular networks, they can form a massive sensing network, in which some transmit for illumination while others receive and collect data for imaging the illuminated area. In this paper, such imaging algorithms are discussed. Further, the critical challenge of time synchronization errors is addressed by using the technique of autofocus. The performance conflict and corresponding trade-off between communications and sensing in ISAC are discussed qualitatively.

In this paper, a client-edge-cloud hierarchical federated learning (FL) model has been developed for connected vehicle applications. Generalized models are aggregated on the cloud server, while customized models trained on local data with similar data distribution are aggregated on the edge server, which mitigates the impact of data heterogeneity. To reduce the communication overhead of FL, clients will periodically update to the edge server and edge servers will periodically update to the cloud server. Moreover, we propose a mean field game-based probabilistic device selection scheme. Jointly considering their contributions and the population diversity, a fraction of devices will be selected to join the FL iteration. Taking driving range estimation as an example of connected vehicle applications in the experiment, we have shown that the proposed FL frameworks can increase the prediction accuracy by 28.9% with 5 times fewer clients’ participation, compared with the vanilla FL.

The significant growth in data consumption among mobile users necessitates the development of new architecture to meet the increasing demand. On the other hand, reconfigurable intelligent surface (RIS) has grown in popularity in 6G due to its improved spectral efficiency, simplicity of deployment, and low cost. However, with the constrained limitation of the coverage by conventional RIS, the research direction has turned towards simultaneously transmitting and reflecting RIS (STAR-RIS) to provide 360° coverage alongside the benefits of RIS. In this paper, a STAR-RIS-assisted downlink communication system for both indoor and outdoor users is investigated. Then, the optimization problem to maximize the spectral efficiency while jointly controlling the beamforming power for each user and phase shift values of the STAR-RIS is formulated. Since the formulated problem is NP-hard and challenging to solve in polynomial time, a policy gradient method for reinforcement learning named proximal policy optimization (PPO) is implemented to solve the problem. To demonstrate the effectiveness of our proposed algorithm, extensive simulation results are executed. Numerical results prove that our proposed algorithm outperforms several benchmark schemes in the literature.

We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.