Federated fine-tuning of large language models (LLMs) provides a privacy-preserving approach to deploying pervasive generative AI services, yet the substantial memory overhead of first-order (FO) gradient computation presents significant practical challenges.While zeroth-order (ZO) optimization methods offer memory-efficient alternatives, they remain susceptible to performance degradation brought by data heterogeneity.Specifically, direct ZO-for-FO substitution is incompatible with existing strategies tailored for cross-client discrepancies.In response, we propose a new federated LLM fine-tuning framework, with a holistic revamped design of the entire ZO gradient processing pipeline.Crucially, with our proposed global adaptive optimization and local personalized perturbation, we present a unified solution for incorporating ZO gradients in federated learning, from local personalized perturbation sampling and ZO gradient transmission, to global ZO gradient reconstruction and aggregation with adaptive momentum, thereby directly addressing the challenges of inefficiencies and cross-client discrepancies.Our convergence analysis and experimental results demonstrate the superiority of our proposed framework over diverse heterogeneous data settings, both in terms of generalization and efficiency.

We introduce a new analytical framework, developed based on the spatial network calculus, for performance assessment of Low Earth Orbit (LEO) satellite networks. Specifically, we model the satellites' spatial positions as a strong ball-regulated point process on the sphere. Under this model, proximal points in space exhibit a locally repulsive property, reflecting the fact that intersatellite links are protected by a safety distance and would not be arbitrarily close. Subsequently, we derive analytical lower bounds on the conditional coverage probabilities under Nakagami-$m$ and Rayleigh fading, respectively. These expressions have a low computational complexity, enabling efficient numerical evaluations. We validate the effectiveness of our theoretical model by contrasting the coverage probability obtained from our analysis with that estimated from a Starlink constellation. The results show that our analysis provides a tight lower bound on the actual value and, surprisingly, matches the empirical simulations almost perfectly with a 1 dB shift. This demonstrates our framework as an appropriate theoretical model for LEO satellite networks.

Existing semantic communication schemes primarily focus on single-hop scenarios, overlooking the challenges of multi-hop wireless image transmission. As semantic communication is inherently lossy, distortion accumulates over multiple hops, leading to significant performance degradation. To address this, we propose the multi-hop parallel image semantic communication (MHPSC) framework, which introduces a parallel residual compensation link at each hop against distortion accumulation. To minimize the associated transmission bandwidth overhead, a coarse-to-fine residual compression scheme is designed. A deep learning-based residual compressor first condenses the residuals, followed by the adaptive arithmetic coding (AAC) for further compression. A residual distribution estimation module predicts the prior distribution for the AAC to achieve fine compression performances. This approach ensures robust multi-hop image transmission with only a minor increase in transmission bandwidth. Experimental results confirm that MHPSC outperforms both existing semantic communication and traditional separated coding schemes.

To ensure future high-quality and reliable massive communication during 6G uplink transmissions, we propose a conditional diffusion model-driven massive MIMO detector, which can iteratively estimate channels and detect data for the uplink multiuser access. This approach utilizes a generative diffusion model to learn the score function of the joint posterior by integrating the prior distribution with the likelihood derived from the transmission model. The prior distribution is obtained either by learning from channel statistics or through analytical derivation from the symbol constellation. By employing noise matching initialization and an asynchronous annealed Langevin dynamics (ALD) sampling scheme, the receiver alternates efficiently between score-based channel estimation and data detection, thus avoiding traps of local minima. Simulation results demonstrate that this iterative diffusion process outperforms Bayesian-based and existing synchronous ALD channel estimation and data detection schemes in multiuser uplink scenarios.

The Levenshtein sequence reconstruction problem studies the reconstruction of a transmitted sequence from multiple erroneous copies of it. A fundamental question in this field is to determine the minimum number of erroneous copies required to guarantee correct reconstruction of the original sequence. This problem is equivalent to determining the maximum possible intersection size of two error balls associated with the underlying channel. Existing research on the sequence reconstruction problem has largely focused on channels with a single type of error, such as insertions, deletions, or substitutions alone. However, relatively little is known for channels that involve a mixture of error types, for instance, channels allowing both deletions and substitutions. In this work, we study the sequence reconstruction problem for the single-deletion two-substitution channel, which allows one deletion and at most two substitutions applied to the transmitted sequence. Specifically, we prove that if two $q$-ary length-$n$ sequences have the Hamming distance $d\geq 2$, where $q\geq 2$ is any fixed integer, then the intersection size of their error balls under the single-deletion two-substitution channel is upper bounded by $(q^2-1)n^2-(3q^2+5q-5)n+O_q(1)$, where $O_q(1)$ is a constant independent from $n$ but dependent on $q$. Moreover, we show that this upper bound is tight up to an additive constant.

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework, abbreviated as WVSC, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, multi-frame compensation (MFC) is proposed to produce compensated current semantic frame with a multi-frame fusion attention module. With both the reference frame transmission and MFC, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC over other DL-based methods e.g. DVSC about 1 dB and traditional schemes about 2 dB in terms of PSNR.

As new applications evolve rapidly, wireless networks increasingly require low-delay communication to significantly enhance the quality of user experience. In response, the evolution of the medium access control (MAC) layer has gained more attention, particularly through the application of reinforcement learning to optimize access strategies. In order to meet the low-delay requirements, we propose a reinforcement learning-based MAC protocol, named soft actor-critic multiple access (SAC-MA). To mitigate frequent collisions caused by the exploratory behavior, we propose a multiple waiting actions mechanism that allows stations to wait for multiple time slots. This mechanism enables the agent to develop a more flexible and intelligent access strategy, thereby effectively reducing delay. Additionally, we introduce an innovative formulation in which the head-of-line packet is treated as the agent, enabling more timely feedback and observations. We conduct extensive simulations to demonstrate that SAC-MA: 1) reduces delay by approximately 27.9% and 56.5% compared to the conventional MAC protocol with standard parameters under the collision and capture models, respectively; 2) adapts to environmental changes in dynamic scenarios; 3) coexists harmoniously with legacy stations and reduces the network delay in heterogeneous scenarios. Finally, we perform ablation studies to evaluate the effectiveness of the proposed mechanisms.

The integration of edge computing and satellite networks has emerged as a promising solution to support remote terrestrial computation with wide coverage and low latency. However, single-satellite computing leads to uneven resource utilization and degraded service quality. To address this, peer offloading is required to improve both service quality and resource efficiency. Asides from peer offloading, diverse service requests also call for an appropriate service deployment strategy, which should be jointly optimize with offloading decision. In this paper, taking into processing cost and service update cost, we formulate a long-term optimization for service deployment and peer offloading. To pursue long-term performance, the problem is first reformulated into a sequence of time-invariant problems. Since frequent service deployment adjustments incur overhead and may cause service interruption, we decompose the time-invariant problem into a service deployment subproblem and a peer offloading subproblem, optimized at different timescales. A hierarchical method iteratively solve the two subproblems. In particular, we propose an online distributed algorithm for small-timescale peer offloading. Each local peer offloading problem is transformed into a capacity-constrained minimum cost maximum flow problem, enabling a low-complexity solution via the successive shortest path algorithm. We provide theoretical analysis showing that the proposed algorithm asymptotically approaches the offline optimum at the expense of system congestion. Moreover, we show that the performance bound grows with the large-timescale interval. Simulations results validate the theoretical analysis and demonstrate the effectiveness of the propose algorithm in terms of processing cost and service update cost.

This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

Acquiring synthetic aperture radar (SAR) images is inherently difficult and laborious. To mitigate this data scarcity, generative models for SAR images aim to learn underlying data distributions from large-scale datasets, ensuring robust performance. Generative models mitigate data scarcity but rely on centralized frameworks, posing privacy risks and limiting deployment in Internet of Things (IoT) scenarios. To tackle the aforementioned challenges, we introduce an innovative federated angle-aware generative diffusion (FAGD) framework for secure SAR image generation. This framework integrates three key innovations: federated learning (FL), the angle-aware generative diffusion (AAGDiff) model, and the local data knowledge distillation (LDKD) strategy. Specifically, we introduce the FL framework, which enables clients to collaboratively train a generative model by transmitting model weights, thereby eliminating raw data exchange and enhancing security. We then propose the AAGDiff model for client-side high-quality generation, leveraging denoising diffusion probabilistic models (DDPMs) to synthesize high-quality SAR images from noise using an angle-conditioned encoder, enabling the generation of SAR images at different target azimuth angles. Additionally, the LDKD strategy alleviates overfitting during federated training under Non-Independent and Non-Identically Distributed (non-IID) data distributions by allowing the model to distill knowledge primarily from less frequent classes, thus enhancing generalization and robustness. Evaluated extensively on MSTAR and OpenSARShip datasets, the proposed framework ensures secure SAR image generation while achieving superior target recognition performance. Overall, the FAGD framework offers an effective and scalable solution for privacy-preserving SAR image synthesis.

Driven by the escalating demand for extensive coverage and ultra-reliable communication in the Internet of Vehicles (IoV), conventional single-transmission technologies frequently encounter a severe ”bottleneck effect” in long-distance and heavily obstructed environments. To break through this limitation, this paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted cascaded amplify-and-forward (AF) and decode-andforward (DF) relay system over independent and identically distributed (i.i.d.) Nakagami-mfading channels. The proposed architecture innovatively exploits the synergistic advantages of cascaded active relays (providing initial power amplification via AF and terminal signal regeneration via DF) alongside the ”passive array gains” from the RIS, thereby effectively overcoming the severe ”double path loss” inherent in passive links while suppressing accumulated noise. Addressing the challenges of prohibitive pilot overhead and latency associated with acquiring instantaneous channel state information (CSI) in highly dynamic IoV environments, we propose a statistical CSI-based adaptive power allocation algorithm utilizing particle swarm optimization (PSO). By jointly optimizing the transmit power allocation among the source node, the AF relay, and the DF relay, the proposed algorithm efficiently tackles the intricate non-convex optimization problem subject to a total power constraint. Theoretically, we derive exact closed-form expressions for the system outage probability (OP) and average channel capacity, establishing a rigorous theoretical evaluation framework. Extensive simulation results not only validate the accuracy of the theoretical analysis but also demonstrate that the proposed PSO strategy effectively exploits spatial diversity gains and RIS passive beamforming gains. The findings reveal that the scheme significantly enhances transmission reliability and system robustness in complex propagation environments, substantiating its application potential for Intelligent Transportation Systems (ITS).

Beamspace channel estimation is crucial for unlocking the potential of millimeter-wave (mmWave) communications in low Earth orbit (LEO) satellite networks. Effective channel estimation tailored to the unique characteristics of LEO channels, including hybrid beamforming architectures, is imperative. This paper presents a novel joint design framework integrating transmit pilot waveforming and receive compressive sensing (CS) for downlink beamspace channel estimation in LEO satellite communications. By exploiting channel sparsity and deep learning with an autoencoder (AE), the joint design is formulated as a sensing matrix design problem. We explore a simple AE training method utilizing 1-sparse channel patterns, enabling efficient decoding with orthogonal matching pursuit (OMP) and its variants. Subsequently, a low-rank approximation method is employed to extract the precoder and compressor from the trained sensing matrix. We further propose a machine learning (ML)-OMP method using a customized multilayer perceptron (MLP) network for iterative support selection. Simulation results demonstrate that the proposed transmit pilot waveforming and receive CS methods significantly enhance beamspace channel estimation performance with fewer pilot time slots. Furthermore, the proposed ML-OMP method outperforms conventional OMP, particularly in scenarios with non-customized precoders and compressors.

The Internet of Thing (IoT) devices play a vital role in collecting mission-critical and time-sensitive sensing data from remote areas, where traditional terrestrial networks are constrained by sparse infrastructures. However, resource-limited ground devices (GDs) in such scenarios often lack the ability to directly transmit essential information to distant data centers. To overcome this challenge, this paper proposes a Satellite-unmanned aerial vehicle (UAV)-assisted data collection framework, where the UAV is controlled by a remote control center via satellite relays. Aiming to maximize the energy efficiency (EE) of the UAV, we first design a reference trajectory to the UAV with given hovering positions. Subsequently, we optimize the power allocation for communication and state sensing strategies for trajectory tracking control, while guaranteeing control stability and communication reliability. These challenging problems are addressed using sequently an efficient algorithm, incorporating Deep Q-Network (DQN), closed-form derivations, and one-dimensional search method. Extensive numerical simulations and experimental validations are conducted to demonstrate the effectiveness of the proposed approach. Key findings point that the data size of collection has greater impacts than transmission power. Moreover, the results reveal the relationships among the communication, control and state sensing in terms of the EE.

Considering the random access behaviour of mobile devices (MDs), heterogeneous service requirements, and dynamic completion or discarding of tasks, the statistical characteristics of tasks to be scheduled, transmitted, and computed vary over time in practical multi-access edge computing (MEC) systems. Therefore, it is necessary to design a dynamic task offloading and resource allocation (TORA) strategy to adaptively match these time-varying task characteristics. To achieve the goal, we first formulate the dynamic TORA problem as a Markov decision process (MDP) with time-varying extension of state space and action space, which cannot be effectively solved by conventional deep reinforcement learning (DRL) algorithms. To address this challenge, we propose a general state-action space adaptive (SASA) DRL framework by exploiting the advantages of the Transformer architecture and its multi-head attention (MHA) mechanism. This framework facilitates the integration of available actor-critic DRL algorithms to efficiently solve MDPs with time-varying state and action spaces. Based on the proposed SASA DRL framework, we further develop the SASA-based TORA algorithm, referred to as SASA-TORA, which is adaptable to not only dynamic network conditions but also time-varying statistical characteristics of tasks. Simulation results demonstrate the superiority of SASA-TORA over baseline algorithms and highlight the limitations of conventional DRL algorithms in handling MDPs with time-varying state and action spaces.

Mobile edge computing–space-air-ground integrated network (MEC-SAGIN) is emerging as a crucial component of future wireless systems. Despite its potential, addressing network fluctuations while ensuring continuous low-latency computing services in highly dynamic environments remains a significant challenge. To address this issue, this paper proposes a fluid antenna (FA)-assisted MEC-SAGIN system, which enhances channel transmission conditions and reduces uplink task offloading latency by flexibly adjusting the antenna ports of edge computing users equipped with FAs. Specifically, we aim to minimize the maximum total computational delay (TCD) of edge computing tasks for ground users (GUs) and the satellite user (SU) by jointly optimizing the task offloading strategies, computational resource allocation, FA port positions, unmanned aerial vehicle (UAV) location, and the receive beamforming matrix. To solve this non-convex problem, we employ the block coordinate descent (BCD) technique to decompose the original problem into four subproblems. The subproblems are optimized using a combination of low-complexity iterative algorithms and the projected gradient descent (PGD) method to refine communication and computation configurations as well as FA port selection. Simulation results demonstrate that the FA-assisted scheme significantly improves the TCD performance of the MEC-SAGIN system. It maintains transmission stability and reliability in dynamic environments while outperforming conventional fixed-position antennas (FPAs) and random-port antenna schemes.

Zero-Touch Networks (ZTNs) represent a transformative paradigm toward fully automated and intelligent network management, providing the scalability and adaptability required for the complexity of sixth-generation (6G) networks. However, the distributed architecture, high openness, and deep heterogeneity of 6G networks expand the attack surface and pose unprecedented security challenges. To address this, security automation aims to enable intelligent security management across dynamic and complex environments, serving as a key capability for securing 6G ZTNs. Despite its promise, implementing security automation in 6G ZTNs presents two primary challenges: 1) automating the lifecycle from security strategy generation to validation and update under real-world, parallel, and adversarial conditions, and 2) adapting security strategies to evolving threats and dynamic environments. This motivates us to propose SecLoop and SA-GRPO. SecLoop constitutes the first fully automated framework that integrates large language models (LLMs) across the entire lifecycle of security strategy generation, orchestration, response, and feedback, enabling intelligent and adaptive defenses in dynamic network environments, thus tackling the first challenge. Furthermore, we propose SA-GRPO, a novel security-aware group relative policy optimization algorithm that iteratively refines security strategies by contrasting group feedback collected from parallel SecLoop executions, thereby addressing the second challenge. Extensive real-world experiments on five benchmarks, including 11 MITRE ATT&CK processes and over 20 types of attacks, demonstrate the superiority of the proposed SecLoop and SA-GRPO. We will release our platform to the community, facilitating the advancement of security automation towards next generation communications.

Motivated by International Mobile Telecommunications (IMT)-2030, sixth generation (6G) mobile networks in 3GPP (third-generation partner project) commenced with a workshop in March 2025, attracting more than 200 contributions and 700 in-person attendances. While the details of 6G study and the eventual 6G specifications in 3GPP are yet to be developed, there is strong motivation in 3GPP to focus on the fundamental values that 6G may bring, most notably in terms of improving user experience and the overall network operation, particularly with respect to reducing the total cost of ownership (TCO). It is evident that there is a strong need to streamline and simplify the 6G specifications for efficient standardization, implementation, and commercial deployments. For the services also accommodated by the fifth generation (5G) networks, 6G is expected to provide meaningful enhancements. That is, instead of simply pushing for even higher envelopes such as peak data rates, more emphasis will be on improved coverage (especially at the cell edge and for certain data rates or services), and energy efficiency (for both end devices and networks) using the existing and new spectrum. Moreover, 6G is expected to support new services, with artificial intelligence (AI) and sensing as two primary examples. In this article, we provide a tutorial on the key technologies driving fundamental enhancements for the standardization of 6G and beyond, covering the necessary co-existence between 5G and 6G for smooth migration, AI-native radio access network (RAN), multiple-input-multiple-out (MIMO), sustainable operation with increased energy efficiency, coverage enhancements involving both terrestrial and non-terrestrial networks (NTN), integrated sensing and communication (ISAC), diverse device types including the low-end back-scattering based ambient internet of things (IoT) devices, and native support of NTN. We conclude this article by pointing out several key challenges and opportunities towards standardization of 6G and beyond.

The low-altitude economy (LAE), an emerging economic paradigm encompassing various flight activities in low-altitude airspace, has attracted widespread attention from academia and industry due to its appealing economic and social benefits. In this paper, we investigate the design of integrated sensing and communication (ISAC) waveforms for LAE applications. Specifically, we propose an adaptive ISAC waveform, which integrates the maximum distance separable (MDS) code and index modulation (IM) into the orthogonal frequency division multiplexing (OFDM) waveform, namely A-MDS-OFDM-IM. This design combines the hybrid benefits of MDS code, IM, and OFDM techniques, i.e., the error detection capability of MDS code, the high spectral efficiency (SE) of IM, and the high sensing resolution of OFDM, thereby enabling robust communication and sensing. A comprehensive performance analysis of A-MDS-OFDM-IM is provided, including its bit error rate (BER), peak-to-sidelobe level (PSL), and peak-to-average power ratio (PAPR). Moreover, to address the high PAPR issue of A-MDS-OFDM-IM, we develop an adaptive design criterion based on the alternating direction method of multipliers (ADMM), which is capable of jointly optimizing the communication, sensing, and PAPR performance of the proposed system. Simulation results demonstrate that the proposed waveform achieves better BER performance than conventional OFDM-based waveforms under a non-ideal high power amplifier (HPA), owing to its low-PAPR characteristic. Additionally, the proposed waveform ensures robust sensing with satisfactory PSL performance, making it a promising ISAC waveform for LAE applications.

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

Accurate channel prediction is essential in massive multiple-input multiple-output (m-MIMO) systems to improve precoding effectiveness and reduce the overhead of channel state information (CSI) feedback. However, existing methods often suffer from accumulated prediction errors and poor generalization to dynamic wireless environments, making it challenging to maintain high prediction accuracy. Large language models (LLMs) have demonstrated remarkable modeling and generalization capabilities in tasks such as time series prediction, making them a promising solution. Nevertheless, a significant modality gap exists between the linguistic knowledge embedded in pretrained LLMs and the intrinsic characteristics of CSI, posing substantial challenges for their direct application to channel prediction. Moreover, the large parameter size of LLMs hinders their practical deployment in real-world communication systems with stringent latency constraints. To address these challenges, we propose a novel channel prediction framework based on semantically aligned large models, referred to as CSI-ALM, which bridges the modality gap between natural language and channel information. Specifically, we design a cross-modal fusion module that aligns CSI representations with the language feature space using a pretrained corpus. Additionally, we maximize the cosine similarity between word embeddings and CSI embeddings to construct semantic cues, effectively leveraging the latent knowledge in LLMs. To reduce complexity and enable practical implementation, we further introduce a lightweight version of the proposed approach, called CSI-ALM-Light. This variant is derived via a knowledge distillation strategy based on attention matrices, which extracts essential features from the teacher model, CSI-ALM, and transfers them to a compact, efficient student model, CSI-ALM-Light. Extensive experimental results demonstrate that CSI-ALM consistently outperforms state-of-the-art deep learning methods across various communication scenarios, achieving substantial performance gains. Moreover, under limited training data conditions—where all models are trained using only 10% of the original training dataset—CSI-ALM-Light, with only 0.34M parameters, attains performance comparable to CSI-ALM and significantly outperforms conventional deep learning approaches. These validate the effectiveness of the proposed approach for accurate and efficient channel prediction in m-MIMO systems.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance both achievable rate and energy efficiency in next-generation wireless networks. This article investigates a novel energy-efficient UAV-RIS-assisted architecture for simultaneous wireless information and power transfer (SWIPT) in integrated ground–aerial–space networks, where a satellite cooperates with multiple UAV-RISs to provide downlink wireless energy transfer (WET) and support uplink wireless information transmission (WIT) for energy-constrained IoT devices in remote environments. We formulate an alternating iterative joint optimization problem (AIJOP) that aims to maximize the system sum achievable rate while ensuring causality of device energy via jointly optimizing UAV-RIS trajectories, RIS phase shift matrices, and satellite power allocation and time allocation between WET and WIT. The problem is highly non-convex due to the strong coupling among variables. To address this challenge, we propose a trajectory–phase–power–time alternating optimization algorithm (TPPTAOA), which decomposes the original problem into four tractable subproblems and solves them iteratively. Specifically, the UAV-RIS trajectories is first optimized via using a device scheduling and TSP-based path planning approach to solve the first subproblem, followed by the proposition of a two-stage heuristic phase optimization algorithm under fixed parameters to solve the second subproblem. Subsequently, the satellite power allocation is solved using a Lagrangian dual method, while the time allocation is optimized through a two-stage strategy combining grid-based coarse search with gradient-based refinement. Simulation results under various system settings verify the fast convergence, robustness, and superior performance of the proposed TPPTAOA, showing significant improvements in both energy efficiency and uplink achievable rate compared with benchmark schemes.

Low-altitude economy (LAE) is an emerging business model, which heavily relies on integrated sensing and communications (ISAC), mobile edge computing (MEC), and covert communications. This paper investigates the covert transmission design in MEC-based networked ISAC systems towards LAE, where an MEC server coordinates multiple access points to simultaneously receive computation tasks from multiple unmanned aerial vehicles (UAVs), locate a target in a sensing area, and maintain the UAVs’ covert transmission against multiple wardens. We first derive closed-form expressions for the detection error probability (DEP) at the wardens. Then, we formulate a total energy consumption minimization problem by optimizing communication, sensing, and computation resources as well as UAV trajectories, subject to the requirements on the quality of MEC services, DEP, and the radar signal-to-interference-and-noise ratio, and the causality constraints of UAV trajectories. An alternating optimization-based algorithm is proposed to handle the considered problem, which decomposes it into two subproblems: joint optimization of communication, sensing, and computation resources, and UAV trajectory optimization. The former is addressed by a successive convex approximation-based algorithm, while the latter is solved via a trust-region-based algorithm. Simulations validate the effectiveness of the proposed algorithm compared with various benchmarks, and reveal the trade-offs among communication, sensing, and computation in LAE systems.

Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources. However, existing intelligent spectrum management methods, typically based on small-scale models, suffer from notable limitations in recognition accuracy, convergence speed, and generalization, particularly in the complex and dynamic spectrum environments. To address these challenges, this paper proposes a novel spectrum foundation model, termed SpectrumFM, establishing a new paradigm for spectrum management. SpectrumFM features an innovative encoder architecture that synergistically exploits the convolutional neural networks and the multi-head self-attention mechanisms to enhance feature extraction and enable robust representation learning. The model is pre-trained via two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, which leverage large-scale in-phase and quadrature (IQ) data to achieve comprehensive and transferable spectrum representations. Furthermore, a parameter-efficient fine-tuning strategy is proposed to enable SpectrumFM to adapt to various downstream spectrum management tasks, including automatic modulation classification (AMC), wireless technology classification (WTC), spectrum sensing (SS), and anomaly detection (AD). Extensive experiments demonstrate that SpectrumFM achieves superior performance in terms of accuracy, robustness, adaptability, few-shot learning efficiency, and convergence speed, consistently outperforming conventional methods across multiple benchmarks. Specifically, SpectrumFM improves AMC accuracy by up to 12.1% and WTC accuracy by 9.3%, achieves an area under the curve (AUC) of 0.97 in SS at -4 dB signal-to-noise ratio (SNR), and enhances AD performance by over 10%.

In integrated sensing and communication (ISAC) systems, the timeliness performance is pivotal to support high-precision sensing and reliable high-rate data communication, particularly for real-time applications. However, there are two critical challenges in the research on the timeliness of ISAC systems: joint characterization of sensing-communication time-liness tradeoffs and unified resource allocation optimization. To address these challenges, a timeliness-centric analysis and optimization framework is proposed for ISAC systems. First, the age of information (AoI) of sensing tasks and the delay of communication tasks are analyzed to evaluate the timeliness performance of ISAC systems. Then, the closed-form expressions of the outage probabilities for sensing and communication tasks are derived, quantifying their reliability under dynamic channel conditions. Next, a joint sensing-and-communication time-allocation optimization problem is established to minimize the weighted sum of sensing AoI and communication delay. To tackle the non-linear integer programming problem, a dynamic programming (DP) algorithm is designed, which can be verified to converge to an optimal solution with polynomial computational complexity. Simulation results are presented to validate the accuracy of the analytical results, and demonstrate the optimality and performance gains of our proposed DP algorithm.

The satellite-aerial-terrestrial network (SATN) is a promising architecture to achieve seamless global coverage and meet diverse quality-of-service (QoS) requirements in next-generation wireless communications. To support such multi-layered connectivity, we consider a mixed radio frequency (RF) and free-space optical (FSO) architecture, where heterogeneous users access a high-altitude platform (HAP) via RF links, and the HAP, acting as an aerial relay, forwards the aggregated traffic to a satellite through an FSO backhaul. Existing transmission schemes for such mixed RF-FSO SATNs, however, are not well suited to providing differentiated statistical QoS guarantees for heterogeneous users. To address this limitation, we propose a mixed RF-FSO QoS-aware uplink transmission (MRQ-UT) scheme. Specifically, we impose statistical delay-QoS constraints at both the user and HAP buffers, thereby explicitly capturing heterogeneous constraints on queueing delay and buffer overflow. On this basis, we derive the system effective capacity using a two-stage tandem queue model, which captures the sequential queuing behavior over the RF access and FSO backhaul links. Building upon this model, we develop a tractable effective-capacity-based optimization framework and propose a statistical channel-aware joint power and beamforming algorithm that enhances QoS provisioning under imperfect channel state information. Simulation results demonstrate that the proposed MRQ-UT scheme significantly outperforms benchmark schemes in terms of effective capacity and statistical QoS performance.

To support emerging language-based applications using dispersed and heterogeneous computing resources, the hybrid language model (HLM) offers a promising architecture, where an on-device small language model (SLM) generates draft tokens that are validated and corrected by a remote large language model (LLM). However, the original HLM suffers from substantial communication overhead, as the LLM requires the SLM to upload the full vocabulary distribution for each token. Moreover, both communication and computation resources are wasted when the LLM validates tokens that are highly likely to be accepted. To overcome these limitations, we proposecommunication-efficient and uncertainty-aware HLM (CU-HLM). In CU-HLM, the SLM transmits truncated vocabulary distributions only when its output uncertainty is high. We validate the feasibility of this opportunistic transmission by discovering a strong correlation between SLM’s uncertainty and LLM’s rejection probability. Furthermore, we theoretically derive optimal uncertainty thresholds and optimal vocabulary truncation strategies. Simulation results show that, compared to standard HLM, CU-HLM achieves up to 206× higher token throughput by skipping 74.8% transmissions with 97.4% vocabulary compression, while maintaining 97.4% accuracy.

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.

To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various SnS characteristics and construct an SnS near-field channel model for XL-MIMO systems. Due to the limitations of uniform subarray patterns in capturing SnS, we analyze the adverse effects of the non-ideal array segmentation (over- and under-segmentation) on CE accuracy. To counter these issues, we develop a dynamic hybrid beamforming-assisted power-based subarray segmentation paradigm (DHBF-PSSP), which integrates power measurements with a dynamic hybrid beamforming structure to enable joint subarray partitioning and decoupling. A power-adaptive subarray segmentation (PASS) algorithm leverages the statistical properties of power profiles, while subarray decoupling is achieved via a subarray segmentation-based sampling method (SS-SM) under radio frequency (RF) chain constraints. For subarray CE, we propose a subarray segmentation-based assorted block sparse Bayesian learning algorithm under the multiple measurement vectors framework (SS-ABSBL-MMV). This algorithm exploits angular-domain block sparsity under a discrete Fourier transform (DFT) codebook and inter-subcarrier structured sparsity. Simulation results confirm that the proposed framework outperforms existing methods in CE performance.

This work investigates the time-dependent statistical characteristics (SCs) of age of information (AoI) in V2I periodic-updating. The D/G/1/1 (non-)preemptive discrete-time (slotted) queue model is employed. The entire AoI process is split into a set of slot-specific AoI processes w.r.t. updating period (UP). A Markov multi-dimensional age process w.r.t. UP is used to track the evolution of slot-specific AoI. Accordingly, the time-dependent AoI distribution and expected AoI (EAoI) are derived, forming a framework to study the time-dependent AoI SCs under periodic updating. Based on the AoI SCs, we optimize the decision-making (DM) process at roadside unit to minimize age upon decisions, improving information freshness when conducting DMs. A quasiperiodic DM mechanism is regarded, where one DM slot is probabilistically determined in each UP. The optimal DM probabilities are obtained in closed-form. In the D/Geo/1/1 case, we find that the preemption-induced EAoI gains are identical among slots; for extremely reliable/error-prone channels, conducting DM at the second/middle slot in UP is optimal. Numerical results verify the effectiveness of theoretical analyses and DM process optimization.

Semantic communication (SemCom), as a novel paradigm for future communication systems, has recently attracted much attention due to its superiority in communication efficiency. However, similar to traditional communication, it also suffers from eavesdropping threats. Intelligent eavesdroppers could launch advanced semantic analysis techniques to infer secret semantic information. Therefore, some researchers have designed Semantic Steganography Communication (SemSteCom) schemes to confuse semantic eavesdroppers. However, the state-of-the-art SemSteCom schemes for image transmission rely on the pre-selected cover image, which limits the generalization. To address this issue, we propose a Generative Diffusion Model-based Coverless Semantic Steganography Communication (SemSteDiff) scheme to hide secret images into generated stego images. The semantic related private and public keys enable legitimate receiver to decode secret images correctly while the eavesdropper without the completely correct key-pairs fail to obtain them. Simulation results demonstrate the effectiveness of the plug-and-play design in different Joint Source-Channel Coding (JSCC) frameworks. Results under different eavesdropping settings show that, when Signal-to-Noise Ratio (SNR) = 0 dB, the peak signal-to-noise ratio (PSNR) of the legitimate receiver is 4.14 dB higher than that of the eavesdropper.

Federated learning (FL) and split learning (SL) are two effective distributed learning paradigms in wireless networks, enabling collaborative model training across mobile devices without sharing raw data. While FL supports low-latency parallel training, it may converge to less accurate model. In contrast, SL achieves higher accuracy through sequential training but suffers from increased delay. To leverage the advantages of both, hybrid split and federated learning (HSFL) allows some devices to operate in FL mode and others in SL mode. This paper aims to accelerate HSFL by addressing three key questions: 1) How does learning mode selection affect overall learning performance? 2) How does it interact with batch size? 3) How can these hyperparameters be jointly optimized alongside communication and computational resources to reduce overall learning delay? We first analyze convergence, revealing the interplay between learning mode and batch size. Next, we formulate a delay minimization problem and propose a two-stage solution: a block coordinate descent method for a relaxed problem to obtain a locally optimal solution, followed by a rounding algorithm to recover integer batch sizes with near-optimal performance. Experimental results demonstrate that our approach significantly accelerates convergence to the target accuracy compared to existing methods.

Recent popularization of the Internet of Vehicles (IoVs) and vehicles-to-everything (V2X) enables the emergence of real-time vehicular applications, posing challenges to resourcelimited vehicles. Toward this end, vehicle edge computing (VEC) has been proposed to alleviate the computational burden on vehicles by leveraging resources from roadside units (RSUs) and VEC servers. While existing works mainly focus on the task requirement for either vehicles or RSUs, the joint task offloading for both V2X and RSUs-to-everything (R2X) has not been fully studied. In this paper, we aim at optimizing the task offloading strategies for both vehicles and RSUs, and adopt a multi-hop task offloading manner to fully utilize the VEC network resources. This problem introduces a severe state-action space shift issue with varying dimensions and representation, which poses challenges for conventional DRL approaches. To address it, we propose a Bidirectional Encoder Representations from Transformers (Bert)-based matching Q-network (BMQN) algorithm. First, we design the BMQN model to efficiently capture correlations among all vehicles and RSUs through bidirectional attention. Then, we propose type-embedded grouped attention and available action embedding to mitigate the overfitting sequence length issue, thereby enhancing generalization capacity. Moreover, we propose to address the state-action space shift issue through a matching-based manner, which can significantly enhance the task offloading ability by matching the states among devices. Simulation results demonstrate that: 1) the BMQN can achieve much better performance than other approaches in scenarios comprising various numbers of vehicles and RSUs as well as diverse road lengths; 2) the BMQN has sufficient generalization capacity to adapt to inexperienced scenarios through matching-based architecture and available action embedding.

The advent of the 5G RedCap, the upcoming 6G and the proliferation of the Internet of Things (IoT) have catalyzed the rapid advancement of unmanned aerial vehicle (UAV) technology while also promoting UAVs' widespread application. In IoT-enabled environments where the global positioning system (GPS) signals are compromised, visual simultaneous localization and mapping (V-SLAM) technology has emerged as an effective positioning solution, valued for its reliability. However, the presence of dynamic elements in complex environments, such as pedestrians and vehicles, poses challenges to the positioning accuracy of UAVs employing V-SLAM for navigation. This paper proposes a dynamic feature filtering-based SLAM (DFF-SLAM) approach to eliminate the impact of dynamic factors in dynamic environments, thereby enhancing the positioning accuracy of UAVs in IoT-enabled complex environments. Firstly, a semantic detection thread is designed to identify semantic information in the scene and acquire prior dynamic targets, facilitating the filtering of prior dynamic feature points. Secondly, optical flow tracking conducted at each level of the image pyramid facilitates feature point matching across consecutive images. Finally, the epipolar geometry constraint is utilized to determine the motion status of remaining feature points, further filtering out dynamic feature points. Simulation results demonstrate that compared to traditional visual SLAM systems, the UAV equipped with the DFF-SLAM system achieves more accurate positioning and meets real-time positioning requirements when navigating through IoT enabled complex environments

Federated Learning (FL) is widely used in mobile computing as a communication-efficient distributed machine learning (ML) paradigm; however, it faces challenges such as model convergence to local optima or slow convergence due to the heterogeneity of client data. To mitigate data heterogeneity, the Nesterov Accelerated Gradient (NAG) method demonstrates its effectiveness by predictively updating the gradient to improve system performance. However, the performance of NAG depends heavily on the choice of decay coefficients; larger coefficients have greater acceleration but may lead to an unstable convergence process due to their unreasonable prediction of the descent gradient. To solve the above problems, this paper proposes the first radical heavy ball (RHB) method that combines momentum and NAG. In Stochastic Gradient Descent (SGD), momentum stabilizes the gradient descent process by integrating the historical gradients to update the parameters, and the RHB strategy decouples a single decay coefficient into an NAG component and a momentum component. The RHB introduces a gradient recall after each gradient acceleration by the NAG to strengthen the NAG's perception of the historical gradients, thus stabilizing the gradient descent process. By weighing the historical gradients and the predicted gradient, RHB effectively mitigates the instability of NAG convergence and demonstrates better performance. As a result, the algorithm further mitigates the impact of customer data heterogeneity in FL and can effectively deliver global update information to participants without additional communication costs. We conduct comprehensive experiments in a binary function, single node, and federated model environment to analyze the convergence properties in non-convex loss functions. RHB exhibits better performance and less computational overhead than many existing algorithms.

The advancement of wireless networks has spurred an increasing demand for high-quality maritime communication services. This study presents an innovative unicast-multicast access and backhaul maritime communication network (UMABMCN), in which a high-altitude platform (HAP) provides HAP-to-vessel (H2V) unicast services to vessels and backhaul support to unmanned aerial vehicles (UAVs) through HAP-to-UAV (H2U) links. Additionally, multiple UAVs are deployed to deliver UAV-to-vessel (U2V) multicast transmission services to vessels. Specifically, we formulate a HAP-UAV-assisted unicast-multicast cooperation multi-objective optimization problem (UMCMOP) aimed at maximizing the sum achievable rate of base stations (BS)-to-vessel (B2V), maximizing the sum backhaul rate of H2U, and minimizing the energy consumption of UAVs via jointly optimizing communication connection between BSs and vessels, power allocations of UAVs, along with the placement of UAVs. The formulated UMCMOP is a mixed integer non-linear programming (MINLP) problem. To address this, we propose an enhanced multi-objective multi-verse optimization (EMOMVO-CGD) algorithm, which integrates achaos probability operator,gray wolf exploitation operator, anddiscrete update operator. To further validate the performance of EMOMVO-CGD, a joint communication connection, power allocation and placement optimization (JCCPAPO) method is proposed. Simulation results demonstrate that the two proposed algorithms outperform benchmark strategies in optimizing the aforementioned objectives.

Open Radio Access Network (O-RAN) supports heterogeneous service coexistence through functional splitting and open interfaces, enabling traffic steering via functional orchestration and resource scheduling. However, existing studies focus on known traffic patterns and lack the ability to anticipate dynamic service demands in advance. Isolated optimization of orchestration and scheduling fails to ensure End-to-End (E2E) latency. The varying time scales and vast solution space further complicate the joint optimization. To address this, we propose a traffic twin-enabled orchestration and scheduling multi-timescale joint optimization scheme. Explicitly, we design a spatiotemporal attention-assisted Time Series Generative Adversarial Network (TimeGAN) traffic twin model (STAG-TD) to capture unknown traffic patterns. Based on twin results, we formulate a joint optimization problem and design a dual-timescale algorithm framework, including propose a Task Decomposed Dueling Double Deep Q-Network (TD3QN) algorithm to handle large-timescale orchestration, and use a Penalty-based Particle Swarm Optimization (PPSO) algorithm to manage small-timescale scheduling. Our scheme achieves a predictive joint optimization to reduce the transmission latency of services. Extensive results show our scheme outperforms state-of-the-art methods, reducing E2E latency by over 39% and increasing throughput by over 14.9%. The highly consistent results between real and twin data also demonstrate the effectiveness of the traffic twin model.

Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterogeneity, limit its practicality in mobile edge computing. Existing compression methods like sparsification and pruning reduce per-round costs but may increase training rounds and thus the total training cost, especially under heterogeneous environments. We propose a lightweight personalized FL framework built on parameter decoupling, which separates the model into shared and private subspaces, enabling us to uniquely apply gradient sparsification to the shared component and model pruning to the private one. This structural separation confines communication compression to global knowledge exchange and computation reduction to local personalization, protecting personalization quality while adapting to heterogeneous client resources. We theoretically analyze convergence under the combined effects of sparsification and pruning, revealing a sparsity-pruning trade-off that links to the iteration complexity. Guided by this analysis, we formulate a joint optimization that selects per-client sparsity and pruning rates and wireless bandwidth to reduce end-to-end training time. Simulation results demonstrate faster convergence and substantial reductions in overall communication and computation costs with negligible accuracy loss, validating the benefits of coordinated and resource-aware personalization in resource-constrained heterogeneous environments.

Since there exists a single point of server failure in conventional centralized federated learning, the decentralized federated learning (DFL) framework has become increasingly popular in recent years. However, when a large number of edge devices participate in DFL, it requires frequent model interactions between edge devices and long convergence time. In this work, we combat the impact of device heterogeneity in the large-scale DFL framework. To optimize communication efficiency and reduce the network complexity in large-scale DFL framework, we propose a decentralized edge devices clustering (DEDC) approach which leverages dense connectivity as the foundation to group edge devices with similar data distributions into clusters, thereby forming a novel multi-cluster decentralized federated edge learning (MD-FEEL) framework. The clustering method is adaptive, meaning it can effectively work across various network topologies, as long as the network is connected. We propose an asynchronous algorithm in the formed MD-FEEL framework, which consists four steps, i.e., local stochastic gradient descent (SGD) update, gradient consensus, intra-cluster model aggregation and inter-cluster model aggregation. We prove the convergence of our proposed asynchronous MD-FEEL algorithm on a non-convex setting and elaborate on the effect of some hyperparameters. Empirically, we evaluate our proposed asynchronous MD-FEEL on the MNIST and CIFAR-10 datasets. The simulations show that our proposed asynchronous MD-FEEL can perform better in terms of convergence speed and generalization performance than some benchmark algorithms.

In Federated Learning (FL), with parameter aggregated by a central node, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the single point of failure within the FL framework, recent studies have introduced Decentralized Federated Learning (DFL) as a viable alternative. Considering the device heterogeneity, and energy cost associated with parameter aggregation, in this paper, the problem on how to efficiently leverage the limited resources available to enhance the model performance is investigated. Specifically, we formulate a problem that minimizes the loss function of DFL while considering energy and latency constraints. The proposed solution involves optimizing the number of local training rounds across diverse devices with varying resource budgets. To make this problem tractable, we first analyze the convergence of DFL with edge devices with different rounds of local training. The derived convergence bound reveals the impact of the rounds of local training on the model performance. Then, based on the derived bound, the closed-form solutions of rounds of local training in different devices are obtained. Meanwhile, since the solutions require the energy cost of aggregation as low as possible, we modify different graph-based aggregation schemes to solve this energy consumption minimization problem, which can be applied to different communication scenarios. Finally, a DFL framework which jointly considers the optimized rounds of local training and the energy-saving aggregation scheme is proposed. Simulation results show that, the proposed algorithm achieves a better performance than the conventional schemes with fixed rounds of local training, and consumes less energy than other traditional aggregation schemes.

In software-defined satellite information networks (SD-SINs), each requested service can be characterized by a predetermined sequence of virtual network functions (VNFs), referred to as a service function chain (SFC). However, VNFs shared by multiple requested services are prone to failures, causing service interruptions. Furthermore, the rapid movement of satellites results in an intermittent yet predictable network topology. Moreover, efficient use of multi-dimensional heterogeneous resources can enhance reliability and network performance. Therefore, in this paper, we investigate reliability-enhanced network slicing by jointly exploiting communication, storage, and computation resources in time-varying SD-SINs. Specifically, we use the time-expanded graph (TEG) to model time-varying SD-SINs with multi-dimensional heterogeneous resources. Based on TEG, we propose a joint reliability-enhanced VNF deployment and flow routing strategy, formulated as an integer nonlinear programming (INLP) problem, to maximize the number of completed services with reliability requirements. To effectively solve the INLP problem, we propose two novel algorithms: the integer linear programming reformulation (ILPR) algorithm, which achieves optimal solutions but with high complexity, and the LP relaxation-based VNF deployment and routing (LPR-VDR) algorithm, which provides near-optimal solutions with significantly lower complexity. Simulation results demonstrate that the LPR-VDR algorithm performs very closely to the ILPR algorithm.

We investigate the role of channel access schemes in enhancing the timeliness of status updates in sensor networks. Specifically, we model the large-scale sensor network as a Poisson cellular network and derive the network average age of information (AoI) under five different channel access schemes: slotted ALOHA, frame slotted ALOHA, random scheduling, round robin, and channel-aware. These schemes are categorized based on random vs. scheduled access and non-channel-aware vs. channel-aware. Our goal is to investigate when the additional overhead and complexity introduced by scheduling and channel state information (CSI) are beneficial, enabling better decisions in network design. Our findings reveal that the effectiveness of these schemes is influenced by the signal-to-interference ratio (SIR) decoding threshold, which often reflects the length of communication data. For short-packet communications, the performance differences among various channel access strategies are minimal, and the gains from scheduling are limited. Additionally, the inclusion of extra CSI does not yield performance improvements; in fact, some simple scheduling strategies, along with channelaware strategy that leverage CSI, may not outperform basic random access methods. Among the protocols we examined, the round robin scheme achieves the best performance. In contrast, scheduled access schemes exhibit a clear performance advantage in long-packet communications. Furthermore, the channel-aware scheme significantly enhances the network AoI performance, particularly in networks with higher transmitter competition.

The Low-Altitude Wireless Networks (LAWNs) has emerged as a cornerstone of next-generation mobile due to their flexibility and adaptability in providing on-demand connectivity. However, ensuring reliable and high-throughput aerial drone communication remains a major challenge, mainly due to the dynamic mobility of aerial drones and the complexity of the wireless propagation environment. Traditional LAWNs rely heavily on channel sampling and real-time feedback, which introduce latency and communication overhead. In this work, we proposeRadioRS, a novel sampling-free aerial drone communication framework that combines Radio Map (RM) prediction with Rate-Splitting Multiple Access (RSMA) to enable robust and efficient communication without requiring explicit channel estimation during flight. RadioRS leverages a RM that provides location-aware predictions of channel state. To enhance the accuracy and generalization of these predictions under complex propagation conditions, we develop a generative model based on the Mamba architecture, which efficiently captures fine-grained correlations in the radio environment. Building on the RM, RSMA is employed to flexibly manage interference and improve spectral efficiency. In addition, we design a Mamba-powered controller that adapts beamforming strategies from the RM directly, further improving link reliability and throughput. Comprehensive simulation results demonstrate that the proposed RadioRS framework significantly outperforms conventional channel-sampling-based approaches in terms of both communication reliability and throughput.

The Digital Twin (DT) system has become a new paradigm to empower Unmanned Aerial Vehicles (UAV) networks for low-altitude applications, such as parcel delivery. However, due to high computing complexity, traditional DT technology might confront challenges to imitating highly dynamic UAVs in large-scale parcel delivery scenarios. It causes a negative influence on low-latency and high-accuracy delivery. To address the issue, we propose a terminal-edge cooperative multi-scale DT framework. It can perform a cooperative DT implementation with a cross-layer computing resource orchestration based on a multi-scale imitation manner. Explicitly, we propose a graph matching network based DT algorithm to run macro-scale DTs at the edge. It can assist edge UAVs in exploring feasible delivery associations among UAV groups and parcel clusters based on information on UAV topology and parcel destinations for a high successful delivery ratio. We then propose a Competitive and Cooperative Reinforcement Learning (CCRL) based DT algorithm to implement micro-scale DTs at the terminal. It can enable UAVs to implement low-latency delivery by optimizing delivery paths with low energy consumption. We demonstrate the effectiveness of the proposed framework with verifications under multiple metrics. The results show that our solution provides a real-time UAV delivery performance, with up to 94% successful delivery ratio, under a low system latency compared to the state-of-the-art solutions.

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly uses two localization methods: 1) generative adversarial network (GAN) based positioning method and 2) time difference of arrival (TDOA) based positioning method. Since GAN-based method cannot defend against a strong jamming signal while TDOA-based method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to localize the target UAV. This problem is formulated as an optimization problem. The aim of this problem is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model based collaborative reinforcement learning (RL) method which enables the active UAV to optimize its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the UAVs movement and the unknown jamming attack pattern. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

The rising popularity of Internet of things (IoTs) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and intelligent applications across various domains, IoT service providers must gather vast amounts of sensitive data from users, which nonetheless concomitantly raises concerns about privacy breaches. Federated learning (FL) has emerged as a promising decentralized training paradigm to tackle this challenge. This work focuses on enhancing the aggregation efficiency of distributed local models by introducing over-the-air computation into the FL framework. Due to radio resource scarcity in large-scale networks, only a subset of users can participate in each training round. This highlights the need for effective user scheduling and model transmission strategies to optimize communication efficiency and inference accuracy. To address this, we propose an integrated approach to user scheduling and receive beam steering, subject to constraints on the number of selected users and transmit power. Leveraging the difference-of-convex technique, we decompose the primal non-convex optimization problem into two sub-problems, yielding an iterative solution. While effective, the computational load of the iterative method hampers its practical implementation. To overcome this, we further propose a low-complexity user scheduling policy based on characteristic analysis of the wireless channel to directly determine the user subset without iteration. Extensive experiments validate the superiority of the proposed method in terms of aggregation error and learning performance over existing approaches.

Emerging mega-constellations with numerous Low Earth Orbit (LEO) satellites actively provide pervasive Internet services worldwide, which are usually considered crucial components of Non-Terrestrial Networks (NTNs). However, the high mobility and limited coverage of LEO satellites introduce frequent handovers, causing network interruptions and degrading Quality of Service (QoS). While many efforts have been made to alleviate the impact of handovers on service provisioning from NTNs, they usually assume channel conditions are pre-determined and remain unchanged as satellites move, which is different from real situations and thus may experience significant performance degradation compared to theoretical analysis. In this paper, we proposeOracleto promise QoS-aware service provisioning in NTNs under dynamic channel conditions. Specifically, we mathematically formulate a channel model to characterize dynamic channel conditions in NTNs and develop a QoS maximization problem considering handover frequency and transmission capacity. To accommodate the dynamic nature of NTNs, we introduce a Model Predictive Control (MPC)-based controller to predict future network status and generate control strategies correspondingly, and leverage Digital Twin (DT) for real-time network status consideration. For higher efficiency, we further employ Generative Artificial Intelligence (GAI) with a safe transfer learning-based framework to enhance model adaptivity to environmental uncertainties and ensure feasible control decisions in real-world NTNs. Extensive simulation results under the real-world constellation demonstrate thatOraclecan enhance up to$3\times $QoS during online service provisioning.

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation; ii) Even though the introduction of reconfigurable intelligent surfaces (RISs) can overcome the performance degradation caused by serve path loss, the location of users and RISs as well as their densities incur a significant impact on the coverage and rate performance; iii) When the RISs’ density is very high, i.e., the network becomes extremely dense, a user sees several line-of-sight RISs and thus experiences significant interference, which degrades the system performance. Motivated by the challenges above, we first analyze distributed multi-RIS-aided mmWave communication system over Nakagami-mfading from the stochastic geometry perspective. To be specific, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in terms of the E2E SINR coverage probability and rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when deploying a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the RIS and destination. Furthermore, we optimize the multi-RIS-user association based on the physical distances between the RISs and destination by exploiting the maximum-ratio transmission. Numerical and simulation results indicate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

Terahertz communication is a pivotal candidate technology for future 6G networks. Deep learning (DL)-based transmission methods can utilize the real-time data to model channel statistics and device imperfections, presenting an effective manner to solve the modeling problems of non-ideality and channel in the terahertz band. However, there still exist several shortages hindering the development of DL-based terahertz communications, that is high system complexity, costly online retraining to fit changing non-ideality and channel conditions, and learned diagrams with high peak-to-average power ratio (PAPR). This paper proposes novel methods to address the above challenges. At first, a new regulated autoencoder (RAE) structure is proposed to fit changing conditions without online retraining. Secondly, a binary neural network (BNN) method is leveraged to reduce the receiver complexity and a lookup table based method to cut down the transmitter complexity. Lastly, a new maximum normalization method is proposed to reduce PAPR of the learned diagram. Extensive simulations are performed to verify the effectiveness of the proposed methods.

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.

This paper investigates the receiver design for clipped orthogonal time frequency space (OTFS) systems, where the user devices are equipped with power amplifiers (PAs) with low dynamic range. To improve power efficiency, the PAs have to work near the saturation points, which leads to unknown nonlinear distortions, thus making the signal detection more challenging. To solve this problem, techniques like intentional clipping or pre-distortion are adopted, thus approximating the outputs of the PAs as clipped signals. To further compensate for the unknown time-varying multipath channel and the clipping distortion at the receiver, the channel and clipping amplitude (CA) estimation, channel tracking, and signal detection are studied in this paper. Firstly, a receiver framework is developed for clipped OTFS. Secondly, by adopting the sparsity of the delay-Doppler (DD) domain channel and the piecewise linearized signal model with respect to CA, a novel sparse Bayesian learning (SBL) based joint channel and CA estimation scheme is proposed. Then, to further reduce the estimation error and bit error rate, a Kalman filter (KF) based channel tracking scheme and a minimum mean square error decision feedback blockwise equalization (MMSE-DFBE) based detection scheme are proposed. These two schemes are integrated in an expectation maximization (EM) based iterative tracking and detection algorithm. Finally, numerical simulations are conducted to demonstrate the superiority of the proposed schemes in terms of both estimation error and bit error rate.

This work considers the problem of remote estimation of Wiener processes and proposes a sample preprocessing method to ensure the convergence of the estimation distortion. Specifically, the autocorrelation of the Wiener process is exploited to counteract the effect of strong quantization noise arising from the linearly increasing variance over time. We first derive the exact expression for the convergent mean squared error (MSE) without considering transmission outages to explain the proposed preprocessing method. Then, the analysis is extended to more complex and general scenarios with outages. Based on the derived MSE, the quantization precision, the sampling interval, and the transmission time of a single piece of update information are optimized individually. We further give two algorithms to obtain two global suboptimal MSEs for practical cases considering low thresholds of quantization precision and sampling interval, following a demonstration of the unsolvability of the joint optimization. The numerical results reveal that dynamic distortion plays a greater role than static distortion due to the fast-varying nature of the Wiener process, which also verifies the effectiveness of the proposed preprocessing method in controlling the quantization error.

This paper proposes a novel diffusion-enabled pluggable encryption/decryption modules design against semantic eavesdropping, where the pluggable modules are optionally assembled into the semantic communication system for preventing eavesdropping. Inspired by the artificial noise (AN)-based security schemes in traditional wireless communication systems, in this paper, AN is introduced into semantic communication systems to prevent semantic eavesdropping. However, the introduction of AN also poses challenges for the legitimate receiver in extracting semantic information. Recently, denoising diffusion probabilistic models (DDPM) have demonstrated their powerful capabilities in generating multimedia content. Here, the paired pluggable modules are carefully designed using DDPM. Specifically, the pluggable encryption module generates AN and adds it to the output of the semantic transmitter, while the pluggable decryption module before semantic receiver uses DDPM to generate the detailed semantic information by removing both AN and the channel noise. In the scenario where the transmitter lacks eavesdropper’s knowledge, the artificial Gaussian noise (AGN) is used as AN. We first model a power allocation optimization problem to determine the power of AGN, in which the objective is to minimize the weighted sum of data reconstruction error of legal link, the mutual information of illegal link, and the channel input distortion. Then, a deep reinforcement learning framework using deep deterministic policy gradient is proposed to solve the optimization problem. In the scenario where the transmitter is aware of the eavesdropper’s knowledge, we propose an AN generation method based on adversarial residual networks (ARN). Unlike the previous scenario, the mutual information term in the objective function is replaced by the confidence of eavesdropper correctly retrieving private information. The adversarial residual network is then trained to minimize the modified objective function. Simulation results show that the diffusion-enabled pluggable encryption module prevents semantic eavesdropping with high covertness while the pluggable decryption module achieves the high-quality semantic communication.

Efficient resource utilization is vital for terrestrial and non-terrestrial networks (TN-NTN) integration. However, different spatio-temporal resource scales in TN and NTN networks pose challenges for joint resource allocation. To tackle this problem, we propose a joint user scheduling and multi-domain resource allocation scheme in the downlink network, to improve coverage for ground users (GUs). Specifically, the scheme is designed in two time-scales, including large-scale satellite beam-hopping (i.e., spatial resource allocation) and small-scale time-frequency resource allocation. For beam-hopping, we first analyze the coverage of terrestrial base stations (TBS) for GUs, and accordingly propose a joint design of user scheduling and beam-hopping. For time-frequency resource allocation, to cope with the complexity induced by multi-domain and multi-scale resources, we propose a two-step approach which first obtains a preliminary allocation with worst-case co-frequency interference assumption, then employs the genetic algorithm to re-allocate redundant resources, thereby increasing the proportion of successfully served GUs. We evaluate the performance of the proposed scheme through simulations with different user demands and network service settings. Results show that the proposed scheme provides considerable improvement over existing schemes, which can efficiently reduce co-frequency interference and provide service for more GUs.

With the rapid expansion of Internet of Things (IoT) applications, the demand of wide coverage and massive connectivity is inevitable. In this context, this paper investigates massive unsourced random access (URA) paradigm for low earth orbit (LEO) satellite IoT applications, focusing on device separation and signal detection. By exploiting the structured Grassmannian constellation to generate the codebook, a tensor-based URA transmission scheme is provided, which models the separation and detection problem as a general canonical polyadic (CP) decomposition. Then, to evaluate the access capability of our considered URA scheme, a comprehensive uniqueness analysis considering both sufficient conditions and necessary conditions is presented. Accordingly, an efficient generalized line-search-accelerated alternating least squares (GLSA-ALS) method is proposed to conduct the device separation and signal detection, which can avoid a large number of inverse computations for large-scale matrices. To be specific, with the help of the relaxation factors during the iteration, our proposed method can converge at a fast speed with negligible performance loss, which facilitates a better trade-off between the detection accuracy and computational complexity. Furthermore, depending on the demand of a specific application scenario, the flexible selection of relaxation factors enables the proposed method to be compatible to the classical ALS method, which can enhance the performance at the cost of additional complexity. Finally, relying on the maximum likelihood (ML)-based detection approach, the message list transmitted by active devices from one common codebook can be recovered. Simulation results demonstrate that the proposed GLSA-ALS method outperforms the state-of-the-art methods for practical LEO satellite IoT applications.

This paper studies the problem of long-term, high-quality data collection in dynamic industrial environments using autonomous mobile robots (AMRs). We formulate a max–min data-rate optimization problem that jointly designs AMR trajectories, receive beamforming, sensor association, and decoding policy under mobility, communication, and battery constraints. The problem is challenging because motion decisions directly influence link quality, which affects decoding and beamforming efficiency, while all of these factors are tightly coupled with battery dynamics, resulting in a mixed-integer, time-varying, and non-convex design. To handle this strong coupling and the need for scalable long-horizon decision-making, we propose a semi-distributed reinforcement learning framework that combines cloud-level global coordination with fog-level local adaptability. In this framework, a cloud-layer deep Q-network determines AMR-sensor associations, and a fog-layer federated actor-critic algorithm jointly designs continuous trajectories and beamforming. A Fubini–Study distance-assisted k-means clustering method enhances multi-antenna directional gains, and next-generation multiple access (NGMA) improves spectral efficiency and interference suppression. Theoretical analysis confirms the convergence and computational efficiency of the proposed algorithm. Simulations show that the proposed approach achieves faster and more stable convergence, sustains large-scale coverage through periodic recharging, and significantly improves the minimum data rate compared with orthogonal multiple access-based baselines.

Direct localization (DL) of high-order quadrature amplitude modulation (QAM) sources is a pivotal challenge in wireless communications, particularly in environments characterized by complex multipath propagation and the presence of multiple sensor array-based anchors. This paper introduces a novel solution based on dual atomic norm minimization (DANM) framework that capitalizes on the fourth-order cumulant property of QAM signals to suppress Gaussian noise and expand the effective array aperture. Unlike traditional DL frameworks based on discrete Fourier transform (DFT) and spatial smoothing pre-processing (SSP) techniques, the proposed framework enhances localization accuracy and improves robustness against multipath effects. By framing the localization problem as a semidefinite program that utilizes dual atomic norm properties, our solution eliminates the need for prior knowledge of the number of sources and achieves a favorable balance between computational complexity and localization performance. Simulation results reveal that the DANM-based DL algorithm outperforms existing DFT- and SSP-based DL methods in terms of localization accuracy, with its root mean square error (RMSE) closely approaching the Cramér-Rao bound (CRB) even under challenging conditions. These findings underscore the potential of DANM in advancing high-precision DL for high-order QAM sources, thereby paving the way for more reliable and precise wireless communication systems.

The reconfigurable intelligent surface (RIS)-aided cognitive radio (CR) system holds significant promise for enhancing spectrum utilization. However, its practical implementation hinges critically on accurate channel state information (CSI). Obtaining full cascaded CSI in RIS-aided CR systems with the cross interference between multiple cascaded channels is challenging. To fill this gap, we propose the deep learning-based channel acquisition schemes for the users in static scenario and mobile scenario, respectively. In static scenario, we propose a novel deep neural network (DNN)-based channel estimation scheme named dual output parameter estimation (DOPE). This scheme achieves remarkable normalized mean square error (NMSE) performance in CSI estimation while significantly reducing the required pilot overhead. In mobile scenario, we propose a channel prediction scheme with hybrid recurrent neural network (RNN) and Transformer (HRT-CP). This scheme utilizes RNN to extract dynamic and static features of cascaded channels, and introduces Transformer’s powerful parallel processing capability to efficiently predict dynamic features. By combining static and dynamic features appropriately, the HRT-CP scheme has predicted the future cascaded CSI accurately, and mitigated the error accumulation phenomenon effectively. The simulation results under both near-field and far-field channel models demonstrate that our proposed schemes provide significant gains on NMSE performance compared to other benchmarks.

While spatial, temporal, and frequency domains are explored to enhance spectral efficiency (SE) in wireless communications, utilizing the polarization of electromagnetic (EM) waves also contributes to achieving this goal. Unlike conventional reconfigurable intelligent surface (RIS)-aided communications, polarization-transforming RIS (PTRIS) is first applied in this work to assist the line-of-sight (LoS) communication system. We introduce a novel framework for accurately modeling the direct and cascaded channels in the PTRIS-aided LoS communication system. This framework considers the spatial positions of the transmitter and receiver, the radiation patterns, antenna rotations, and the physical propagation mechanisms of EM waves based on antenna theory. The aperture field method is used to model the physical reflection of EM waves by PTRIS. Additionally, we aim to maximize SE by investigating four cases regarding the polarization-transforming capability of the PTRIS. Optimal closed-form solutions are derived for Case 1 and Case 2, while a best-effort approximation-based alternative optimization (BEA-AO) method is proposed for Case 3 to obtain sub-optimal solutions. Case 4 can be solved optimally with the barnch-and-cut algorithm within a reasonable computation time. Numerical results demonstrate that PTRIS can provide a robust and enhanced SE in LoS communications, even with arbitrary antenna rotations, compared to the scenarios without PTRIS.

To meet the growing demand for communication capacity and address the scarcity of wireless spectrum resources, we propose a novel orthogonal time frequency space (OTFS) channel estimation scheme enhanced by visual environment semantics. This approach establishes a theoretical foundation for semantic-assisted channel estimation by modeling the potential relationship between the wireless communication channel and its surrounding environment. Leveraging a computer vision-based adaptive environment semantic sensing framework, the system extracts and processes environment features to infer channel characteristics. To tackle the challenge of capturing small-scale fading solely through visual environment semantics, we design two new pilot structures and the corresponding channel estimation methods. These are tailored to maximize the utility of information derived from the environment while minimizing pilot overhead. The simulation results demonstrate that the proposed scheme outperforms the conventional channel estimation method in terms of spectral efficiency and robustness in high-mobility and low-SNR scenarios with fewer pilot symbols.

Massive random access is an important technology for achieving ultra-massive connectivity in next-generation wireless communication systems. It aims to address key challenges during the initial access phase, including active user detection (AUD), channel estimation (CE), and data detection (DD). This paper examines massive access in massive multiple-input multiple-output (MIMO) systems, where deep learning is used to tackle the challenging AUD, CE, and DD functions. First, we introduce a Transformer-AUD scheme tailored for variable pilot-length access. This approach integrates pilot length information and a spatial correlation module into a Transformer-based detector, enabling a single model to generalize across various pilot lengths and antenna numbers. Next, we propose a generative diffusion model (GDM)-driven iterative CE and DD framework. The GDM employs a score function to capture the posterior distributions of massive MIMO channels and data symbols. Part of the score function is learned from the channel dataset via neural networks, while the remaining score component is derived in a closed form by applying the symbol prior constellation distribution and known transmission model. Utilizing these posterior scores, we design an asynchronous alternating CE and DD framework that employs a predictor-corrector sampling technique to iteratively generate channel estimation and data detection results during the reverse diffusion process. Simulation results demonstrate that our proposed approaches significantly outperform baseline methods with respect to AUD, CE, and DD.

Beamforming is a key technology for achieving integrated sensing and communication (ISAC). However, most existing works focus on mono-static sensing, which has limited sensing accuracy and strong self-interference. To address these issues, this paper investigates hybrid beamforming (HBF) design for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) ISAC system with multi-static cooperative localization. Specifically, one access point (AP) simultaneously forms communication beams to serve multiple user equipments (UEs) and a sensing beam towards one target, and other multiple distributed APs perform cooperative localization on the target by estimating the angle-of-arrivals (AOAs) of received echo signals. First, to characterize the target localization accuracy, we derive the squared position error bound (SPEB) of AOA-based multi-static cooperative localization. Then, two HBF optimization problems are formulated to investigate the performance tradeoff between sensing and communication. For the sensing-centric design, we aim to minimize the SPEB of target localization while ensuring the signal-to-interference-plus-noise ratio (SINR) requirements of individual UEs. To tackle this nonconvex problem, we propose a semidefinite relaxation (SDR)-based alternating optimization algorithm. For the communication-centric design, a fractional programming (FP)-based alternating optimization algorithm is proposed for solving the communication sum-rate maximization problem under the sensing SPEB constraint. Simulation results demonstrate that the proposed two HBF algorithms can achieve localization accuracy and sum-rate performance close to fully-digital beamforming counterparts and outperform other baseline schemes.

As a typical scenario for the 6th Generation mobile communication systems (6G), Hyper Reliable Low Latency Communication (HRLLC) is expected to ensure extremely low delay and high reliability, while supporting wireless transmission of large-scale massive data. However, existing communication networks face the dual challenges of inadequate performance metrics and limited network resources. Therefore, this paper proposes the Flexible Bit and Semantic on-demand Transmission (FBST) framework, including three key technologies: adaptive transmission mode decision, flexible transmission time interval scheduling, adjustable semantic compression ratio. The FBST framework could satisfy the strict QoS requirements of users and provide on-demand services for users. Based on the Stochastic Network Calculus (SNC) modeling method, we conduct precise delay analysis and provided a general expression for the delay violation probability of the α - κ - μ channel, which could be extended to various complex channels. In addition, the Knowledge-base Parameterized Deep Q-Network (KP-DQN) algorithm is proposed to solve the resource allocation issue, which is a mixed action space problem with complex calculations caused by SNC. Finally, the simulation results show that FBST framework could satisfy extremely strict delay and reliability requirements of users, and the KP-DQN algorithm improving operational efficiency by over 76.8%.

In this paper, we pay our attention towards secure and robust communication in the presence of a Reconfigurable Intelligent Surface (RIS)-enhanced mobile eavesdropping attacker in Multiple-Input Multiple-Output (MIMO) wireless networks. Specifically, we first provide a unifying framework that generalizes specific intelligent wiretap model wherein the passive eavesdropper configured with any number of antennas is potentially mobile and can actively optimize its received signal strength with the help of RIS by intelligently manipulating wiretap channel characteristics. To effectively mitigate this intractable threat, we then propose a novel and lightweight secure communication scheme from the perspective of information theory. The main idea is that the data processing can in some cases be observed as communication channel, and a random bit-flipping scheme is then carefully involved for the legitimate transmitter to minimize the mutual information between the secret message and the passive eavesdropper’s received data. The Singular Value Decomposition (SVD)-based precoding strategy is also implemented to optimize power allocation, and thus ensure that the legitimate receiver is not subject to interference from this random bit-flipping. The corresponding results depict that our secure communication scheme is practically desired, which does not require any a prior knowledge of the eavesdropper’s full instantaneous Channel State Information (ICSI). Perfect acquisition of ICSI is clearly always not affordable, which is further exacerbated by the RIS involved and the potential mobility of the passive eavesdropper that leads to unavoidable fast fading channels. Furthermore, we consider the RIS optimization problem from the eavesdropper’s perspective, and provide RIS phase shift design solutions under different attacking scenarios. Finally, the optimal detection schemes respectively for the legitimate user and the eavesdropper are provided, and comprehensive simulations are presented to verify our theoretical analysis and show the effectiveness and robustness of our secure communication scheme across a wide range of attacking scenarios.

The rise of heterogeneous aerial and space platforms within Space-Air-Ground Integrated Networks (SAGINs) introduces significant challenges, as the limited spectrum resources force these platforms to operate within shared frequency bands, resulting in co-existing systems. Effective interference management in such networks requires both the design of communication channels and the dynamic mitigation of interference between them. Prior research has largely focused on interference mitigation with fixed communication links, often overlooking adaptive channel selection, which can result in performance degradation. In this study, we address this limitation by introducing MetaRS, an innovative, self-intelligent rate-splitting solution designed for more flexible interference management in co-existing SAGINs. MetaRS enables adaptive channel and communication scheme selection, by leveraging a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA)-based framework enhanced with a one-pass diffusion model. Specifically, the FD-RSMA-based framework allows MetaRS to dynamically shift its interference management strategy according to the current network status. The integration of the diffusion model further enhances MetaRS by allowing it to recognize and adapt to real-time channel conditions and user deployment, thereby enabling self-intelligent interference mitigation. Simulation results demonstrate that MetaRS significantly outperforms conventional SDMA, RSMA, and FD-RSMA approaches. This improvement stems from MetaRS’s joint optimization of channel selection and its adaptive, intelligent interference management capabilities, which effectively balance channel utilization and mitigate interference in complex, multi-platform environments.

The challenge of mitigating interference in Space-Air-Ground Integrated Networks (SAGINs) is exacerbated by the inherent channel uncertainty, which arises due to dynamic weather conditions, heterogeneous user deployment, and different altitude of transmitters. To tackle this problem, Rate-Splitting Multiple Access (RSMA) has been seen as a promising solution due to its robustness. However, conventional beamforming designs for RSMA often suffer from two major limitations: high processing delays and overfitting to specific channel conditions. When the channel conditions change, the performance of these predictors degrades significantly, limiting their effectiveness in dynamic environments. To address these challenges, we propose a novel Fast-Adaptive Predictive Beamforming (FA-PB) framework for RSMA in SAGINs. Unlike traditional predictive beamforming approaches that rely on fixed predictive models, FA-PB integrates a transfer-learning-based online learning mechanism. This innovative approach allows the predictor to dynamically adapt to new channel conditions with minimal computational overhead. FA-PB achieves this by leveraging few-shot Channel State Information at the Transmitter (CSIT) samples, enabling real-time updates and adjustments to the predictor. Consequently, FA-PB ensures that the beamforming process can rapidly adapt to fluctuating channel conditions, maintaining high levels of performance even in highly dynamic SAGIN environments. Extensive simulation results validate the superiority of the FA-PB framework, demonstrating its enhanced adaptability and improved beamforming performance in SAGINs.

Prior studies on mixed near-field and far-field communications have focused exclusively onsingle-cellscenarios, where both near-field and far-field users are served by the same base station (BS), leading tointra-cellmixed-field interference. In this paper, we consider a more general and practicalmulti-cell mixed-fieldscenario consisting of multiple cells, each serving multiple users, thus resulting in more complexinter-cellmixed-field interference. To address this new challenge, we propose leveragingrotatable antenna(RA) technology to enhance multi-cell mixed-field communication performance by exploiting the additional spatial degree-of-freedom (DoF) introduced by RA rotation to mitigate interference in an efficient way. Specifically, we study an RA-enabled multi-cell mixed-field communication system in which each BS is equipped with an RA array to serve its associated users. We formulate a network-wide sum-rate maximization problem that jointly optimizes the transmit beamforming and the rotation angles of the RA arrays, subject to per-BS power constraints and admissible array rotation limits. To gain useful insights into the role of RAs in multi-cell mixed-field communications, we first analyze a special case with a single user per cell. For this case, we obtain a closed-form expression for therotation-awareinter-cell mixed-field interference using the Fresnel integrals and analytically show that RA rotation can effectively mitigate such interference, thereby substantially improving system performance. For the general case with multiple users per cell, we develop an efficientdouble-layeralgorithm: the inner layer optimizes the transmit beamforming at each BS via semidefinite relaxation (SDR) and successive convex approximation (SCA); while the outer layer determines the rotation angles of the RA arrays using particle swarm optimization (PSO). Numerical results demonstrate that RA-enabled multi-cell systems achieve significant performance gains over conventional fixed-antenna systems, and the proposed joint design consistently outperforms various benchmark schemes.

Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang, Ziyang Wang, Tony Quek, Joey Tianyi Zhou, Soujanya Poria, Zuozhu Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

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.

As communications evolve into the terahertz (THz) band, the near-field region expands accordingly, providing additional distance-domain information that can be exploited for high-accuracy sensing. While the ultra-short THz wavelength offers high resolution, it in turn narrows the unambiguous estimation range and demands large-scale arrays for path loss compensation. Sparse arrays (SAs) are considered to reduce the cost of massive elements, but inevitably cause performance degradation. Moreover, near-field channels exhibit complex coupling among time, frequency, and angular parameters, complicating sensing information extraction. To address these challenges, we propose a coarse-to-fine velocity estimation algorithm, enabled by a tensor-based near-field channel decomposition, while balancing complexity through SAs. Simulation results show that the proposed scheme achieves near-CRB performance with an extended unambiguous speed range. Furthermore, analysis reveals that SAs significantly reduce the computational complexity, and the resulting performance loss can be effectively mitigated by moderately increasing the bandwidth or signal duration, enabling high-accuracy, low-complexity velocity estimation.

Mobile devices increasingly rely on deep neural networks (DNNs) for complex inference tasks, but running entire models locally drains the device battery quickly. Offloading computation entirely to cloud or edge servers reduces processing load at devices but poses privacy risks and can incur high network bandwidth consumption and long delays. Split computing (SC) mitigates these challenges by partitioning DNNs between user equipment (UE) and edge servers. However, 5G wireless channels are time-varying and a fixed splitting scheme can lead to sub-optimal solutions. This paper addresses the limitations of fixed model partitioning in privacy-focused image processing and explores trade-offs in key performance metrics, including end-to-end (E2E) latency, energy consumption, and privacy, by developing an adaptive ML partitioning scheme based on realtime AI-powered throughput estimation. Evaluation in multiple scenarios demonstrates significant performance gains of our scheme.

Terahertz (THz) integrated sensing and communication (ISAC) is a promising enabler for 6G networks, offering ultra-high data rates and environment-aware capabilities. However, realizing its full potential requires accurate construction of directional THz radio maps and environment map from sparse and noisy signal measurements, which is a highly ill-posed problem. While recent generative models, particularly conditional diffusion models, show promise in radio map construction, they fail to capture the physical characteristics of THz signal propagation, limiting generalization. To address this, we propose a THz propagation-informed diffusion model that jointly generates multi-directional radio maps and infers the environment map via an image-intersection strategy. Crucially, our model embeds two novel physics-guided loss functions: the intra-beam propagation-informed loss and inter-beam environmental consistency loss, which enforce geometric and semantic fidelity on THz propagation behaviors into the training process. Simulation results demonstrate superior performance over existing methods across varying sensor densities and environment complexities.

Edge intelligence has emerged as a promising strategy to deliver low-latency and ubiquitous services for mobile devices. Recent advances in fine-tuning mechanisms of foundation models have enabled edge intelligence by integrating low-rank adaptation (LoRA) with federated learning. However, in wireless networks, the device heterogeneity and resource constraints on edge devices pose great threats to the performance of federated fine-tuning. To tackle these issues, we propose to optimize federated fine-tuning in heterogenous wireless networks via online learning. First, the framework of switching-based federated fine-tuning in wireless networks is provided. The edge devices switches to LoRA modules dynamically for federated fine-tuning with base station to jointly mitigate the impact of device heterogeneity and transmission unreliability. Second, a tractable upper bound on the inference risk gap is derived based on theoretical analysis. To improve the generalization capability, we formulate a non-convex mixed-integer programming problem with long-term constraints, and decouple it into model switching, transmit power control, and bandwidth allocation subproblems. An online optimization algorithm is developed to solve the problems with polynomial computational complexity. Finally, the simulation results on the SST-2 and QNLI data sets demonstrate the performance gains in test accuracy and energy efficiency.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, assisted by an existing receive access point. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme, where the beamforming and placement of the UAV are dynamically adjusted in different stages. Specifically, in the first stage, without prior knowledge about the target’s location, the UAV fixes at the initial location and performs wide beam sensing to probe the target. In the following stages, given the coarse estimation result of the target’s location (obtained in the previous stage), the UAV adjusts its location and performs narrow beam sensing to locate the target. Besides, the quality of service requirements of the users are guaranteed in all stages. Based on the proposed sensing scheme, we formulate and solve two optimization problems to improve the sensing accuracy. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate a joint communication and computation resource allocation strategy for an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system employing fluid antenna (FA). Specifically, each user is equipped with an FA to offload the entire computation tasks to the MEC server deployed on the UAV. By dynamically selecting antenna ports, users can achieve latency-efficient edge computing services, especially advantageous in dynamic environments. To minimize the maximum execution delay of all the users, we jointly optimize the UAV location, FA port selection, and computation resource allocation, subject to computational capacity constraints. The original non-convex optimization problem is decomposed into three tractable subproblems within a block coordinate descent (BCD) algorithm. The optimal computing frequencies are derived in closed form, while the UAV location and FA port selection are optimized using low-complexity iterative algorithms based on successive convex approximation (SCA) and linear programming (LP) techniques. In addition to conventional benchmarks with fixed-position antennas (FPAs), we also introduce a reconfigurable intelligent surface (RIS)-assisted system as a comparative baseline. Simulation results demonstrate that the proposed FA-assisted scheme significantly outperforms both FPAs and RIS-assisted counterparts, with performance gains becoming more pronounced in multi-task and highly dynamic scenarios, establishing FA-assisted UAV-MEC as a promising solution for future deployments.

The integration of LEO satellite onboard processing with federated learning has propelled a promising paradigm: satellite federated learning (SFL), by empowering onboard machine learning (ML) to provide various intelligent services. Especially, the decentralized satellite federated learning (DSFL), where each LEO satellite serving as a client exploits one-hop inter-satellite links (ISLs) to exchange local models, could alleviate the reliance on the centralized ground server, and reduce multi-hop model transmissions. In a DSFL system, the scheduling of model training and transmission significantly affects the learning performance. Thus it is crucial yet challenging to design an efficient scheduling strategy for the dynamic LEO satellite networks with heterogeneous onboard datasets. In this paper, we propose a contact-based DSFL framework, where each contact between a pair of satellites is regarded as a collaboration opportunity to exchange local models. Under this framework, we formulate a problem of optimizing the scheduling strategy with the aim of maximizing the DSFL model accuracy. To solve this problem, we design a Double deep$Q$learning (DDQN) based scheduling strategy, which schedules the local model training and transmission upon each contact by leveraging the instantaneous environmental information, such as model accuracy, the neighbor's model accuracy, and available training time. Simulation results demonstrate the effectiveness and efficiency of the proposed DDQN-based scheduling strategy over three baselines.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, the densely populated users and diverse services with high data rate demands has triggered an increasing scarcity of UAV spectrum utilization. To tackle this problem, it is promising to incorporate the underutilized unlicensed spectrum with the licensed spectrum to boost network capacity. However, the openness of unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a spectrum sharing UAV network coexisting with licensed cellular network and unlicensed Wi-Fi network is considered with the anti-jamming technique in this paper. The sum rate maximization of the secondary network is studied by jointly optimizing the transmit power, subchannel allocation, and UAV trajectory. We first decompose the challenging non-convex problem into two subproblems, 1) the joint power and subchannel allocation and 2) UAV trajectory design subproblems. A low-complexity iterative algorithm is proposed in a alternating optimization manner over these two subproblems to solve the formulated problem. Specifically, the Lagrange dual decomposition is exploited to jointly optimize the transmit power and subchannel allocation iteratively. Then, an efficient iterative algorithm capitalizing on successive convex approximation is designed to get a suboptimal solution for UAV trajectory. Simulation results demonstrate that our proposed algorithm can significantly improve the sum transmission rate compared with the benchmark schemes.

To satisfy the communication, localization, and energy demands of low-power IoT nodes, combining integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) can offer an innovative solution. However, carrying private information in wireless sensing beams critically increases the vulnerability of being eavesdropped by the target. In this paper, we propose a non-orthogonal multiple access (NOMA)-enhanced secure transmission strategy to counteract the internal eavesdropping for SWIPT-ISAC. We jointly optimize the transmit beamforming and power splitting to maximize the secrecy rate towards the eavesdropping by the target, ensuring the nonlinear energy harvesting (EH) and precise sensing beampatterns. Our approach enables the nodes to manage the interference through successive interference cancellation and to harvest energy from the transmitted signal. The original optimization problem is non-convex and difficult to tackle. Using the semidefinite relaxation, we convert it to convex form, and propose alternating optimization algorithm to derive the solutions. Simulation results indicate that the proposed scheme can effectively counteract the internal eavesdropping, while ensuring the nonlinear$\mathbf{E H}$and sensing performance.

Six-dimensional movable antenna (6DMA) is an innovative and transformative technology to improve wireless network capacity by adjusting the 3D positions and 3D rotations of antennas/surfaces (sub-arrays) based on the channel spatial distribution. For optimization of the antenna positions and rotations, the acquisition of statistical channel state information (CSI) is essential for 6DMA systems. In this paper, we unveil for the first time a new directional sparsity property of the 6DMA channels between the base station (BS) and the distributed users, where each user has significant channel gains only with a (small) subset of 6DMA position-rotation pairs, which can receive direct/reflected signals from the user. By exploiting this property, a covariance-based algorithm is proposed for estimating the statistical CSI in terms of the average channel power at a small number of 6DMA positions and rotations. Based on such limited channel power estimation, the average channel powers for all possible 6DMA positions and rotations in the BS movement region are reconstructed by further estimating the multi-path average power and direction-of-arrival (DOA) vectors of all users. Simulation results show that the proposed directional sparsitybased algorithm can achieve higher channel power estimation accuracy than existing benchmark schemes, while requiring a lower pilot overhead.

In this paper, the problem of using active unmanned aerial vehicles (UAVs) and a base station (BS) to jointly localize a target UAV under global positioning system (GPS) spoofing attacks is studied. In the considered model, active UAVs transmit signals, which will be reflected by the target UAV and received by active UAVs. Based on the signal transmission time, active UAVs calculate the distance between the target UAV and active UAVs. Then, active UAVs transmit these distance measurement information and their GPS information to the BS for localizing the target UAV. During the localization process, the target UAV is equipped with a GPS jammer, which can interfere with GPS information of active UAVs. Since the localization accuracy depends on distance between the target UAV and active UAVs and GPS information accuracy of active UAVs, active UAVs must optimize their trajectories and determine whether to transmit measurement information to the BS for localizing the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error of the target UAV between the estimated and true positions of the target UAV by jointly optimizing the trajectories of active UAVs and determining distance information transmission scheme. To find the optimal solution, a historical observations and actions-based reinforcement learning (HOA-RL) method is proposed. Compared to traditional state-based reinforcement learning (RL) methods, the proposed method can capture the historical decision-making process of agents and optimally adjust trajectories of active UAVs and measurement information transmission scheme based on their observations. Simulation results show that the proposed method can achieve 44.4% and 70.4% gains in terms of reducing the positioning error of the target UAV compared to Qmix method and Qtran method, respectively.

In this paper, we consider the problem of remote estimation of Wiener processes and propose a sample preprocessing method to ensure the convergence of estimation distortion. The autocorrelation of the Wiener process is exploited to counteract the effect of strong quantization noise arising from the linearly increasing variance over time. We first derive the convergent expression for the mean squared error without considering transmission outages to explain the proposed preprocessing method. Then, the analysis is extended to more complex and general scenarios with outages. Based on the analyses, the quantization precision and the sampling interval are optimized individually.

In this work, we study the sequence reconstruction problem for the single-deletion single-substitution channel, assuming that the transmitted sequence belongs to a$q$-ary code with minimum Hamming distance at least 2, where$q \geq 2$is any fixed integer. Specifically, we prove that for any two$q$-ary sequences of length$n$and with Hamming distance$d \geq 2$, the size of the intersection of their error balls is upper bounded by$2 q n-3 q-2-\delta_{q, 2}$, where$\delta_{i, j}$is the Kronecker delta. We also prove the tightness of this bound by constructing two sequences whose error ball intersection size achieves this bound.

This work investigates the time-dependent statistical characteristics of age of information (AoI) in the real-time Internet of Things systems with wireless periodic status updating. The periodic updating system is modeled as a discrete-time D/G/1/1 non-preemptive queue. The entire AoI process w.r.t. slot is split into a set of slot-specific AoI processes w.r.t. updating period. A Markov high-dimensional age process is introduced to track the evolution of the slot-specific AoI. Accordingly, the time-dependent AoI distribution, expected AoI (EAoI), and the time-average AoI are derived, forming a framework to study the time-dependent AoI statistical characteristics under periodic updating. Numerical results verify the effectiveness of theoretical analyses and usefulness of packet retransmission. It is found that the EAoI varies non-monotonously and greatly w.r.t. slot in an updating period; the EAoI variations are distinct for the update transmission times with different distributions; and the EAoI gains of channel enhancement differ greatly among slots.

Unmanned Aerial Vehicles (UAVs) have been an attractive device to serve target tracking scenarios, such as hit- and-run tracking and border patrol. Nonetheless, it is difficult to implement real-time UAV topology optimization due to communication resources of UAVs and random moving speeds of targets. To address the problem, we propose a Digital Twins-assisted topology optimization framework (TopoDT). We formulate a UAV topology optimization model based on Lyapunov theory in the framework. The model is decoupled into two subproblems using our proposed TopoDT topology optimization algorithm. The DT model can allow UAVs to implement neighbor selection to construct and optimize small-scale local topologies for tracking low-speed moving targets. In addition, it allows UAVs to construct large-scale global topologies for tracking high-speed moving targets based on trajectory derivation. The system simulation results demonstrate that our solution reduces the end-to-end latency by 63.0% while decreasing the hop counts by 50% compared to state-of-the-art benchmarks.

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.

In a practical Mobile Edge Computing (MEC) system, the stochastic arrival and departure of heterogeneous Mobile Devices (MDs) can cause fluctuations in the number of generated tasks to be scheduled over time, with the generated tasks differing in data size, complexity, and delay constraint. In this work, we consider a dynamic Task Offloading (TO) problem aiming at maximizing the long-term average system utility jointly defined by task completion and energy consumption in the MEC system, where the number of MDs to be served varies over time, and the processing of generated tasks may span multiple time slots. Particularly, we first transform the TO problem into a Markov Decision Process (MDP) with time-varying state and action spaces, which cannot be effectively solved by conventional Deep Reinforcement Learning (DRL) algorithms. Then, we propose a state and action spaces adaptive DRL algorithm to efficiently solve the formulated MDP by leveraging the Transformer model. Finally, simulation results demonstrate the superiority of our proposed algorithm over baseline algorithms and emphasize the limitation of the conventional DRL algorithm in handling time-varying state and action spaces.

This paper proposes a framework for applying a novel multi-domain modulation scheme, orthogonal chirp division multiplexing with index modulation (OCDM-IM), in integrated sensing and communication (ISAC) systems by combining OCDM and index modulation (IM). To support simple SISO-ISAC applications, a low-complexity fast Fourier transform (FFT)-based sensing algorithm is developed for the sake of the superior performance of OCDM-IM over traditional OCDM. Building on this, the framework is further extended to more attractive MIMO-ISAC systems in order to achieve improved bit error rate (BER) and a lower peak-to-average power ratio (PAPR) compared to the conventional MIMO-OCDM scheme. For sensing, it enables distance, velocity, and angle estimation by formulating the OCDM-IM waveform within a compressed sensing framework, which is then efficiently solved using the proposed orthogonal matching pursuit (OMP) algorithm. Simulation results confirm that the OCDM-IM waveform enhances communication performance through IM while preserving the promising sensing performance.

Orthogonal frequency division multiplexing (OFD-M) has been widely used in modern communication networks. Notice that OFDM typically relies on (inverse) Discrete Fourier Transform (DFT/IDFT) for processing its waveforms. In this context, we propose a deep learning-based OFDM receiver that uses a deep complex-valued convolutional neural network (DC-CNN) to recover the information bit stream from synchronized time-domain signals without relying on DFT/IDFT. Specifically, a learned linear transform is designed to utilize the cyclic prefix (CP) of OFDM waveforms instead of DFT/IDFT, which presents the ability of DCCNN for complex communication waveforms. To improve the convergence of the training model for the DCCNN-based receiver, a novel transfer learning scheme is developed to train channel equalization and demodulation in two phases. In addition, both the DCCNN equalizer and DCCNN demodulator are trained and tested at different SNRs for Rayleigh fading and noise, and a mixed multiple fading channel model with various delay spreads is utilized to smooth the training loss. Simulation results suggest that our developed DCCNN channel estimator outperforms conventional estimators such as least square (LS), linear minimum mean square error (LMMSE) and low-rank approximation of LMMSE (ALMMSE) in multipath Rayleigh fading models with varying Doppler spreads and delay spreads.

This work explores the data-driven online tracking control problem for linear dynamic systems across multiple-input multiple-output (MIMO) fading channels. Initially, we address the optimal tracking control for a system with known plant dynamics, and design an innovative stochastic-approximation (SA)-based data-driven algorithm that leverage the instantaneous wireless channel state information (CSI). Subsequently, we extend this approach to accommodate unknown plant dynamics by proposing a novel normalized-stochastic-gradient-descent (NSGD)-based algorithm. This algorithm facilitates simultaneous system identification and control in an online setting using the real-time plant state as well as the CSI. Through Lyapunov drift analysis, we establish the asymptotic optimality of our proposed data-driven algorithms. Numerical results and analysis further demonstrate notable performance improvements compared to several leading learning techniques.

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization in OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

This paper investigates the trajectory generation problem for multi-unmanned aerial vehicle (UAV)-enabled uplink data collection. Specifically, we minimize the age-of-information (AoI) and maximize the coverage as well as the amount of collected data by planning the multi- UAV trajectory considering the energy consumption and collisions constraints. Motivated by diffusion models' exceptional generative capabilities, we propose a multi-UAV trajectory generation (MUTG) solution based on soft actor-critic and diffusion to solve the optimization problem. A diffusion model-based predictor is designed to obtain the action policy, where a hierarchical graph-transformer network is developed to extract entities' interactive information as a conditional guide for the diffusion. Numerical results verify the effectiveness and superiority compared with benchmark schemes in terms of average AoI, user coverage and data collection ratio.

Leveraging over-the-air computations for model aggregation is an effective approach to cope with the communication bottleneck in federated edge learning. By exploiting the superposition properties of multi-access channels, this approach facilitates an integrated design of communication and computation, thereby enhancing system privacy while reducing implementation costs. However, the inherent electromagnetic interference in radio channels often exhibits heavy-tailed distributions, giving rise to exceptionally strong noise in globally aggregated gradients that can significantly deteriorate the training performance. To address this issue, we propose a novel gradient clipping method, termed Median Anchored Clipping (MAC), to combat the detrimental effects of heavy-tailed noise. We also derive analytical expressions for the convergence rate of model training with analog over-the-air federated learning under MAC, which quantitatively demonstrates the effect of MAC on training performance. Extensive experimental results show that the proposed MAC algorithm effectively mitigates the impact of heavy-tailed noise, hence substantially enhancing system robustness.

Over-the-air federated learning (OTA FL) provides a joint computation and communication approach to design FL systems with improved efficiency. By leveraging the superposition property of wireless channels, OTA FL enables the automatic aggregation of intermediate parameters-such as gradients-across a large number of clients, significantly reducing communication overhead while concurrently enhancing transmission privacy. However, gradient aggregation requires all clients to use identical model architectures, a condition often impractical in real-world scenarios due to variations in client hardware and computational capabilities. This mismatch can lead to scalability issues and system incompatibilities. To address this challenge, we propose a model-agnostic method based on model prototypes that enables collaborative training across clients with heterogeneous models. The proposed method bypasses the requirements of the conventional model weight/gradient updates with prototype vector aggregation, without requiring the model structures of all clients to be identical. To the best of our knowledge, the proposed method is the first to explore the prototypical model-heterogeneous OTA FL with desirable training performance and extremely low communication cost. We conducted extensive experiments to verify the efficacy of the proposed method. The results show that our approach not only significantly reduces communication overhead but also exploits the superior capabilities of large models to enhance the performance of smaller models.

The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive Multiple-Input Multiple-Output (mMIMO) systems. However, these DAE approaches presented in prior works rely heavily on large-scale data collected through the base station (BS) for model training, thus rendering excessive bandwidth usage and data privacy issues, particularly for mMIMO systems. When considering users’ mobility and encountering new channel environments, the existing CSI feedback models may often need to be retrained. Returning back to previous environments, however, will make these models perform poorly and face the risk of catastrophic forgetting. To solve the above challenging problems, we propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework. Notably, Gossip-GAN enables the CSI feedback training with low-overhead while preserving users’ privacy. Specially, each user collects a small amount of data to train a GAN model. Meanwhile, a fully distributed gossip learning strategy is exploited to avoid model overfitting, and to accelerate the model training as well. Simulation results demonstrate that Gossip-GAN can: 1) achieve a similar CSI feedback accuracy as centralized training with real-world datasets; 2) address catastrophic forgetting challenges in mobile scenarios, and 3) greatly reduce the uplink bandwidth usage. Besides, our results show that the proposed approach possesses an inherent robustness.

The rapid development of the aviation Internet of Things (IoT) has positioned in-flight connectivity (IFC) as one of its critical applications. Space-air–ground integrated networks (SAGINs) are essential for ensuring the performance of IFC by enabling seamless and reliable connectivity. However, most existing research treats satellites merely as transparent forwarding nodes and overlooks their potential caching capabilities to enhance IFC data rates. In this article, we explore an IFC-oriented SAGIN where satellites and ground stations (GSs) work together to transmit content to airborne passengers, thereby facilitating airborne communication. By categorizing files into cached (instantly accessible via satellites) and noncached files (available only through GSs), this article pioneers the integration of multiple intersatellite links (ISLs) into the IFC framework, thus innovating the content delivery process for both types of files. To minimize the average delay of content delivery, we formulate the corresponding optimization problems: 1) for cached files, we propose an exact penalty-based method to determine the satellite association scheme and 2) for noncached files, we present an efficient algorithm based on alternating optimization to jointly optimize satellite association and GS bandwidth allocation. Our proposed framework is low in complexity, paving the way for high-speed Internet connectivity for aviation passengers. Finally, simulation results are provided to demonstrate the effectiveness of our proposed IFC framework for SAGIN.

With the rapid development of large-scale low-Earth orbit (LEO) satellite Internet, very LEO (VLEO) Earth observation constellations are increasingly using intersatellite links (LISLs) for cross-layer access to Internet satellites. It is an effective means to enhance data return throughput. When both observation and communication satellite constellations use lasers for networking, cross-layer access between the VLEO and LEO satellite networks requires reallocating lasers. This reallocation disrupts the original topology and impacts network performance. To maximize the collaborative operational efficiency of the double-layer network, we fundamentally analyze the relationship between topology links, throughput, and delay using graph and queuing theory. Innovatively, we discover the superiority of two axioms in addressing the cross-layer topology optimization problem, including the “Minimum Hop Count” and “Minimum Overlap Path.” Based on these axioms, a many-objective cross-layer topology optimization model is established that considers the hop count and the average link utilization frequency of both VLEO and LEO satellite networks. To reduce the reliance on centralized algorithms on global transmission demand, a local distributed interaction mechanism (LDIM) is proposed for cross-layer LISL establishment. An onboard novel distributed many-objective cross-layer topology optimization (NDMTO) algorithm is also introduced for VLEO satellites to manage access strategies. Finally, we use real data from Typhoon LEKIMA to create a multitask scenario and conduct packet-level simulations based on the Starlink and Dove constellations. The results indicate that, compared to existing benchmarks, the NDMTO algorithm improves data throughput by 26.73% and reduces the average transmission delay of emergency task data by 20.7%.

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.

Integrated sensing and communications (ISAC) has been widely applied in Internet of Things (IoT) networks for its capability to simultaneously support high-performance communication and sensing, and non-orthogonal multiple access (NOMA) is introduced to further improve spectral efficiency and connection density. However, with the deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) and high-frequency (HF) technologies, the near-field (NF) paradigm replaces the conventional far-field (FF) paradigm and becomes dominant, necessitating a reassessment of NOMA-based ISAC system performance in the NF region. To address this, a novel NOMA-based ISAC scheme in NF XL-MIMO systems is proposed in this paper, wherein a multi-beam design based on subarray partitioning is employed to realize a communication-and-sensing coexistence ISAC system, while the additional distance-based degree of freedom (DoF) provided by the unique NF beamfocusing is utilized to improve NOMA performance gains. To balance the optimal performance tradeoff between communication and sensing, an optimization problem for joint device scheduling, subarray partitioning, and power allocation is formulated to maximize the ISAC joint rate under various constraints. Based on alternating optimization (AO) and fractional programming (FP) techniques, an efficient joint optimization algorithm is developed to solve the complex non-convex problem with coupled variables. In particular, the original problem is decoupled into three subproblems and solved by applying the linearization of 0-1 polynomial programming, Lagrangian dual reformulation, and quadratic transform techniques. Numerical results validate that our proposed NOMA-based ISAC scheme and joint optimization algorithm significantly enhance the ISAC joint rate performance in NF XL-MIMO systems for IoT networks.

Orthogonal Time Frequency Space (OTFS) modulation technology which provides reliable communication and precise sensing in high-mobility scenarios, has emerged as a potential solution for various unmanned aerial vehicle (UAV)-related applications. In this paper, we consider an OTFS communication waveform-based UAV sensing situation. Due to random wind gusts and varying weather conditions, the sensing signals may experience sudden disturbances. To effectively address this challenge, we propose a deep learning (DL)-based framework to compensate the impulse interference, which leverages empirical information and generates real-time predictions to achieve accurate UAV sensing. Specifically, we develop a prediction-assisted estimation network (PAEnet) to implement the proposed framework. The core component of PAEnet, the estimation network (ESnet), is capable to directly extract fractional delay and Doppler from the transmitted OTFS frame, thereby reducing the complexity of the sensing process. Through comprehensive simulation results, we demonstrate the effectiveness of the compensation mechanism in unreliable sensing scenarios, while showcasing the PAEnet’s capability to achieve superior accuracy for OTFS-based UAV sensing.

The Age of Information (AoI), which measures the time since the generation of the latest update, quantifies information freshness in timeliness-critical systems. Minimizing AoI and characterizing it precisely are crucial for system efficiency and decision-making. This work investigates AoI under external interference modeled as an On-Off process, providing a foundation for future research in more complex scenarios. We consider a discrete-time remote status-updating system with a monitor and a sensor, where the sensor observes a physical process, generates timestamped updates, and sends them to the monitor. Both inter-arrival and service times follow geometric distributions, with service interrupted according to a two-state On-Off process. We analyze AoI under two queuing disciplines: 1) non-preemptive, where arriving updates are discarded if the server is occupied, and 2) preemptive, where in-service updates are replaced with new ones during the Off state. For both, we derive closed-form expressions for average AoI and peak AoI (PAoI). We also explore the relationship between discrete-time and continuous-time systems, showing that the latter is the limiting case of the former. Numerical results validate the theoretical analysis, revealing a linear relationship between the relative normalized increase in average PAoI and AoI and the proportion of Off state time. Frequent On-Off switching and higher service rates under the same system load help mitigate freshness deterioration caused by interruptions. The On-Off process is shown to have a large impact on the average AoI (resp. PAoI) of systems with relatively high (resp. low) arrival and service rates.

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an autonomous aerial vehicle (AAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a AAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by AAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different AAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed semi-synchronous FL with cross-sharpness and layer-freezing (SFedXL), tailored for a AAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a tradeoff of increased local computation latency.

Interactive multimodal applications (IMAs), such as route planning in the Internet of Vehicles, enrich users’ personalized experiences by integrating various forms of data over wireless networks. Recent advances in large language models (LLMs) utilize mixture-of-experts (MoE) mechanisms to empower multiple IMAs, with each LLM trained individually for a specific task that presents different business workflows. In contrast to existing approaches that rely on multiple LLMs for IMAs, this paper presents a novel paradigm that accomplishes various IMAs using a single compositional LLM over wireless networks. The two primary challenges include 1) guiding a single LLM to adapt to diverse IMA objectives and 2) ensuring the flexibility and efficiency of the LLM in resource-constrained mobile environments. To tackle the first challenge, we propose ContextLoRA, a novel method that guides an LLM to learn the rich structured context among IMAs by constructing a task dependency graph. We partition the learnable parameter matrix of neural layers for each IMA to facilitate LLM composition. Then, we develop a step-by-step fine-tuning procedure guided by task relations, including training, freezing, and masking phases. This allows the LLM to learn to reason among tasks for better adaptation, capturing the latent dependencies between tasks. For the second challenge, we introduce ContextGear, a scheduling strategy to optimize the training procedure of ContextLoRA, aiming to minimize computational and communication costs through a strategic grouping mechanism. Experiments on three benchmarks show the superiority of the proposed ContextLoRA and ContextGear. Furthermore, we prototype our proposed paradigm on a real-world wireless testbed, demonstrating its practical applicability for various IMAs. We will release our code to the community.

Space-air-ground integrated network (SAGIN), which integrates satellite systems, aerial networks, and terrestrial communications, offers ubiquitous coverage for a multitude of applications. Nevertheless, the highly dynamic and open nature of SAGIN increases the network’s vulnerability. Hence, zero-trust security, operating on the principle of “never trust, always verify”, holds the significant potential of securing SAGIN. However, implementing zero-trust SAGIN in practice presents three primary challenges: 1) understanding massive unstructured threat information across diverse domains, 2) performing adaptive security assessments, and 3) making in-depth security decisions. This motivates us to propose SAG-Attack and LLM-SA to enhance zero-trust SAGIN. SAG-Attack serves as a simulator that aims to mimic various attacks in SAGIN. Our LLM-SA is a novel situation awareness method that explores the multiple agents of large language model (LLM). Specifically, the output logs of SAG-Attack will be fed into LLM-SA, and LLM-SA fuses vast amounts of heterogeneous threat information from various domains, thus tackling the first challenge. Then, our LLM-SA relies on multiple LLM-based agents to perform adaptive security assessments, utilizing the chain-of-thought capabilities of LLMs to automatically generate in-depth defense strategies, thereby addressing the second and third challenges. Experiments on five benchmarks demonstrate the superiority of the proposed SAG-Attack and LLM-SA. Notably, our method based on open-sourced Llama3-8B even outperforms ChatGPT-4 under the same setting, despite involving significantly fewer parameters. To foster further research in this area, we will release our platform to the community, facilitating the advancement of zero-trust SAGIN.

Efficiency and security are the core challenges in the intelligent communication field. Lightweight neural networks have accelerated the information interpretation and communication efficiency, thereby fostering the rapid development of the Internet of Things. Enhancing the recognition capability of lightweight neural networks remains challenging. Knowledge distillation, a technique that transfers knowledge from a complex model to a smaller one, is often used to improve the recognition performance of lightweight networks. However, practical issues such as transmission constraints and user privacy make the original data required for knowledge distillation difficult to access directly. To tackle this issue, this paper proposes a Data-Free Cloud-Edge Knowledge Distillation (DF-CEKD) model, which uses a complex network in the cloud to provide training guidance for lightweight networks deployed on mobile devices. Specifically, DF-CEKD employs a novel Deep Inversion Diffusion Generation (DIDG) module to provide proxy data as input for the distillation process, thereby transferring the feature learning capability from the cloud network to the edge network. Meanwhile, a Multi-Layer Feature Joint Supervision Distillation (MLF-JSD) module is designed to further enhance the feature selection guidance provided by the teacher network in the cloud for training the lightweight student network. The simulation results demonstrate that the proposed DF-CEKD reduces the number of parameters to 1/20 and the floating-point operations to 1/12 when distilling from WRN40-2 to WRN16-1, resulting in only a 0.27% decrease in accuracy.

This paper proposes a novel integrated sensing and communication (ISAC) receiver design framework for OTFS (orthogonal time frequency space)-based MIMO (multi-input-multi-output) systems from a unified perspective of variational inference. We first construct a factor graph representation for the OTFS-based MIMO system according to the factorization of the a posteriori probability (APP). This representation establishes a direct probabilistic link between sensing and communication, allowing both functionalities to benefit from their integration. On this basis, we develop a low computational complexity message passing algorithm by minimizing the variational free energy associated with the global APP. In particular, belief propagation, mean field, and expectation maximization algorithms for data detection, channel coefficient estimation, and kinematic parameter sensing are derived, respectively. To reduce the communication overhead for the implementation of ISAC algorithm, we propose a federated learning scheme for distributed kinematic parameter sensing. Specifically, by solving the sensing problem in different fashions, three federated learning modes are devised. Simulation results validate the superior performance of the proposed scheme.

Cognitive radio (CR) and integrated sensing and communication (ISAC) are both critical technologies for the sixth generation (6G) wireless networks. However, their interplay has yet to be explored. To obtain the mutual benefits between CR and ISAC, we focus on a reconfigurable intelligent surface (RIS)-enhanced cognitive ISAC system and explore using the additional degrees-of-freedom (DoFs) brought by the RIS to improve the performance of the cognitive ISAC system. Specifically, we mathematically prove that the position error bound (PEB) of each mobile sensor (MS) decreases with the increasing signal-to-noise ratio (SNR) of the received signals at each MS. We also formulate an optimization problem of maximizing the signal-to-noise-plus-interference ratios (SINRs) of the MSs while ensuring the requirements of the spectrum sensing (SS) and the secondary transmissions by jointly designing the SS time, the secondary base station (SBS) beamforming, and the RIS beamforming. The formulated non-convex problem can be solved by the proposed block coordinate descent (BCD) algorithm based on the Dinkelbach’s transform and the successive convex approximation (SCA) methods. Simulation results demonstrate that all the proposed iterative algorithms converge fast, and the SINRs of MSs can be effectively enhanced by increasing the transmit power of the SBS, the number of MS antennas, and the number of RIS elements. Moreover, higher MS SINRs lead to lower PEBs of MSs, thereby having the potential to improve the accuracy of radio environment map (REM) for CR networks. Additionally, the RIS needs to be deployed near the SBS or MSs to guarantee the performance gain brought by the RIS.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

This work studies the average Age of Information (AoI) of a remote monitoring setup in which a multi-sensor system observes independent sources and updates the status to a common monitor using orthogonal channels. Considering the limited buffer size at the sensors, we first model each sensor as a first-come-first-served M/M/1/1 queue. Leveraging tools from stochastic hybrid systems, we derive the average AoI of a homogeneous single-source multi-sensor system in which all sensors’ arrival and service rates are the same. We then extend the results to the multi-source, multi-sensor system. For a multi-source dual-sensor system, we present an approximate optimal arrival rate for a given sum arrival rate at a light load. For heterogeneous cases with different arrival and service rates at sensors, the average AoI is derived for the single-source dual-sensor and more general multi-source systems. Our analysis shows that the average AoI decreases by 16.44% and 21.44% for the dual-sensor and three-sensor systems, respectively, compared to the single-sensor system when the service rate and the total arrival rate of the sensors are normalized. Numerical results confirm that the average AoI performance of the single-source dual-sensor system outperforms the M/M/2 system at high system load.

As a promising candidate for high-mobility communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention owing to its robustness to Doppler shifts and efficient hardware implementation. In this paper, motivated by the urgent demand for seamless and reliable communications in high-speed railway (HSR) scenarios, we innovatively integrate OCDM into cell-free massive multiple-input multiple-output (CFmMIMO) systems and establish a novel transmission framework, termed CFmMIMO-OCDM. Within this framework, we conduct a comprehensive analysis of the doubly-dispersive HSR channel model and derive the input-output signal relation in HSR communications. Moreover, to address the challenges posed by high computational complexity and excessive data exchange inherent in centralized signal processing, we first reveal the quasi-sparsity of the Fresnel-domain channel matrix in HSR communications. Then, we develop a distributed baseband processing (DBP) architecture by leveraging the channel sparsity. Aimed at enhancing the signal detection efficiency and accuracy, we further design a distributed message passing (DMP)-based detection algorithm for CFmMIMO-OCDM in HSR communications, which achieves considerably reduced complexity and data exchange compared to the centralized detection. Numerical results confirm the superiority of CFmMIMO-OCDM over conventional orthogonal frequency division multiplexing (OFDM)-assisted CFmMIMO systems in HSR communications. Moreover, theoretical analysis and numerical results are provided to demonstrate that our proposed DMP detection can achieve attractive bit error rate (BER) and complexity performance compared to conventional centralized detection.

As a key technology in beyond 5G and future 6G communications, RAN slicing can realize differentiated service level agreement (SLA) guarantees. In this paper, we investigate the multi-slice multi-user RAN slicing. A dynamic bandwidth allocation scheme for RAN slicing is proposed based on hierarchical intelligence, where bandwidth pre-allocation and hyper-parameter tuning for MAC layer schedulers are jointly optimized to maximize the system utility, i.e., the weighted sum of spectrum efficiency (SE) and SLA satisfaction ratio (SSR) of different slices. The problem is formulated as a twin-time scale Markov decision process (MDP), where the bandwidth pre-allocation and the scheduler parameter tuning are performed on a long-term scale (e.g., seconds) and a short-term scale (e.g., 100 ms), respectively, for which we propose a hierarchical twin-time scale Dueling deep Q learning network (TTS-DDQN) algorithm. A new reward-clipping mechanism is proposed to get a better trade-off between stabilized training and higher system utility. In order to improve the robustness to time-varying traffic patterns and non-stationary dynamic environments, we further propose a traffic-aware module for more efficient sampling of the experience pool, and a variational adversarial inverse reinforcement learning (VAIRL) module for reward automation design. Extensive simulations show that the traffic-aware TTS-DDQN in stationary scenarios and the VAIRL module embedded TTS-DDQN in non-stationary scenarios outperform existing typical DQN-based algorithms, hard slicing and non-slicing, etc.

It is challenging to ensure both high accuracy and low complexity when localizing radiation sources. To address this challenge, we propose two novel methods leveraging time difference of arrival (TDOA) measurements. Specifically, we introduce a TDOA estimation method and a direct position determination (DPD) method based on parallel factor (PARAFAC) analysis in the cross-correlation domain. Initially, multiple sensors synchronously capture the source signal, and the cross-correlation function between signals received from a reference sensor and other sensors is calculated. Then, the primary cross-spectrum data undergoes an expansion and integration process to establish the PARAFAC model. Through cross-spectrum expansion, virtual nodes are formed, which further improves the estimation performance. The TDOA estimates for each sensor are obtained by normalizing and extracting the phase from this matrix. Additionally, we introduce a novel DPD method tailored for multipath propagation scenarios. Simulations and real-world measurements demonstrate the superiority and effectiveness of our proposed methods compared with cutting-edge methods.

To satisfy the communication, localization, and energy demands of low-power IoT nodes, combining integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT) can offer an innovative solution. However, carrying private information in wireless sensing beams critically increases the vulnerability of being eavesdropped. In this paper, we propose two non-orthogonal multiple access (NOMA)-enhanced secure transmission strategies to counteract the internal and external eavesdropping for SWIPT-ISAC. First, we jointly optimize the transmit beamforming and power splitting to maximize the secrecy rate towards the internal eavesdropping by the target, ensuring the nonlinear energy harvesting (EH) and precise sensing beampatterns. Then, facing a more severe scenario with L external eavesdroppers, we exploit the artificial jamming with the highest power allocation to mitigate both the internal and external eavesdropping. Our approach enables the nodes to manage the interference through successive interference cancellation and to harvest energy from the jamming. The original optimization problems in both scenarios are non-convex and difficult to tackle. Using the semidefinite relaxation, we convert them to convex forms, and propose alternating optimization algorithms to derive the solutions. Simulation results indicate that the proposed schemes can effectively counteract the internal and external eavesdropping, while ensuring the nonlinear EH and sensing performance.

This paper is concerned with the theoretical modeling and analysis of uplink connection performance of a radiosonde network deployed in a typhoon. Similar to existing works, the stochastic geometry theory is leveraged to derive the expression of the uplink connection probability (CP) of a radiosonde. Nevertheless, existing works assume that network nodes are spherically or uniformly distributed. Different from the existing works, this paper investigates two particular motion patterns of radiosondes in a typhoon, which significantly challenges the theoretical analysis. According to their particular motion patterns, this paper first separately models the distributions of horizontal and vertical distances from a radiosonde to its receiver. Secondly, this paper derives the closed-form expressions of cumulative distribution function (CDF) and probability density function (PDF) of a radiosonde’s three-dimensional (3D) propagation distance to its receiver. Thirdly, this paper derives the analytical expression of the uplink CP for any radiosonde in the network. Finally, extensive numerical simulations are conducted to validate the theoretical analysis, and the influence of various network design parameters is comprehensively discussed. Simulation results show that when the signal-to-interference-noise ratio (SINR) threshold is below -35 dB, and the density of radiosondes remains under 0.01/km3, the uplink CP approaches 26%, 39%, and 50% in three patterns.

Using multiple sensors to update the status process of interest is promising in improving the information freshness. The unordered arrival of status updates at the monitor end poses a significant challenge in analyzing the timeliness performance of parallel updating systems. This work investigates the age of information (AoI) of a discrete-time dual-sensor status updating system. Specifically, the status update is generated following the zero-waiting policy. The two sensors are modeled as a geometrically distributed service time queue and a deterministic service time queue in parallel. We derive the analytical expressions for the average AoI and peak AoI using the graphical analysis method. Moreover, the connection of average AoI between discrete-time and continuous-time systems is also explored. It reveals that in dual-queue systems, the AoI results of continuous-time systems with exponential time distribution can be extended from the limit cases of discrete-time systems with geometric distribution. Numerical results validate the effectiveness of our analysis and further show that randomness of service time contributes more AoI reduction than determinacy of service time in dual-queue systems in most cases, which is different from what is known about the single-queue system.

Orthogonal Time Frequency Space (OTFS) modulation holds significant potential for diverse applications in both sensing and communication fields. This paper mainly investigates waveform optimization for OTFS modulation, aiming to design pilot symbol matrices with low sidelobe levels and data symbol matrices with high communication rates under peak-to-average power ratio constraints. We first formulate the problem of minimizing the weighted integrated sidelobe levels and maximizing the communication rate in the delay-Doppler domain. Subsequently, to address the complicated optimization problem, a Majorization-Minimization based algorithm is proposed to decompose it into a series of subproblems. These subproblems are then reformulated as unconstrained optimization problems on the Stiefel manifold, and the Riemannian conjugate gradient method is employed to solve them efficiently. Moreover, a faster iterative algorithm is proposed based on second-order Taylor approximation to accelerate the convergence speed. Simulation results validate that the proposed algorithms effectively achieve pilot matrices with desirable ambiguity functions and data symbol matrices with high communication rates under various weighting factors.

Non-terrestrial networks (NTN) have emerged as a transformative solution to bridge the digital divide and deliver essential services to remote and underserved areas. In this context, low Earth orbit (LEO) satellite constellations offer remarkable potential for efficient cache content broadcast in remote regions, thereby extending the reach of digital services. In this paper, we introduce a novel approach to optimize wireless edge content placement using NTN. Despite wide coverage, the varying NTN transmission capabilities must be carefully aligned with each content placement to maximize broadcast efficiency. In this paper, we introduce a novel approach to optimize wireless edge content placement using NTN, positioning NTN as a complement to TN for achieving optimal content broadcasting. Specifically, we dynamically select content for placement via NTN links. This selection is based on popularity and suitability for delivery through NTN, while considering the orbital motion of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant improvement in placement speed compared to existing methods, which neglect network mobility. We also demonstrate that NTN links significantly outperform standalone wireless TN solutions, particularly in the early stages of content delivery. This advantage is amplified when there is a higher correlation of content popularity across geographical regions.

The lunar surface ad-hoc network is a critical component of the international lunar research station and an extension of the earth-moon communication networks. Its high reliability and low delay are essential for ensuring the safety of the lunar station and improving the efficiency of node collaboration. However, due to the lack of large-scale grid infrastructures, the network must operate autonomously for long periods under strong energy constraints. We propose EMOR, a cross-layer routing protocol, which aims to achieve sustainable high reliability and low latency while balancing energy recovery and consumption. EMOR improves reliability through the “parallel” forwarding feature of opportunistic routing and reduces delay through a mixture of table-based and timer-based routing mechanisms. Moreover, EMOR uses reinforcement learning to analyze the environment and calculate the weights of energy and progress to guide the emphasis on multi-metrics routing. To balance energy consumption and recovery, EMOR introduces a dynamic duty cycle in the MAC layer. Compared to table-based routing and the latest opportunistic routing, EMOR maintains the optimal end-to-end delay in the order of 1ms while improving the packet delivery ratio 6% to 21% higher than other protocols. Moreover, the network lifetime using EMOR is extended by 75.5% to 242%.

Clustered vehicular federated learning (CVFL) facilitates data sharing and collaborative decision-making among vehicles, thus refining traffic behavior and demonstrating the immense potential for transforming intelligent transportation systems into a reality. However, non-independent and identically distributed data and diverse model requirements among vehicular clients hinder the feasibility of a one-size-fits-all model. Besides, “selfish” vehicular clients may be unwilling to participate in learning tasks because of the huge resource consumption of the training process. To address these challenges, in this paper, we first group local models using the adaptiveK-means-based model grouping method and then aggregate the models within each group to generate CVFL models for subsequent multi-model training. Secondly, we propose a dynamic matching-based clustering method based on the local data quality and similarity to achieve efficient vehicular client clustering. Subsequently, a meticulously crafted hierarchical incentive mechanism, grounded in a three-stage Stackelberg game, is introduced to incentivize both cluster heads and members in a layered fashion, with the initiation stemming from the CVFL server. To determine the optimal strategies for the three-stage game, an iterative algorithm is proposed, and near-optimal analytical solutions are obtained with reduced complexity. The simulation results demonstrate that our CVFL system, augmented with the hierarchical incentive mechanism, can effectively motivate multiple clusters to train multiple models in parallel, thus improving overall efficiency.

Integrating non-terrestrial networks (NTN) with terrestrial networks (TN) is vital to support scalable multicast/broadcast services (MBS) in 6G, particularly for low-altitude UAV swarms requiring seamless and reliable coverage. Low Earth orbit (LEO) constellation in integrated TN-NTN can effectively take over multicast to UAVs when flying over TN underserved regions. However, distinct differences in signal variation and mobility between TN and NTN make it difficult to optimally exploit MBS cooperation and maintain superior delivery. To address these challenges, this paper proposes a mobility-aware TN-NTN MBS orchestration framework for low-altitude UAVs. We fist cognize signal variations of TN and NTN in low-altitude layer with UAV mobility characteristics from cell center to edge, and use an Adaboost-based machine learning classifier to dynamically group UAVs into two segments for optimal system multicast delivery. A joint file multicast scheduling strategy is also proposed to align with UAV and NTN mobility-driven grouping dynamics to globally enhance multicast time efficiency. System-level case studies with a practical LEO constellation confirm our approach significantly outperforms existing methods, especially when more UAVs near cell edges. Our method also demonstrates strong adaptability to network dynamics and superior time efficiency, enabling robust and efficient MBS delivery in integrated 6G TN-NTN systems.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms.

This work studies information freshness of a V2I status updating link in IoV. The status updating link is modeled as a multi-source Ber/Geo/1/1 non-preemptive or preemptive queue. We focus on statistical characteristics of the age of information (AoI) and peak AoI (PAoI). To fully track the AoI evolutions under non-preemptive and preemptive policies, Markov three-dimensional age process (3DAP) and two-dimensional age process (2DAP) are respectively introduced. Their first element is the AoI process; The second one stands for if an update of the concerned source is in transmission and its current age; The third element of 3DAP denotes if an update of another source is in transmission. An analytical approach for studying the AoIs and PAoIs in discrete-time multi-source systems is presented. By studying the state transitions, balance equations, and stationary distributions of 3DAP and 2DAP, analytical expressions of the distributions and averages of AoIs and PAoIs under both queueing policies are derived. Moreover, the optimal probabilistic update selection mechanism (PUSM) that maximizes overall freshness is derived in closed-form for the two-source case. Numerical results validate effectiveness of the theoretical analyses and reveal usefulness of the retransmission. It is found that in terms of improving the overall freshness, the PUSM should be designed to make effective update generation probabilities of sources as close as possible.

This paper investigates the hybrid beamforming design for the reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cell-free networks, where the access points (APs) connected to the central processing unit serve multiple users cooperatively. The weighted sum rate of uplink and downlink transmissions is maximized by jointly optimizing the digital and analog beamformers, the phase-shift coefficients of RISs and the uplink transmit power while satisfying the power budget constraints of the APs and users. For solving the problem, the objective function is reformulated using Lagrangian dual transform and fractional programming. Then, to tackle the variables coupling, the problem is divided into five subproblems that are solved iteratively via a proposed block coordinate descent (BCD)-based algorithm. To handle the unit-modulus constraint on analog beamformers and phase-shift coefficients, a monotonic fast proximal gradient (mFPG)-based method is proposed under the alternating direction method of multipliers (ADMM) framework. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm compared to three baseline algorithms. The impacts of the numbers of radio frequency chains and reflecting elements on the uplink and downlink transmissions are presented, respectively. The performance comparison between the FD and half-duplex schemes is provided.

Extremely Large-scale Antenna Array (ELAA) is increasingly recognized as a promising solution for enhancing spectral efficiency and spatial resolution in the 6G mobile system. However, realizing these benefits necessitates the development of sophisticated interference management strategies, which typically rely on perfect Channel State Information at the Transmitter (CSIT) and involve computationally intensive operations. In real-world scenarios, perfect CSIT is typically infeasible due to inherent channel estimation errors and hardware impairments, which also lead to imperfect Successive Interference Cancellation (SIC). Additionally, the computational complexity associated with precoding schemes poses a formidable challenge. To address these issues, this study proposes a Deep Learning (DL)-assisted Rate-Splitting Multiple Access (RSMA) scheme for ELAA systems. The primary objective is to maximize the geometric mean of ergodic user-rates under imperfect CSIT and SIC, thereby optimizing both fairness and system throughput. Given the prohibitively high computational complexity of conventional optimization approaches to address this optimization problem, we introduce a DL model, named GruCN, to optimize precoder design. Simulation results demonstrate that the proposed RSMA-enabled ELAA system achieves better performance in terms of fairness and robustness under imperfect CSIT. Moreover, the GruCN model exhibits remarkable efficiency and effectiveness in precoder optimization.

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.

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.

Precise unmanned aerial vehicle (UAV) detection over long distances is of crucial importance for guaranteeing the airspace security. Although deep learning-based vision detectors have been developed, they still rely on a large amount of hand-crafted fixed feature priors. The existing static dense-based detectors suffer from the severe mismatch and imbalance between the small size and the high mobility of UAVs. To solve the problem, a novel multimodal fusion-based dynamic sparse UAV detection framework is proposed. The framework reformulates the feature priors in a completely dynamic sparse paradigm by using the radar data. Based on the framework, a vision-radar fusion-based dynamic sparse network (Vira-DSNet) is proposed for more balanced and robust UAV detection. The Vira-DSNet exploits our designed dynamic sparse candidate generator and radar-guided semantic feature transform to generate a small set of customized high-quality object candidates and semantic features based on the radar data. Moreover, based on Hungarian bisection matching, our Vira-DSNet eliminates the post-processing and is completely end-to-end differentiable. Furthermore, the Vira-DSNet is deployed in our developed actual vision-radar fusionbased UAV detection system to evaluate the performance in the practical applications. Experimental results demonstrate that our Vira-DSNet achieves an average precision AP50of 88.2%. It is also shown that the average recall AR1of Vira-DSNet is higher than the state-of-the-art scheme by 10.1%, while maintaining the real-time performance.

Federated Learning (FL) stands as a privacy-preserving machine learning paradigm that enables collaborative training of a global model across multiple clients. However, the practical implementation of FL models often confronts challenges arising from data heterogeneity and limited communication resources. To address the aforementioned issues simultaneously, we develop a Sparsified Random Partial Update framework for personalized Federated Learning (SRP-pFed), which builds upon the foundation of dynamic partial model updates. Specifically, we decouple the local model into personal and shared parts to achieve personalization. For each client, the ratio of its personal part associated with the local model, referred to as the update rate, is regularly renewed over the training procedure via a random walk process endowed with reinforced memory. In each global iteration, clients are clustered into different groups where the ones in the same group share a common update rate. Benefiting from such design,SRP-pFedrealizes model personalization while substantially reducing communication costs in the uplink transmissions. We conduct extensive experiments on various training tasks with diverse heterogeneous data settings. The results demonstrate that theSRP-pFedconsistently outperforms the state-of-the-art methods in test accuracy and communication efficiency.

Deep learning-based semantic communications (DLSC) leverage deep neural networks in transmitters and receivers, pushing the boundaries beyond Shannon limit. However, DLSC is extremely vulnerable to malicious physical-layer adversarial attacks due to the openness of wireless channels. Meanwhile, existing defense approaches still suffer from two challenges for robust DLSC. First, most methods require offline DLSC retraining to defend against various attacks, causing interruptions of online service. Second, they struggle to achieve effective defense in real-world time-varying channels, thus limiting DLSC reliability. We propose PBNet, integrating a pluggable protector and an adaptive protector to respectively address the above two challenges. First, the pluggable protector utilizes a novel denoising module to safeguard the transmitted signals, enabling hot-pluggable deployment without interrupting communication. Second, the adaptive protector leverages a novel alternating adaption strategy to achieve effective defense in time-varying channels, ensuring robust performances under real-world dynamic conditions. Evaluations involving symbols, images, texts, and speeches show the efficacy of our PBNet, which has respectively achieved an impressive 72.22% and 73.71% accuracy improvement in defending against unknown$l_{0}$-norm and$l_{2}$-norm attacks on image-based DLSC. Furthermore, we developed two real-world radio systems of PBNet to perform over-the-air signal generation, integrating hardware and software such as FPGA chips and GNU radio. We also implemented an interactive UI of PBNet based on QT5, aiming to demonstrate the effect of attacks and defense visually. This work achieves robust DLSC performances under various attacks and time-varying channels, taking a significant step towards the practical defense scheme for robust DLSC.

Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks, but its practical deployment is hindered by the high communication overhead caused by frequent and costly server-device synchronization. Notably, most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance resulting from the prevalent issue of device heterogeneity. This variance severely decelerates algorithm convergence, increasing communication overhead and making it more challenging to achieve a well-performed model. In this paper, we propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme to achieve heterogeneity-robustness in the presence of quantized transmission and heterogeneous local updates among active edge devices. Comprehensive theoretical analysis justifies that FedQVR is inherently resilient to device heterogeneity and has a comparable convergence rate even with a small number of quantization bits, yielding significant communication savings. Besides, considering non-ideal wireless channels, we propose FedQVR-E which enhances the convergence of FedQVR by performing joint allocation of bandwidth and quantization bits across devices under constrained transmission delays. Extensive experimental results are also presented to demonstrate the superior performance of the proposed algorithms over their counterparts in terms of both communication efficiency and application performance.

Time-sensitive networking (TSN) is proposed to satisfy the increasingly stringent demands of Industrial 4.0 for deterministic transmission. This is achieved by generating a series of centrally configured gate control lists to strictly restrict the forwarding time of arriving flows. However, such a centralized scheme requires prior information of all flows, severely impeding TSN from providing an online response to dynamic industrial applications. To solve this problem, we innovatively propose to use admission control (AC) to realize deterministic transmission in the virtualized TSN network. In this approach, AC is distributively executed on each node and link, whereby flows of applications are served by passing through a series of virtual network functions (VNFs). This distributed AC execution is regarded as a VNF embedding (VNE) process. Specifically, we propose a two-stage online framework, Smart Admission Control (SmartAC), to cater to dynamic applications. The first stage, referred to as thestatic stage, obtains a deterministic VNE solution by synthesizing AC decisions of individual TSN nodes and links. The second stage, referred to as thedynamic stage, fine-tunes VNE solutions obtained from thestatic stageto adapt to the harsh environment with insufficient resources or limited VNF migration budgets. Simulation results demonstrate the effectiveness of SmartAC in improving response rate and resource utilization ratio. Notably, SmartAC reduces runtime by 90% compared to existing algorithms and exhibits robustness across different network topologies.

In Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users’ tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques.

With the development of cyber-physical systems, Digital Twins (DT)-powered network autonomy is emerging to embrace the fifth-generation industrial revolution. In this context, Unmanned Aerial Vehicles (UAVs)-based low-altitude networks are expected to be the engines that drive industrial development. As an attractive industry application, UAVs-based intelligent logistics has been widely investigated to achieve a fully automated distribution manner without the aid of a workforce. However, it is difficult to perform real-time DT implementations due to limited computing resources and the high mobility of UAVs. To address the mentioned problems, we propose a Versatile DT (VerDT) framework operating at the edge. It can enable a double DT cooperation manner with a resource scheduling model and a path planning model for real-time and accurate logistics distributions. The resource scheduling model can implement the integration of computing and communication resources among UAVs for feasible cooperative distribution decisions. With the decisions, the path planning model can imitate to derive positions and velocities of UAVs for low-latency distribution performance with energy saving. Experiment results demonstrate the efficiency of our VerDT framework. Compared to state-of-the-art logistics distribution solutions, our solution reduces the distribution latency by 63.9% while improving the successful distribution ratio by 10.9%.

This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), which uses the geometric median for aggregation and allows flexible round number for local updates. Unlike most existing resilient approaches, which base their convergence analysis on strongly-convex loss functions or homogeneously distributed datasets, this work conducts convergence analysis for both strongly-convex and non-convex loss functions over heterogeneous datasets. The theoretical analysis indicates that as long as the fraction of the data from malicious users is less than half, RAGA can achieve convergence at a rate of$\mathcal {O}({1}/{T^{2/3- \delta }})$for non-convex loss functions, where$T$is the iteration number and$\delta \in (0, 2/3)$. For strongly-convex loss functions, the convergence rate is linear. Furthermore, the stationary point or global optimal solution is shown to be attainable as data heterogeneity diminishes. Experimental results validate the robustness of RAGA against Byzantine attacks and demonstrate its superior convergence performance compared to baselines under varying intensities of Byzantine attacks on heterogeneous datasets.

Caching recommended contents at the network edge can effectively alleviate the traffic pressure of the backbone network and significantly improve user experience. However, highly personalized and precise recommendations often rely on leveraging more user request records, raising serious privacy concerns. Existing recommendation-aware edge caching mechanisms typically apply a fixed level of privacy protection, without considering the personalized privacy of users. This one-size-fits-all approach often introduces significant noise, adversely impacting cache hit ratio (CHR). In this work, we propose a differential privacy-based edge caching framework supporting personalized privacy-preserving to address these challenges. We formulate a CHR maximization problem under personalized privacy constraints and reveal the NP-completeness of the problem with a rigorous mathematical proof. Subsequently, we mathematically model the relationship between personalized privacy and user preference distortion, analyzing its impact on recommendations and user requests. To solve it, we introduce an efficient heuristic algorithm named the Backhaul Traffic-Aware Caching Algorithm. This algorithm utilizes backhaul traffic as a feedback signal to make accurate caching decisions, enabling adaptive optimization of caching decisions by perceiving the impact of noise and low-quality recommendations. Extensive experiments on two typical real-world datasets validate the effectiveness of our framework, demonstrating its ability to enhance privacy protection while simultaneously improving CHR.

Diffusion models have been extensively utilized in AI-generated content (AIGC) in recent years, thanks to the superior generation capabilities. Combining with semantic communications, diffusion models are used for tasks such as denoising, data reconstruction, and content generation. However, existing diffusion-based generative models do not consider the stringent bandwidth limitation, which limits its application in wireless communication. This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model. Our designed architecture utilizes the diffusion model, where the signal transmission process through the wireless channel acts as the forward process in diffusion. To reduce bandwidth requirements, we incorporate a downsampling module and a paired upsampling module based on a variational auto-encoder with reparameterization at the receiver to ensure that the recovered features conform to the Gaussian distribution. Furthermore, we derive the loss function for our proposed system and evaluate its performance through comprehensive experiments. Our experimental results demonstrate significant improvements in pixel-level metrics such as peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS). These enhancements are more profound regarding the compression rates and SNR compared to deep joint source-channel coding (DJSCC).

Given the crucial role of the earth observation satellites in numerous key applications, using Low Earth Orbit (LEO) satellite internet as intermediaries via Laser Inter-Satellite Links (LISLs) has emerged as a promising solution to help transmit substantial amounts of observation data to ground stations. For Very Low Earth Orbit (VLEO) observation satellites, optimizing the cross-layer topology between themselves and LEO communication satellites has become paramount. To mitigate existing centralized algorithms’ reliance on global data transfer requirement information, a Novel Distributed Interactive Mechanism (NDIM) for cross-layer LISL establishment is proposed. Here, the VLEO observation satellite decides its own access strategy based on local network information gleaned from three information exchanges with the LEO communication satellite. Within this mechanism, the cross-layer link optimization is performed via the formulation of a multi-objective topology optimization model, which considers the transmission requirements of observation satellites, load balancing amongst the communication satellite layer, and the transmission delay of emergency tasks. Based on this framework, we propose a Distributed Multi-objective cross-layer Topology Optimization (DMTO) algorithm. Our algorithm is novel in considering the remaining load of the intermediary communication satellites, and it allows observation satellites to decide on access plans on demand, given incoming data. Additionally, we used real data from Typhoon LEKIMA to establish a multi-task scenario and conducted packet-level simulations based on the Starlink and Dove constellations. The results indicate that, compared to the existing baseline, the DMTO algorithm increased the observation data throughput by 1.37% (326.95 GB) and reduced the average transmission delay of emergency task data by 4.20% (20.5 seconds).

Ultra-Reliable and Low-Latency Communications (URLLC) is essential for sixth generation communications, with Cell-Free massive Multiple-input-Multiple-Output (CF mMIMO) being a promising architecture to support these demands. This paper addresses the challenge of optimizing energy efficiency in CF mMIMO systems for URLLC, focusing on the probabilistic delay bounds and finite blocklength communications. We propose a theoretical framework that considers tail distributions to evaluate extreme reliability and latency requirements, instead of relying on asymptotic analysis. In particular, a closed-form expression for the signal-to-interference-plus-noise ratio (SINR) distribution is derived, accommodating imperfections in channel state information caused by pilot contamination. Then, the paper also presents a comprehensive reliability analysis, incorporating both delay violation probability and average decoding error probability, utilizing stochastic network calculus for accurate statistical modeling. Finally, an innovative power control algorithm is proposed to maximize effective energy efficiency (EEE), the ratio of the effective data rate to total power consumption, while meeting stringent Quality-of-Service (QoS) constraints and power limits. Extensive simulations validate the theoretical framework and the efficacy of the proposed algorithm, demonstrating its ability to enhance EEE in various scenarios and providing insights into the interplay between EEE, delay, and reliability metrics.

We propose an effective approach toward implementing personalized federated learning at the edge of wireless networks. The scheme employs a bi-level optimization framework to personalize the federated learning models, and leverages over-the-air computations for model aggregation.We identify a mutual benefit in such a design. Specifically, personalized federated learning models address the challenge of data heterogeneity in federated learning, while over-the-air computations, which capitalize on the superposition property of multiple access channels, enable all clients to upload their intermediate parameters in each communication round for global aggregation, significantly enhancing system scalability. However, the channel fading and heavy-tailed noise introduced by over-the-air computations pose challenges to the robustness of personalized federated learning models. By adopting a bi-level optimization framework, we improve the stability of personalized federated learning models based on over-the-air computations, establishing a scalable and robust federated edge learning system. We also derive convergence rates of the proposed algorithm, encompassing key factors such as model compression, channel fading, and heavy-tailed noise. The analysis offers a comprehensive understanding of how system configurations affect training performance. We corroborate the efficacy of our framework via extensive experiments.

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection.

Orbital angular momentum (OAM) in electromagnetic (EM) waves can significantly enhance spectrum efficiency in wireless communications without requiring additional power, time, or frequency resources. Different OAM modes in EM waves create orthogonal channels, thereby improving spectrum efficiency. Additionally, OAM waves can more easily maintain orthogonality in line-of-sight (LOS) transmissions, offering an advantage over multiple-input and multiple-output (MIMO) technology in LOS scenarios. However, challenges such as divergence and crosstalk hinder OAM’s efficiency. Additionally, channel modeling for OAM transmissions is still limited. A reliable channel model with balanced accuracy and complexity is essential for further system analysis. In this paper, we present a quasi-deterministic channel model for OAM channels in the 5.8 GHz and 28 GHz bands based on measurement data. Accurate measurement, especially at high frequencies like millimeter bands, requires synchronized RF channels to maintain phase coherence and purity, which is a major challenge for OAM channel measurement. To address this, we developed an 8-channel OAM generation device at 28 GHz to ensure beam integrity. By measuring and modeling OAM channels at 5.8 GHz and 28 GHz with a modified 3D geometric-based stochastic model (GBSM), this study provides insights into OAM channel characteristics, aiding simulation-based analysis and system optimization.

Disasters severely damage cellular network infrastructures, weakening network communication service capability (CSC) and impeding post-disaster efforts. Therefore, evaluating network ability to resist disasters and recovering CSC are crucial. In this paper, we introduce a novel metric, network capacity scalability (NCS), defined by spatial throughput (ST) and its standard deviation to characterize CSC in disasters. By revealing the impact of disasters on NCS, network resistance to disasters can be reflected. Specifically, a critical disaster intensity (CDI) is derived, below which the effect of disasters on NCS is negligible and networks are disaster-resistant. However, NCS rapidly deteriorates once CDI is exceeded, necessitating recovery strategies. In response, we design a CSC compensation strategy where uncrewed aerial vehicle access points (UAPs) are supplemented, and a critical UAP density maximizing NCS is provided. Notably, compared to ST, NCS can more promptly reflect CSC deterioration, enabling rapider strategy implementation. Moreover, we also demonstrate that fluctuating burst service superlinearly worsens NCS, revealing the network’s poor tolerance to burst service. In this light, we propose a coverage adjustment strategy for terrestrial base stations and UAPs. Simulation results show that the negative effect of burst service can be significantly mitigated, which verifies the effectiveness of the proposed strategy.

Pilot pattern optimization in orthogonal frequency division multiplexing (OFDM) systems has been widely investigated due to its positive impact on channel estimation. In this paper, we consider the problem of multi-user pilot pattern optimization for OFDM systems. In particular, the goal is to enhance channel extrapolation performance for 5G NR systems by optimizing multi-user pilot patterns in frequency-domain. We formulate a novel pilot pattern optimization problem with the objective of minimizing the maximum integrated side-lobe level (ISL) among all users, subject to a statistical resolution limit (SRL) constraint. Unlike existing literature that only utilizes ISL for controlling side-lobe levels of the ambiguity function, we also leverage ISL to mitigate multi-user interference in code-domain multiplexing. Additionally, the introduced SRL constraint ensures sufficient delay resolution of the system to resolve multipath, thereby improving channel extrapolation performance. Then, we employ the estimation of distribution algorithm (EDA) to solve the formulated problem in an offline manner. Finally, we extend the formulated multi-user pilot pattern optimization problem to a multiband scenario, in which multiband gains can be exploited to improve channel extrapolation performance. Simulation results demonstrate that the optimized pilot pattern yields significant performance gains in channel extrapolation over the conventional pilot patterns.

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramér-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

Integrating aerial base stations (ABSs) with terrestrial base stations (TBSs) represents a promising architecture for future networks. However, challenges arise from the ABS mobility and complex interference, leading to degradation in the coverage performance, including both the average coverage quality and coverage stability. To address these challenges, we investigate the average coverage quality and coverage stability via the first- and second-order statistical properties of network spatial throughput, respectively. Our findings reveal that the inappropriate ABS deployment, especially the antenna beamwidth, causes the average coverage quality deterioration due to the co- and cross-layer interference surge induced by the overlapping coverage between ABSs and TBSs. Additionally, coverage stability experiences degradation due to the ABS mobility, particularly exacerbated by factors such as the large deployment density and circling radius as well as the small flight height and antenna beamwidth of ABSs. Moreover, it is demonstrated that there exists an optimal ABS antenna beamwidth maximizing the coverage performance. On this account, we propose an antenna beamwidth optimization algorithm as well as a time-efficient but low performance loss alternative antenna beamwidth optimization strategy, aimed at mitigating the overlapping coverage-induced interference and reducing the ABS mobility-induced impact, both of which are validated through numerical results.

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

Despite the allure of ubiquitous, high-speed, and low-latency connectivity offered by Space-Air-Ground Integrated Networks (SAGINs), the co-existence of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) within the same frequency band poses significant challenges in interference management. Traditional optimization approaches, requiring seconds or even minutes for beamforming design, simply cannot keep pace with this dynamic environment. This work addresses these challenges by proposing a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA), which enables efficient cross-system interference management in SAGINs with statistical Channel State Information (CSI) at the Transmitter (CSIT). Building upon FD-RSMA, we study the precoder design of LEO satellites and UAVs along with common rate allocations of RSMA to maximize Weighted Ergodic Sum Rate (WESR). To handle channel randomness, we employ a Sample Average Approximation (SAA) approach. Furthermore, a Deep Learning (DL)-based precoder design algorithm, called GruCN, which marries the advantages of Gate Recurrent Unit (GRU) and Convolutional Neural Network (CNN), is proposed to efficiently tackle the non-convex optimization problem. Numerical results demonstrate the effectiveness and efficiency of our proposed DL-assisted FD-RSMA. Compared to conventional RSMA approaches, FD-RSMA improves up to 20% of WESR performance, while the GruCN achieves around 50% higher WESR performance and up to four orders of magnitude lower processing time than the conventional optimization approaches.

In-orbit computation offloading plays a crucial role in enhancing the performance of resource-constrained mobile devices by conserving energy and reducing application latency. However, the inherent channel uncertainty in uplink communications poses a significant challenge, often degrading the Quality of Service (QoS) provided by Satellite Edge Networks (SENs). This uncertainty cannot be effectively captured by static parametric modeling, limiting their applicability in dynamic environments. To address this limitation, we propose an environment-aware computational offloading strategy for SENs. Unlike previous studies that neglect the impact of uplink channel uncertainty, we focus on this key issue by formulating a stochastic optimization problem aimed at minimizing offloading latency. Our approach integrates channel state variability into the decision-making process, ensuring a more realistic and robust model for SEN applications. In particular, we design a novel Spatio-Temporal Mixing (STM) methodology to extract relevant features from both environmental data and historical Channel State Information (CSI). These features are then used to jointly optimize the task scheduling, satellite selection, and beamforming vector design. Extensive simulations demonstrate that the proposed STM approach significantly reduces latency compared to traditional methods. The results highlight the effectiveness of our strategy in addressing the challenges posed by uplink channel uncertainty, ultimately leading to more efficient and reliable SEN operations.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), in which one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC) and then DC transmits the processed data back to a group of ground users to fulfill their diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Sensor Association Information (AomaI) metric by incorporating the multi-source and up-downlink aspects. Under this framework, we formulate the optimization problem aiming to minimize the average AomaI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose parallel optimization and primal-dual methods to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, then develop the data processing and results distribution scheduling strategies for the DC, and lastly propose the task-associated genetic algorithm (TAGA) and the improved Nawas-Enscore-Ham (INEH) algorithm to design the UAV’s visiting order. Simulation results demonstrate that uplink and downlink AoI influence each other, and the consideration of up-downlink AoI can effectively enhance the freshness of AomaI.

In this paper, a passive low earth orbit (LEO) satellite localization framework is investigated. In our considered model, one active satellite and multiple passive satellites are selected to localize a target LEO satellite, where the active satellite transmits signals to the target satellite and passive satellites receive signals reflected by the target satellite. Based on the received signals, passive satellites calculate the transmission distances and send this distance information to the active satellite that will estimate the position of target satellite. Since LEO satellites are powered by the sun, the available energy that can be used for target satellite localization is limited and dynamic. Hence, the satellite selection scheme must be optimized for improving the localization accuracy under the energy consumption constraints. This problem is cast into an optimization setting with a goal of minimizing target satellite positioning error by jointly optimizing active/passive satellite selection and transmit power allocation. To solve this problem, a mixture Gaussian distribution-based reinforcement learning (MGD-RL) method is proposed. The proposed MGD-RL method enables each LEO satellite to determine whether to be an active or a passive satellite and optimize its transmit power under the energy constraints. Furthermore, the proposed MGD-RL method can approximate the probability distribution of value functions by using mixture Gaussian distributions, thus reducing the training complexity of the designed RL. Simulation results demonstrate that, compared to a value decomposition network method and independent RL method, the MGD-RL method can improve the positioning accuracy of the target LEO satellite by up to 26.8% and 48.9%.

Existing communication signal-based sensing systems mainly rely on the orthogonal frequency division multiplexing (OFDM) technique due to its remarkable communication performance. However, extracting Doppler shifts from the received signal is not straightforward for OFDM and usually requires additional operations. The recently emerging orthogonal time frequency space (OTFS) modulation, which employs the Delay-Doppler (DD) domain for data transmission, can reveal the physical wireless propagation environments and provide the DD information directly. This paper investigates the moving multi-target sensing problem based on OTFS signaling. In particular, we attempt to tackle sensing and data association tasks concurrently by using the time delay (TD) and Doppler information from OTFS channel estimation. To this end, we formulate a mixed-integer optimization problem and approximate it as a convex problem. Simulation results has demonstrated the effectiveness of the proposed method.

Given the crucial role of the earth observation satellites in numerous key applications, accessing the Low Earth Orbit (LEO) satellite Internet via Laser Inter-Satellite Links (LISLs) has emerged as a promising solution to help transmit substantial amounts of observation data to ground stations. For Very Low Earth Orbit (VLEO) observation satellites, optimizing the inter-layer topology between themselves and LEO communication satellites has become paramount. To mitigate existing centralized algorithms’ reliance on global data transfer requirement information, a Novel Distributed Interactive Mechanism (NDIM) for inter-layer LISLs establishment is proposed. Here, the VLEO observation satellite decides its own access strategy based on local network information gleaned from three information exchanges with the LEO communication satellite. Additionally, considering the transmission requirements of observation satellites and the remaining load of communication satellites, a multi-objective topology optimization model is formulated, leading to the proposal of a Distributed Multi-objective inter-layer Topology Optimization (DMTO) algorithm. Our algorithm is novel in considering the remaining loads of the intermediary communication satellites, and it allows for the transmission of streaming observation data. We perform simulations using real task data (from Typhoon LEKIMA), and our results show that DMTO improves over existing baselines in terms of observation data throughput and communication satellite load balancing.

Space-air-ground integrated networks (SAGIN) are pivotal for achieving uninterrupted in-flight connectivity (IFC). Most existing studies, however, merely treat satellites as transparent forwarding nodes, and overlook their caching capabilities in enhancing the IFC data rate. In this paper, we consider an IFC-oriented SAGIN, where the satellites collaboratively deliver the content to airborne passengers to facilitate airborne communication. Considering the cached files instantaneously accessible via satellites, this work pioneers the integration of multiple inter-satellite links (ISLs) into the IFC framework, thereby innovating the content delivery process. To minimize the average delay of content delivery, we formulate an optimization problem and propose an exact penalty-based method to derive the satellite association scheme. Our proposed framework has a low complexity and thus paves the way for high-speed Internet connectivity to aviation passengers. Finally, simulation results are presented to demonstrate the effectiveness of our proposed IFC framework for SAGIN.

Integrated sensing and communication (ISAC) networks constitute a crucial paradigm for facilitating numerous advanced services in future wireless networks. This paper investigates the joint bandwidth and power allocations for device-to-device (D2D) communications aided ISAC in which D2D pairs complete their data transmissions by using the bandwidth allocated by the base station (BS) while providing sensing services for the BS. To this end, we formulate a joint optimization of bandwidth allocation and power allocations for both the target sensing and data transmission of each D2D pair, with the objective of maximizing a system-wise gain that accounts for both performances of target sensing and D2D data transmission. Despite the formulated optimization problem is strictly non-convex, we develop an efficient algorithm based on Lagrangian duality and sequential convex programming for solving it. Simulation results demonstrate that our proposed D2D communications aided ISAC is both accurate and efficient over several benchmark schemes.

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

Along with the advancement of WiFi technologies, commodity devices of WiFi 6E have now prevailed. In this paper, we conduct a wireless sensing task that utilizes WiFi 6E devices to monitor the respiration pattern of a human being. We discover new features, as well as their causes, in the channel state information (CSI) measured from WiFi 6E devices, which significantly differ from those in legacy IEEE 802.11n/ac/ax devices. As a result, directly applying conventional sensing techniques based on the CSI hardly yields satisfactory performance. In response, we propose a new method that effectively rectifies the measurement defects and recovers the targeted signal. We carry out extensive experiments, the results of which show that our approach substantially outperforms state-of-the-art benchmarks in sensing accuracy, verifying the effectiveness of our scheme.

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), where one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC), intending to fulfill the users’ diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Source Information (AomsI) metric. Under this framework, we formulate the optimization problem aiming to minimize the average AomsI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose the parallel optimization method to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, and then propose the task-associated genetic algorithm (TAGA) to design the UAV’s visiting order. Simulation results demonstrate that the UAV tends to prioritize the collection of all SN data required by the same user during data collection.

In this paper, we propose a new framework by combining the concepts of maximum distance separable (MDS) code and generalized spatial modulation (GSM) for multiple-input multiple-output (MIMO) transmission, namely MDS-GSM-MIMO. In our design, we exploit the powerful MDS code to increase the minimum Hamming distance (MHD) of the code-words, up to 2, and use the concept of space-domain index modulation to enlarge the minimum Euclidean distance (MED) of the achieved multidimensional GSM constellations. Moreover, we add the maximum minimum distance (MMD) precoder and guaranteed Euclidean distance (GED) precoder to enhance the MED further. Therefore, the MDS-GSM-MIMO we proposed with precoding is capable of optimizing both the overall MHD and MED. Then, we conduct theoretical analyses and comparisons with regard to average bit error rate (ABER) bound, MHD and MED of the conventional GSM-MIMO and the proposed MDS-GSM-MIMO. Simulation results show that the MDS-GSM-MIMO exhibits a BER performance gain of 3 dB compared to conventional GSM-MIMO, while the proposed MDS-GSM-MIMO with precoding exhibits a BER performance gain of up to 4 dB compared to the counterpart without precoding.

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

Integrated sensing and communication (ISAC) has been proved as a promising technique to further improve the performance for both the communication and the sensing tasks in wireless networks. However, due to the complicated and dynamic interference circumstances, the performance of ISAC cannot be guaranteed. To provide some insights for keeping a sophisticated balance between communication and sensing with considering co-channel interference, the theoretical performance of ISAC is studied in this paper. First, an analytical system model is provided based on stochastic geometry. Second, the success serving probability (SSP) is defined for both the communication and the sensing tasks based on mutual information, which provided a unified analysis framework for ISAC. The tractable expressions of SSP are also derived. Finally, the simulation results are shown to verify the analytical results of SSP, which can provide some insights for the tradeoff between sensing and communication of ISAC.

Combining half-duplex (HD) and full-duplex (FD) is promising in improving the information transmission rate of relay channels. This work proposes a novel two-phase hybrid duplex scheme for Gaussian relay channel where the relay operates in FD mode for a fraction of time and only transmits information for the rest of time. The achievable rate of the proposed hybrid duplex scheme is characterized in detail. Based on the obtained result, a joint time division and power allocation problem is formulated to maximize the achievable rate. In particular, the formulated problem is solved through a two-step optimization method. Firstly, the optimal relay power allocation is obtained for given time division factors. Then, the achievable rate maximization problem is addressed by finding the optimal time division factors. The closed-form expression for the maximal achievable rate is derived for some specific cases. Numerical results show that the proposed two-phase hybrid duplex scheme significantly improves the achievable rate of Gaussian relay channel compared with existing benchmark schemes.

Emerging as an important enabling technology for smart factories, ultra-reliable low latency communications (URLLC) have attracted extensive attention from academia and industry. In this paper, we aim to improve the performance of downlink URLLC in a smart factory. We first construct the system model based on the 5G New Radio (NR) standard, which specifies the modulation scheme, resource block structure and achievable data rates under finite blocklength codes (FBC). Next, since it is challenging to fulfill all transmission requests with limited radio and power resources, we formulate the problem to maximize the network throughput while considering delay and reliability constraints. This is a mixed integer non-convex nonlinear problem that is difficult to solve directly. To be tractable, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution. Specifically, the flow scheduling sub-problem is transformed into a matching game (MG) and solved by a delayed acceptance-based algorithm. Also a local water-filling algorithm is utilized to solve the power allocation sub-problem. Simulation results reveal that our proposed scheme outperforms other benchmark schemes.

In conventional federated learning (FL), there exists a single point of failure in the central server. Thus the studies about decentralized federated learning (DFL) paradigm have become popular recently. In DFL, some clients with poor computation capacity may take a long time to train local models, therefore, the convergence speed of the global model is usually slow in existing synchronous algorithms. In this work, we consider a large-scale system with device heterogeneity. To reduce training time and fully utilize edge node computation capacity, we propose an asynchronous algorithm in a novel multi-cluster decentralized federated edge learning (MD-FEEL) framework, where there are many clusters and each cluster consists of some clients. Our proposed asynchronous MD-FEEL contains four steps, i.e., local stochastic gradient descent (SGD) update, gradient consensus, intra-cluster model aggregation and inter-cluster model aggregation. To measure the staleness of cluster model, we introduce age of update (AoU) in inter-cluster aggregation stage and theoretically prove the convergence of our proposed algorithm on a non-convex setting. We evaluate our asynchronous MD-FEEL on MNIST and CIFAR-10 datasets and the simulation results show it can aggregate to a global model with better accuracy performance and faster convergence speed than some existing synchronous algorithms.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to the serious challenge, namely, the finite computation, energy and communication resources, which limits the achievable detection performance. In order to overcome this challenge, a UAV cognitive semantic communication system is proposed by exploiting knowledge graph. Moreover, a multi-scale compression network is designed for semantic compression to reduce data transmission volume while guaranteeing the detection performance. Furthermore, an object detection scheme is proposed by using the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that compared to the benchmark systems, our proposed system has superior detection accuracy, communication robustness and computation efficiency even under high compression rates and low signal-to-noise ratio (SNR) conditions.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme.

Satellites equipped with computing capabilities serve as invaluable access platforms for 5G and beyond (NextG) non-terrestrial networks (NTNs). They facilitate the continuous execution of resource-intensive edge-assisted deep learning (DL) tasks that are offloaded from Internet-of-Things (IoT) user equipment (UEs) in remote areas. To this end, satellite access network (SAN) resources need to be carefully “sliced”, consid-ering both the constrained energy availability and the scarcity of SAN resources. Existing SAN slicing approaches tend to treat offloaded tasks conventionally, overlooking the intricate semantics associated with DL tasks. In this paper, we propose semantic SAN (SemSAN), the first semantic SAN slicing algorithm for NextG AI-native NTNs. Our keen observations reveal that various DL tasks (i) can tolerate different degrees of image compression, and (ii) may yield equivalent model accuracy when employing DNN models with different sizes. These observations inspire us to further exploit the computation capability of a SAN to support more tasks while still minimizing overall energy consumption. After analyzing the characteristics of this optimization problem, we propose an online greedy SemSAN slicing algorithm to approximate its optimal solution. Extensive experiments verify the effectiveness of SemSAN in energy saving and its ability to support a substantial number of tasks, compared with other baselines.

Unmanned Aerial Vehicles (UAVs)-assisted networks play a pivotal role in both terrestrial base stations (BSs) and non-terrestrial networks (NTNs) due to their extensive coverage and collaborative decision-making capabilities. However, the presence of diverse node types, rapidly evolving requirements, and dynamic channel conditions poses substantial challenges for ground users (GUs) access, particularly in an unknown environment within BS-UAV-NTN integrated networks. To tackle these challenges, this paper introduces a novel approach-a deep Q-learning network (DQN)-based algorithm for UAVs deployment and an adaptive and load balancing (ALB) scheme for GUs access. This paper formulates the GUs access problem in BS-UAV-NTN networks as a maximization problem, transforming it into a Markov Decision Process (MDP) problem for UAVs deployment in unknown environment. The proposed solution includes a DQN-based UAVs deployment algorithm and an access scheme that prioritizes BSs and UAVs. Simulation results convincingly show that this access scheme outperforms traditional Q-learning and random schemes in terms of rewards and the number of accessed GUs.

Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational resources, it still suffers from resource waste. The primary reason is that end-users from the same area are likely to offload similar tasks to edge servers, thereby leading to duplicate computations. To improve system efficiency, the computation results of previously executed tasks can be cached and then reused by subsequent tasks. However, most existing computation reuse algorithms only consider one edge server, which significantly limits the effectiveness of computation reuse. To address this issue, this paper applies computation reuse in CEC networks to exploit the collaboration among edge servers. We formulate an optimization problem that aims to minimize the overall task response time and decompose it into a caching subproblem and a scheduling subproblem. By analyzing the properties of optimal solutions, we show that the optimal caching decisions can be efficiently searched using the bisection method. For the scheduling subproblem, we utilize projected gradient descent and backtracking to find a local minimum. Numerical results show that our algorithm significantly reduces the response time in various situations.

Motivated by the information transmission in neurons with various applications in in-vivo nano-machines or emerging medical applications, Ye and Elishco [2023] introduced a communication channel, called the absorption channel, and proposed codes correcting absorption errors. For a non-binary alphabet$\Sigma_{q}$, the authors presented constructions of codes of length$n$correcting a single absorption and the best construction yielded a redundancy of$\log_{q}n+12\log_{q}\log_{q}n+O(1)$symbols. In this work, we make progress on the code design problem above and show that the redundancy can be further reduced significantly as follows: When$q=3$, we construct “nearly optimal” ternary codes of length$n$with at most$\log_{3}n+5.43$redundant symbols. Note that such a redundancy is optimal up to a constant. • For a general alphabet$\Sigma_{q}$, we construct q-ary codes of length$n$correcting a single absorption error with$\log_{q}n+3\log_{q}\log_{q}n+O(1)$redundant symbols, providing an alternative. simpler construction that improves the results given by Ye and Elishco.

In this paper, for any fixed integer$q > 2$, we construct q-ary codes correcting a burst of at most$t$deletions with redundancy$\log n+8$log log$n$+ o(log log$n) >+\gamma_{q,t}$bits and near-linear encoding/decoding complexity, where$n$is the message length and$\gamma_{q,t}$is a constant that only depends on$q$and$t$. In previous works there are constructions of such codes with redundancy$\log n+O$(log$q$log log n) bits or$\log n+O$($t$2log log n)$+O(t\log q)$. The redundancy of our new construction is independent of$q$and$t$in the second term.

We optimize the Age of Information (AoI) in random access networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. We derive analytical expressions for the transmission success probability and time-average AoI using stochastic geometry tools. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the time-average AoI. In addition, we establish a scaling law for the time-average AoI in random access networks, revealing that the optimal time-average AoI increases linearly with the deployment density. Notably, the growth rate under TSA is half of that under conventional slotted ALOHA.

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design; second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters.

As an emerging technology, Digital Twin (DT) can provide a virtual presentation of the physical Intelligent Trans-portation Systems (ITS) to enhance the applications of ITS such as cooperation perception. In cooperative perception, accurate location is crucial for selecting proper cooperative vehicles (CoVs) to improve the perception performance. However, due to the high mobility of vehicles, the deviation between DT and physical world may lead to non-negligible location errors, which raises the challenges for achieving efficient CoV selection in cooperative perception. In this paper, we propose a DT-empowered multi-vehicle cooperative perception system, in which the CoV selection and bandwidth allocation are jointly optimized to improve the performance of cooperative perception. Specifically, an asyn-chronous federated learning scheme is deployed in DT for location prediction to mitigate the effect of the deviation. Based on the prediction results, the problem of joint predictive scheduling and bandwidth allocation is then formulated as the average delay minimization problem while reaching the required performances. The adaptive CoV selection and bandwidth allocation algorithm based on deep reinforcement learning is proposed to find the optimal scheduling strategy. Simulation results demonstrate that the proposed algorithm achieves the lowest average delay while effectively guaranteeing the performance requirement.

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE’s mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

Open Radio Access Networks (RAN) aim to bring a paradigm shift to telecommunications industry, by enabling an open, intelligent, virtualized, and multi-vendor interoperable RAN ecosystem. At the center of this movement, O-RAN ALLIANCE defines the O-RAN architecture and standards, so that companies around the globe can use these specifications to create innovative and interoperable solutions. To accelerate the adoption of O-RAN products, rigorous testing of O-RAN Radio Unit (O-RU) and other O-RAN products plays a key role. O-RAN ALLIANCE has approved around 20 Open Testing and Integration Centres (OTICs) globally. OTICs serve as vendor-neutral platforms for providing the testing and integration services, with the vision that an O-RAN product certified in any OTIC is accepted in other parts of the world. To demonstrate the viability of such a certified-once-and-use-everywhere approach, one theme in the O-RAN Global PlugFest Spring 2024 is to demonstrate consistent and repeatable testing for the open fronthaul interface across multiple labs. Towards this, Japan OTIC and Asia Pacific OTIC in Singapore have teamed up together with an O-RU vendor and Keysight Technology. Our international team successfully completed all test cases defined by O-RAN ALLIANCE for O-RU conformance testing. In this paper, we share our journey in achieving this outcome, focusing on the challenges we have overcome and the lessons we have learned through this process.

With the recent development of deep learning (DL), DL-based autoencoder techniques provide a novel paradigm for end-to-end physical layer optimization. In this paper, we address the dynamic interference in an end-to-end communication system with a multiuser Gaussian interference channel. In this context, the standard constellation is not optimal under high interference conditions. To address this issue, we propose an adaptive learning algorithm for learning and predicting dynamic interference. Note that existing DL-based autoencoders are unable to train end-to-end learning systems by deep learning without a known channel. Thus, we propose a generative adversarial network (GAN)-based training scheme to imitate the real channel. Simulation results show that compared with traditional PSK and QAM modulation schemes, our proposed adaptive learning-based auto encoder can achieve significantly lower block error rate (BLER) in presence of interference. Besides, the BLER performance of our proposed GAN-based training scheme is close to that of the optimal training scheme with known channel on different channel models.

As one of the recently proposed attractive multi-carrier waveforms towards high-mobility scenarios for 6G and beyond, the chirp-based affine frequency division multiplexing (AFDM) is capable of adapting to cope with large Doppler frequency shifts, which drastically deteriorates the orthogonality between orthogonal frequency division multiplexing (OFDM) subcarriers. Under doubly selective channels with given delay-Doppler profiles, existing works have proved that the classic AFDM requires a minimum number of subcarriers (such as the number of subcarriers is no less than 6, i.e.,$N\geq 6$) to achieve its full diversity. In this paper, we propose a novel optimized waveform design based on AFDM by combing the concept of index modulation (IM) and repetition coding (RC) in order to achieve additional diversity gain for the case that fewer subcarriers are available, and the proposed scheme is termed as index modulation-repetition coding-aided AFDM (IMRC-AFDM), where the main design idea is to perform the RC to complex-valued symbols to achieve diversity gain, while the transmission rate loss resulting from RC is compensated by exploiting the benefits of IM. Simulations results validate the benefits of our IMRC-AFDM scheme and show the performance gain of the proposed scheme over the original plain scheme.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

Future wireless systems are supposed to provide high-resolution sensing services via communication signals. Under this background, multiband sensing has recently become a promising technology due to that it can improve the sensing performance by jointly utilizing multiple non-contiguous frequency bands at a low cost. However, few studies have investigated the fundamental limits of multiband sensing, especially in the presence of phase distortion factors. In this paper, we investigate the fundamental limits of multiband sensing in terms of time delay. We derive a closed-form expression of the Cramér-Ran bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called the statistical resolution limit (SRL) is employed to investigate the fundamental limits of delay resolution. The fundamental limits of delay estimation error are also investigated based on the CRB and Ziv-Zakai bound (ZZB). Based on the above derived fundamental limits, numerical results are presented to provide key insights on the performance limits of the multiband sensing.

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based SemCom. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation based on the realistic F composite channel fading model. The code is available at https://github.com/wty2011jl/SCDGSC.git.

Integrated sensing and communication (ISAC) provides a spectrum-efficient approach for simultaneously enabling reliable data transmission and high-quality sensing. This paper investigates an ISAC-enabled multi-device cooperative sensing system in which the devices perform cooperative sensing towards multiple targets in a time-division manner. Within the allocated time, each device senses the targets and transmits data to the base station simultaneously via ISAC. To investigate this problem, we formulate a joint optimization of the beamforming for both sensing and transmission as well as the time allocation for different devices, aiming at maximizing the total throughput of the devices while guaranteeing the multi-target sensing quality, the cooperative sensing requirement and the fairness in data transmission. To tackle the non-convexity of the formulated problem, we first decompose the problem into a beamforming subproblem and a time allocation subproblem. Subsequently, we transform the beamforming subproblem into a tractable form. We then analyze the feature of the optimal time allocation in the time allocation subproblem while providing its semi-analytical expression, based on which we further propose an efficient algorithm to solve the original problem. Simulation results validate the effectiveness of our algorithm and the performance advantages of our fairness-aware ISAC-enabled cooperative sensing in improving both throughput and cooperative sensing accuracy.

In this article, we consider multiple solar-powered wireless nodes (WNs) which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node user association, and uplink power control to effectively utilize the harvested energy and manage co-channel interference within a finite time horizon. The design goal is to ensure the fairness of WNs by maximizing the worst user rate. The joint design problem is highly nonconvex and requires causal (future) knowledge of the instantaneous energy state information (ESI) and channel state information (CSI), which are difficult to predict in reality. To overcome these challenges, we propose an offline method based on convex optimization that only utilizes the average ESI and CSI, where line-of-sight (LOS) and non-LOS (NLOS) channels are considered. The problem is solved by three convex subproblems with successive convex approximation (SCA) and alternative optimization. We further design an online convex-assisted reinforcement learning (CARL) method based on real-time environmental information. An idea of multi-UAV regulated flight corridors, based on the optimal offline UAV trajectories, is proposed to avoid unnecessary flight exploration by UAVs and enables us to improve the learning efficiency and system performance, as compared with the conventional reinforcement learning (RL) method. Computer simulations are used to verify the effectiveness of the proposed methods. The proposed CARL method provides 25% and 12% improvement on the worst user rate over the offline and conventional RL methods.

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving. It exploits the ubiquitous service coverage and enables tasks to be offloaded to various components, such as high-altitude platform (HAP), unmanned aerial vehicle (UAV), and roadside unit (RSU). In this article, we address the challenge of minimizing the overall task offloading delay in the air-ground integrated VEC network through a joint multicomputation equipment selection and multidimensional resource allocation (JCESRA) problem. Considering the nonconvexity inherent in the problem, we employ the fundamental idea of the block coordinate descent (BCD) method to tackle it. Initially, we exclude the HAP and decompose the primal problem into three subproblems: 1) low-altitude computation equipment selection; 2) joint bandwidth and computation resource allocation; and 3) UAV trajectory design. The first subproblem, which involves integer programming, is solved by using the many-to-one matching method. Meanwhile, we utilize the CVX and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize the coalition game method to deal with it. Based on the solutions of the three subproblems, the JCESRA algorithm without considering the HAP has been proposed. Subsequently, we consider the HAP into the problem. Because the task offloading decision and computation resource allocation of the HAP problem can be viewed as a knapsack problem, we utilize the dynamic programming method to solve it. Because some tasks are offloaded to the HAP, there are some redundant computation resources in UAVs and RSU. We reallocate the computation resources of UAVs and RSU to further reduce the task offloading delay. At last, we present the complete JCESRA algorithm. The simulation results unequivocally indicate that the proposed JCESRA algorithm outperforms other algorithms by significantly reducing the task offloading delay.

Federated learning (FL) has been recognized as a rapidly growing research area, where the model is trained over massively distributed clients under the orchestration of a parameter server (PS) without sharing clients’ data. This paper delves into a class of federated problems characterized by non-convex and non-smooth loss functions, that are prevalent in FL applications but challenging to handle due to their intricate non-convexity and non-smoothness nature and the conflicting requirements on communication efficiency and privacy protection. In this paper, we propose a novel federated primal-dual algorithm with bidirectional model sparsification tailored for non-convex and non-smooth FL problems, and differential privacy is applied for privacy guarantee. Its unique insightful properties and some privacy and convergence analyses are also presented as the FL algorithm design guidelines. Extensive experiments on real-world data are conducted to demonstrate the effectiveness of the proposed algorithm and much superior performance than some state-of-the-art FL algorithms, together with the validation of all the analytical results and properties.

In this article, we investigate the federated clustering (FedC) problem, which aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server (PS), meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a nonconvex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then, a novel FedC algorithm using differential privacy (DP) technique, referred to as DP- FedC, is proposed in which partial clients participation (PCP) and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP- FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of nonidentically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP- FedC. Then, some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP- FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.

Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this article, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the proposed TESC scheme to two emblematic text tasks: 1) sentiment analysis and 2) question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) the TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency and 2) the TESC scheme enjoys about 150% performance gain compared to the traditional communication method.

The expansion of the Internet of Things (IoT) has led to a significant surge in data flow over edge networks, posing substantial challenges to data mining and management. While federated edge learning (FEEL) effectively accomplishes global integration and local training based on the decentralized data sets, its deployment across expansive IoT networks introduces additional challenges. The primary issues stem from managing the interaction between the communication load and learning effectiveness. The communication loads driven by recurrent data exchanges between the user equipment (UE) and central servers exacerbate network congestion and latency issues. Moreover, the learning efficacy is undermined due to the typically nonindependent and identically distributed (non-IID) characteristics of real-world IoT data. In this article, a novel communication-efficient hierarchical FEEL framework is proposed to tackle these challenges. Specifically, UEs are adaptively clustered according to their link conditions, geographic locations, and data distributions. Small base stations (SBSs) collect local model updates from the UEs in their clusters and communicate with a macro base station (MBS) for the global model aggregation. To jointly maximize the communication gain (in terms of reducing latency) and the learning gain (in terms of improving accuracy), a clustering and resource allocation optimization problem is formulated, and a cross entropy-based method with low computational complexity is proposed. Numerical experiments validate that the proposed hierarchical FEEL system achieves fast convergence and significantly improves the system efficiency for various learning tasks and the system settings.

Multiband technology has recently received incremental attention for its ability to jointly utilize multiple noncontiguous frequency bands to achieve high-resolution delay estimation. In multiband scenarios, numerous signal processing algorithms for delay estimation have been proposed, while research on the fundamental limits remains under explored. In this article, we focus on the analysis of fundamental limits and the optimization of multiband delay estimation in orthogonal frequency division multiplexing (OFDM) systems. We derive a closed-form expression of the Cramer-Rao bound (CRB) for the delay separation to reveal useful insights. Additionally, a metric called statistical resolution limit (SRL) that provides a resolution performance bound is employed to research the fundamental limits of delay resolution. The fundamental limits of the delay estimation error are also investigated using the performance bounds CRB and Ziv-Zakai bound (ZZB). Based on these derived performance bounds, numerical results have been presented to analyse the effect of frequency band apertures and phase distortions on the fundamental limits of the multiband delay estimation error. Inspired by the analysis of fundamental limits, we formulate an optimization problem to find the optimal system configuration in multiband systems with the objective of minimizing the delay SRL. To solve this nonconvex constrained problem, we propose an efficient alternating optimization (AO)-based algorithm that iteratively optimizes the variables using the successive convex approximation (SCA) and 1-D search. Simulation results demonstrate the effectiveness of the proposed algorithm and give useful insights for the multiband system design.

Federated learning (FL) framework enables user devices to collaboratively train a global model based on their local data sets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Besides, to quantify the training performance, the utility of clustered model training is defined based on the analysis results. Then, aiming at optimizing the system utility, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two subproblems. First, given the results of device clustering, a low-complexity iterative algorithm based on the convex optimization theory is proposed to make the bandwidth allocation and the transmit power control. Then, according to the individual stability, a coalition formation algorithm is proposed for the device clustering. Finally, the real-data experiments on the classification tasks (e.g., MNIST, CIFAR-10, and CIFAR-100) validate the results of convergence analysis and advantages of the proposed algorithm in terms of the test accuracy.

In practical status updating systems, most decisions made at monitors are based on diverse data streams. Due to the cask effect, it can be cognised that the effectiveness of decisions is often constrained by the stalest one, i.e., the straggler among all the streams. This work studies the statistical characteristics of age of the stalest information (AoSI) which describes the timeliness of the stalest stream. The AoSI is defined as the time elapsed since the latest successfully received update of the currently stalest stream among all different streams at the monitor was generated. Peak age of the stalest information (PAoSI) is also studied for evaluating the worst cases, i.e., the peaks of AoSI process. We develop an analytical approach to derive the AoSI and PAoSI based on the per-stream age of information (AoI) and peak age of information (PAoI), for the multi-stream single-monitor system with separate status updating. In particular, to comprehensively characterize the timeliness of the stalest stream, the distributions of AoSI and PAoSI are derived in closed-form for the general multi-stream system. Moreover, we concisely derive the explicit expressions of the distributions and averages of AoSI and PAoSI, upon a typical two-stream case with the classical automatic repeat-request protocol. Finally, the accuracy of the theoretical analyses is validated by the numerical results. Appropriateness and advantages of the AoSI (PAoSI) are elaborated by comparing with the maximum average AoI (PAoI), i.e., the maximal one among the averages of all the per-stream AoIs (PAoIs).

Although numerous state-of-the-art deep neural networks have recently been proposed for malware classification, effectively detecting malware on a large-scale sample set and identifying zero-day or new malware variants still pose significant challenges. To address this issue, a deep hashing-based malware classification model is designed for malware identification, including two parts: ResNet50-based deep hashing for malware retrieval and voting-based malware classification. Specifically, multiple deep hashing models are developed by extracting the high-layer outputs (feature maps) from the ResNet50 trained with malware gray-scale images in the first part. In this case, to maximize the Hamming distance or dissimilarity among hash values computed with malware samples under different families, a ResNet50-based deep polarized network (RNDPN) is designed to return Top K similar samples. In the second part, we propose a majority-voting and a Hamming-distance-based voting for malware identification according to the retrieved results. The experiment results show that RNDPN outperforms the other six deep hashing models with 97.54% mean average precision (mAP) for malware retrieval when only 40 similar examples are retrieved, where the best results for all deep hashing models are observed with 48 bits hashing code length. Furthermore, the Hamming distance-based voting method implemented with RNDPN demonstrates unparalleled performance in malware classification compared to other models. Notably, it achieves exceptional results in two key aspects: malware classification accuracy with an impressive accuracy rate of 96.5%, and the identification of new or zero-day malware with a commendable accuracy of 85.7%.

The Industrial Internet of Things (IIoT) is emerging as a promising technology that can accelerate the application of industrial intelligence to smart factories. Because of the sensitive nature of user data, federated learning (FL) which performs distributed machine learning while preserving data privacy, is leveraged to meet the accuracy and privacy requirements of IIoT end devices/clients. However, the unreliable communications in IIoT may result in possible single-point failures in the typical single-server FL framework, thereby negatively affecting the training efficiency. In this paper, we study on the client scheduling problem in a multi-server FL framework for the communication reliability and training efficiency improvement. Specifically, we focus on a semi-decentralized FL (SD-FL) framework, where edge servers and clients collaborate to train a shared global model through unreliable intra-cluster model aggregation and inter-cluster model consensus because of the model transmission error in client-server and server-server communication. Then, a client-server association optimization problem is formulated, with the objective of minimizing the global training loss. Resorting to the convergence analysis of SD-FL, the original problem is simplified and transformed into an integer nonlinear programming problem to guide us to design a high-efficiency client scheduling scheme. Finally, experimental results show that the proposed scheme significantly outperforms the baselines in terms of the test accuracy and training loss.

With the development of the future wireless communication technology and the Internet of Things (IoT), the digital twin (DT) system has become a new enabler for high-efficiency sensing in industrial applications. However, traditional DT designers may encounter a challenging situation for highly dynamic mobile entities in large-scale unmanned aerial vehicle (UAV) application scenarios. It has a direct influence on accurate and real-time sensing. To address the issue, we propose a hierarchical DT-enhanced cooperative sensing architecture. We proposed an intelligent DT model acquisition algorithm for real-time DT model construction. The accuracy of DT models is improved through our proposed model aggregation algorithm for accurate cooperative sensing. In addition, we propose a model transfer algorithm to perform a real-time cooperative sensing manner. We demonstrate the effectiveness of the proposed architecture using a multitarget tracking case study. The results show that our solution provides an accurate and real-time mobile sensing performance in the case study, with up to 90% sensing accuracy, under an acceptable system latency, compared to the traditional centralized and distributed DT manners.

Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.

This paper investigates a location-based downlink transmission scheme to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, the satellite network employs multicast communication to disseminate information to multiple satellite users, while the terrestrial network incorporates non-orthogonal multiple access (NOMA) with intelligent reflecting surface (IRS) technology to serve terrestrial users. Given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by optimizing the transmit power and beamforming (BF) weight vector at the base station, the IRS phase shift vector, and the power allocation factor, while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first propose a low-complexity BF algorithm based on the LoI-CSI, which simplifies the optimization problem while guaranteeing the QoS requirement of the satellite network. Then, assuming that terrestrial links experience Rician fading, we derive an approximate yet accurate OP of the terrestrial network, which is explored to calculate the phase shift vector. Furthermore, we propose a novel power allocation method that employs an exponential-type approximation of the first-order Marcum Q-function, to obtain the power allocation coefficient. Finally, simulation results confirm the theoretical formulas’ validity and reveal the proposed algorithms’ superiority in system performance.

We consider a service cost minimization problem for resource-constrained small-cell networks with caching, where the challenge mainly stems from (i) the insufficient backhaul capacity and limited network bandwidth and (ii) the limited storing capacity of small-cell base stations (SBSs). Besides, the optimization problem is NP-hard since both the users’ mobility patterns and content preferences are unknown. In this paper, we develop a novel mobility-aware joint routing and caching strategy to address the challenges. The designed framework divides the entire geographical area into small sections containing one SBS and several mobile users (MUs). Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs and make a caching decision. The FRPL method completes multiple tasks in one shot, thus reducing the average processing time per global aggregation of learning. By exploiting the outcomes of FRPL together with the estimated service edge of SBSs, the proposed cache placement solution greedily approximates the minimizer of the challenging service cost optimization problem. Theoretical and numerical analyses show the effectiveness of our proposed approaches.

Timely status updating is the premise of emerging interaction-based applications in the Internet of Things (IoT). Using redundant devices to update the status of interest is a promising method to improve the timeliness of information. However, parallel status updating leads to out-of-order arrivals at the monitor, significantly challenging timeliness analysis. This work studies the Age of Information (AoI) of a multi-queue status update system where multiple devices monitor the same physical process. Specifically, two systems are considered: theBasic System, which only has type-1 devices that are ad hoc devices located close to the source, and theHybrid System, which contains additional type-2 devices that are infrastructure-based devices located in fixed points compared to theBasic System. Using the Stochastic Hybrid Systems (SHS) framework, a mathematical model that combines discrete and continuous dynamics, we derive the expressions of the average AoI of the considered two systems in closed form. Numerical results verify the accuracy of the analysis. It is shown that when the number and parameters of the type-1 devices/type-2 devices are fixed, the logarithm of average AoI will linearly decrease with the logarithm of the total arrival rate of type-2 devices or that of the number of type-1 devices under specific condition. It has also been demonstrated that the proposed systems can significantly outperform the FCFS M/M/Nstatus update system.

The integration of unmanned aerial vehicles (UAVs) and marine communication networks has been emerging as a promising paradigm to cater for the growing maritime activities, e.g., marine environment monitoring and ocean resource exploration. The increasing growth of marine applications and services poses challenges for processing marine data, while the resources-limited UAVs cannot satisfy the requirements of computing-intensive and energy consumption. In this paper, we consider a marine edge computing scenario with a group of UAVs and ocean beacon stations (OBSs) and propose a multi-UAV aided multi-access edge computing for marine networks from the perspective of system-welfare and energy-efficient design. Specifically, we propose a multi-task multi-access offloading scheme in marine edge computing networks, in which multiple UAVs can process their workloads locally or offload their partial workloads to multiple OBSs for processing. We consider the total utilities for completing all tasks as the system welfare, and measure the difference between the system welfare and energy consumption as the system revenue. A joint optimization problem is formulated by optimizing the OBS selection, the offloading ratio and the transmission duration, with the objective of increasing the system revenue in marine edge computing networks. We exploit a vertical decomposition architecture to solve the formulated non-convex problem via decomposing it into three sub-problems. Regarding each sub-problem, we propose efficient algorithms to derive the optimal solutions. We finally conduct simulations to verify the performance of the proposed algorithms. The results demonstrate that our proposed algorithms can achieve the best performance for improving the system revenue in comparison with several benchmark algorithms.

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)-based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.

Orthogonal chirp division multiplexing (OCDM) is a new multi-carrier scheme based on chirp spread spectrum (CSS) recently introduced and shown to be more robust to interference. In this paper, we propose a novel OCDM system based on index modulation (IM). In this scheme, information is conveyed not only byM-ary signal constellation in classic OCDM, but also by subchirp indices activated in accordance with the input bitstream. We design a receiver structure based on single-tap frequency domain equalization (FDE) and maximum likelihood (ML) detection. To address the exponential complexity growth caused by ML detection, we also propose a novel reduced-complexity maximum likelihood (RC-ML) detector. The new detector offers a comparable BER performance to the ML one with a substantially reduced complexity. A theoretical peak-to-average power ratio (PAPR) performance analysis of the new scheme is given to illustrate the advantages of combining OCDM with IM. We provide an extensive performance analysis of the new scheme in terms of bit error rate (BER), diversity gain, and minimum Euclidean distance (MED). Simulation results are presented to demonstrate that the PAPR and BER performances of the proposed scheme are significantly better than those of the OCDM scheme due to the information bits carried by the OCDM subchirp indices. Moreover, our numerical results verify the robustness of the system in the presence of carrier frequency offsets (CFO).

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.

In this paper, for a general full-duplex wireless MapReduce distributed computing network, we investigate the minimization of the communication overhead for a given computation overhead. The wireless MapReduce framework consists of three phases: Map phase, Shuffle phase and Reduce phase. Specifically, we model the Shuffle phase into a cooperative X network based on a more general file assignment strategy. Furthermore, for this cooperative X network, we derive an information-theoretic upper bound on the sum degree of freedom (SDoF). Moreover, we propose a joint interference alignment and neutralization (IAN) scheme to characterize the achievable SDoF. Especially, in some cases, the achievable SDoF coincides with the upper bound on the SDoF, hence, the IAN scheme provides the optimal SDoF. Finally, based on the SDoF, we present an information-theoretic lower bound on the normalized delivery time (NDT) and achievable NDT of the wireless distributed computing network, which are less than or equal to those of the existing networks. The lower bound on the NDT shows that 1) there is a tradeoff between the computation load and the NDT; 2) the achievable NDT is optimal in some cases, hence, the proposed IAN scheme can reduce the communication overhead effectively.

Integrated sensing and communications (ISAC) is an emerging technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited number of degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RISs) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.

Cell-free massive multiple-input-multiple-out (CF-mMIMO) systems provide limited coverage because of expensive wired fronthaul between access points (APs) and central processing unit (CPU). To address this challenge, we propose a novel framework where an unmanned aerial vehicle (UAV), acting as an aerial AP, works coherently with the ground APs to expand the coverage of conventional CF-mMIMO system. To fully utilize the spectrum resource, the wireless fronthaul between the CPU and UAV shares the total bandwidth with the radio access networks. Considering limited power supply of the UAV and for the goal of green communications, we formulate a weighted sum power minimization problem to jointly optimize downlink beamforming and fronthaul compression, as well as UAV placement. The formulated problem is a mixed timescale problem, thus we propose a two-timescale optimization framework in which the UAV placement is optimized in each long timescale based on statistical channel state information (CSI), then the downlink beamforming and fronthaul compression are optimized in each short timescale based on instantaneous CSI. Specifically, uplink-downlink duality and semidefinite relaxation (SDR) based alternating optimization techniques are introduced to find solutions to the short timescale issue, while successive convex approximation and SDR methods are invoked to find solutions to the long timescale issue. Finally, simulation results corroborate the performance of the proposed algorithm.

Driven by the demand of Industry 4.0, the integration of 5G and time-sensitive networking (TSN) is proposed to provide ubiquitous connection and deterministic transmission. However, the heterogeneous access mechanisms and scheduling resolutions between 5G and TSN make it still intractable to schedule 5G and TSN resources jointly. To address this issue, we develop the network function virtualization-enabled 5G-TSN framework to offer unified resource management, where flows are scheduled by network slicing and virtual network function embedding, respectively. Specifically, a novel full-path age of information (FP-AoI) model is proposed as a new metric of the 5G-TSN integrated scheduling by innovatively encapsulating the 5G system as the sampling process of the virtual TSN network. To tackle the long latency tail brought by 5G, the 5G and TSN scheduling is formulated by a risk-aware FP-AoI minimization problem. Then, a decomposition and augmentation-based joint scheduling (DAS) algorithm is proposed to solve this NP-hard problem by decomposing it into three subproblems. The first two subproblems are proved to be convex. For the third subproblem, i.e., TSN scheduling, a FP-AoI-driven TSN scheduling scheme (FvQI) is designed by constructing an augmented logical topology according to constraints of service function chain and TSN characteristics. It realizes the TSN scheduling with low complexity. Simulation results demonstrate that our algorithms offer higher reliability, efficiency, and service acceptance ratio than benchmarks. Moreover, the DAS algorithm achieves a better tradeoff between the performance of time cost and AoI violation ratio with a small optimality gap.

In wireless content caching networks (WCCNs), a user's content consumption crucially depends on the assortment offered. Here, the assortment refers to the recommendation list. An appropriate user choice model is essential for greater revenue. Therefore, in this paper, we propose a practical multinomial logit choice model to capture users' content requests. Based on this model, we first derive the individual demand distribution per user and then investigate the effect of the interplay between the assortment decision and cache planning on WCCNs' achievable revenue. A revenue maximization problem is formulated while incorporating the influences of the screen size constraints of users and the cache capacity budget of the base station (BS). The formulated optimization problem is a non-convex integer programming problem. For ease of analysis, we decompose it into two folds, i.e., the personalized assortment decision problem and the cache planning problem. By using structure-oriented geometric properties, we design an iterative algorithm with examinable quadratic time complexity to solve the non-convex assortment problem in an optimal manner. The cache planning problem is proved to be a 0-1 Knapsack problem and thus can be addressed by a dynamic programming approach with pseudo-polynomial time complexity. Afterwards, an alternating optimization method is used to optimize the two types of variables until convergence. It is shown by simulations that the proposed scheme outperforms various existing benchmark schemes.

With the rapid development of Mobile Edge Computing (MEC), various real-time applications have been deployed to benefit people's daily lives. The performance of these applications relies heavily on the freshness of collected environmental information, which can be quantified by its Age of Information (AoI). In the traditional definition of AoI, it is assumed that the status information can be actively sampled and directly used. However, for many MEC-enabled applications, the desired status information is updated in an event-driven manner and necessitates data processing. To better serve these applications, we propose a new definition of AoI and, based on the redefined AoI, we formulate an online AoI minimization problem for MEC systems. Notably, the problem can be interpreted as a Markov Decision Process (MDP), thus enabling its solution through Reinforcement Learning (RL) algorithms. Nevertheless, the traditional RL algorithms are designed for MDPs with completely unknown system dynamics and hence usually suffer long convergence times. To accelerate the learning process, we introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness. Numerical results demonstrate that our algorithm outperforms the benchmarks under various scenarios.

Federated Learning (FL) has been extensively applied in urban environmental prediction tasks of mobile edge computing by training a global machine learning model without data sharing. However, the training of FL faces the challenges such as the poor generalization capability of a single global model over heterogeneous data and hefty communication overhead caused by the frequent model exchange between massive edge servers and remote cloud servers. To address such issues, we propose HPFL-CN, a novel communication-efficient Hierarchical Personalized Federated edge Learning framework with Complex Network clustering. HPFL-CN introduces Privacy-preserving Feature Clustering (PFC) to extract privacy-preserving low-dimensional feature representations of each edge server via mapping the environmental data to different complex network domains for clustering similar edge servers accurately. Based on the clustering results of PFC, anedge-mediator-cloudhierarchical architecture is proposed to realize personalization at the cluster level by Effective Hierarchical Scheduling (EHS). Furthermore, to adapt to dynamic scenarios of new edge servers joining and streaming data generation, we further extend HPFL-CN to Adaptive personalized federated learning with dynamic grouping (Ada-HPFL-CN), which can flexibly re-group edge servers and adjust mixed model weights and the model aggregation frequency adaptively. Our extensive experiments on real-world datasets demonstrate the efficacy of our framework, which outperforms state-of-the-art FL methods regarding personalization and communication efficiency performance.

Many real-time applications of the Internet of Things (IoT) need to deal with correlated information generated by multiple sensors. The design of efficient status update strategies that minimize the Age of Correlated Information (AoCI) is a key factor. In this paper, we consider an IoT network consisting of sensors equipped with the energy harvesting (EH) capability. We optimize the average AoCI at the data fusion center (DFC) by appropriately managing the energy harvested by sensors, whose true battery states are unobservable during the decision-making process. Particularly, we first formulate the dynamic status update procedure as a partially observable Markov decision process (POMDP), where the environmental dynamics are unknown to the DFC. In order to address the challenges arising from the causality of energy usage, unknown environmental dynamics, unobservability of sensors' true battery states, and large-scale discrete action space, we devise a deep reinforcement learning (DRL)-based dynamic status update algorithm. The algorithm leverages the advantages of the soft actor-critic and long short-term memory techniques. Meanwhile, it incorporates our proposed action decomposition and mapping mechanism. Extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with available DRL algorithms for POMDPs.

Federated edge learning(FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model-updates with the server to complete the global model training. Due to limited bandwidth and uncertain wireless environment, FEEL may impose heavy burden to the current communication system. In addition, under the common FEEL framework, the server needs to wait for the slowest device to complete the update uploading before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. In view of these practical issues, we formulate a training time minimization problem, with the compression ratio and deadline to be optimized. To this end, an asymptotically unbiased aggregation scheme is first proposed to ensure zero optimality gap after convergence, and the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is solved in an alternating manner, based on which, the noveljoint compression and deadline optimization(JCDO) algorithm is derived. Numerical experiments for different use cases in FEEL including image classification and autonomous driving show that the proposed method is nearly 30X faster than the vanilla FedSGD algorithm, and outperforms the state-of-the-art schemes.

Hyperspectral tensor completion (HTC) for remote sensing, critical for advancing space exploration and other satellite imaging technologies, has drawn considerable attention from recent machine learning community. Hyperspectral image (HSI) contains a wide range of narrowly spaced spectral bands hence forming unique electrical magnetic signatures for distinct materials, and thus plays an irreplaceable role in remote material identification. Nevertheless, remotely acquired HSIs are of low data purity and quite often incompletely observed or corrupted during transmission. Therefore, completing the 3-D hyperspectral tensor, involving two spatial dimensions and one spectral dimension, is a crucial signal processing task for facilitating the subsequent applications. Benchmark HTC methods rely on either supervised learning or nonconvex optimization. As reported in recent machine learning literature, John ellipsoid (JE) in functional analysis is a fundamental topology for effective hyperspectral analysis. We therefore attempt to adopt this key topology in this work, but this induces a dilemma that the computation of JE requires the complete information of the entire HSI tensor that is, however, unavailable under the HTC problem setting. We resolve the dilemma, decouple HTC into convex subproblems ensuring computational efficiency, and show state-of-the-art HTC performances of our algorithm. We also demonstrate that our method has improved the subsequent land cover classification accuracy on the recovered hyperspectral tensor.

On-demand and resource reservation pricing models, widely used in cloud computing, are currently used in Multi-Access Edge Computing (MEC). Nevertheless the edge's resources are distributed and each server has lower capacity. If too much resources were reserved in advance, on-demand users may not get their jobs served on time, jeopardizing MEC's latency benefits. Concurrently, reservation plan users may possess un-used quota. Therefore, we propose a sharing platform where reservation plan users can re-sell unused resource quota to on-demand users. To investigate the mobile network operator's (MNO‘s) incentive of allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game and characterize the optimal strategies of buyers and re-sellers. We show that users’ actions give rise to 4 different outcomes at equilibrium, dependent on the prices and supply levels of the sharing and on-demand pools. Based on the 4 possible outcomes, we characterise the MNO's optimal prices for on-demand users. Numerical results show that having both pools gives the MNO an optimal revenue when the on-demand pool's supply is low, and unexpectedly, when the MNO's commission is low. We develop an interactive prototype, and show that users’ decision distributions in studies on our prototype are similar to that of our decision model.

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, and effective solution design. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friendly expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.

Backscatter communications (BCs), allowing passive devices to transmit information by reflecting incident RF signals, have emerged as an attractive solution for the green Internet of Things (IoT). In the practical implementation of BC systems, we observe two common and interesting phenomena: wavy backscatter signals and striped-shape constellation clusters. These phenomena differ significantly from the traditional point-to-point communication and the theoretical BC systems, substantially degrading the system performance. Unfortunately, their causes and potential solutions remain unexplored. Motivated by this, this paper investigates the origins and designs of the corresponding solving methods. Specifically, we first reveal the causes of these phenomena: the time-varying interference stemming from the phase-locked loop (PLL) non-ideality. Then, we introduce our solutions: the dynamic self-interference cancellation (DSIC) and the data-aided decision boundary (DDB) algorithms. Finally, we implement and evaluate our solutions on a practical BC platform. Experimental results show that our solutions can reduce the bit error rate (BER) by up to two orders of magnitude, extend the communication range by over three times, and maintain linear runtime complexity, demonstrating their effectiveness and applicability in practical BC systems.

Integrated sensing, communications and computing (ISCC) system has been emerged as a crucial paradigm for addressing the growing demand of emerging wireless applications that require both ultra-reliable low-latency computing and high-precision sensing. In this paper, we investigate a non-orthogonal multiple access (NOMA)-assisted integrated sensing and two-tier task offloading (ISTTO) system in which the multi-functional access point (AP) provides task offloading services for a group of edge computing users via NOMA while performing sensing towards a target. To balance the utilization of the computing resources across different tiers, the AP can further offload part of the received workloads to a group of cloudlet servers. To investigate this problem, we formulate a joint optimization of the AP’s transmit beamforming, the two-tier dedicated sensing signals, the two-tier computation offloading strategies and the associated allocations of the communication and computing resources, with the objective of minimizing the total energy consumption, while guaranteeing the required sensing performance over the total duration. Although the formulated joint optimization problem is strictly non-convex, we identify the features of its solutions and exploit a decomposition-based framework for solving it. Numerical results validate the accuracy and effectiveness of our proposed algorithm and show the performance advantages of our NOMA-assisted ISTTO scheme. Compared with several benchmark schemes, our NOMA-assisted ISTTO scheme achieves better performances in both sensing and task offloading, while suppressing the interference from undesired directions.

Integrated sensing and communication (ISAC) is a promising paradigm for supporting emerging wireless services and applications that require both high-throughput data transmission and accurate environment sensing. In this paper, we investigate the energy-efficient channel sharing aided ISAC with sensing scheduling, in which the ISAC base station (BS) can simultaneously sense multiple targets by reusing the channel of conventional cellular users. To investigate this problem, we formulate a joint optimization of the multi-target sensing scheduling, the BS’s transmitting beamforming, and its receiving beamforming for each sensing target, with the objective of maximizing the energy efficiency for radar sensing while guaranteeing each cellular user’s throughput requirement. Despite that the formulated joint optimization problem is strictly non-convex, we exploit a framework of alternating optimization and propose the corresponding algorithms for solving the problem. Specifically, we address the fractional structure of the objective function by utilizing Dinkelbach’s method. Then, we identify the convexity of the problem after semidefinite relaxation and obtain the beamforming by utilizing the Lagrange duality. Furthermore, we formulate the sensing scheduling problem as a matching game and solve it by adopting the swap matching. Numerical results validate the effectiveness of our proposed algorithms compared to some benchmark algorithms and show the performance advantage of our channel sharing aided ISAC in comparison with different schemes.

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN, where different types of channel state information (CSI) including no CSI, instantaneous CSI, and delayed CSI, are considered. According to the types of CSI, interference alignment, beamforming, and space-time precoding are designed at the satellite and terrestrial transmitter side, and meanwhile, the UAV-RIS is introduced for the cooperating interference elimination process. Additionally, the degrees of freedom (DoF) obtained by the proposed IM scheme are discussed in depth when the number of antennas on the satellite side is insufficient. Simulation results show that the proposed IM scheme improves the system capacity in different CSI scenarios, and the performance is better than the existing IM benchmarks without UAV-RIS, but the performance improvement is at the cost of the requirement on the elements of UAV-RIS.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem, which is guaranteed to converge. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

Aerial base stations (ABSs) flying in the air inject wireless networks more flexibility and agility beyond ground base stations (GBSs) to respond to spatio-temporal coverage demand. To fully unlock the potential of ABSs, a high-capacity, flexible and dynamic wireless backhaul provision is necessitated and integrated access and backhaul (IAB) architecture comes into the picture. In this paper, we investigate the availability of IAB architecture in dynamic aerial-terrestrial networks in terms of coverage probability (CP) and further explore the feasible region of IAB to promote aerial-terrestrial coverage enhancement. Specifically, the results show that the capability of IAB to promote aerial-terrestrial coverage enhancement would be diminished with the increases of ABSs flight speed and GBS density. The reason is found that relying on fixed GBSs to provide dynamic backhaul for flying ABSs would come with frequent handovers, which degrades network CP. On this account, to make IAB adapt to dynamic aerial-terrestrial networks, a mobility-adaptable IAB scheme is proposed where a distance thresholdLpis set to alleviate the negative effect caused by handovers. WithLpoptimized, the coverage performance of dynamic aerial-terrestrial IAB network is shown to be increased, especially in dense GBS regime.

Over-the-air computation (AirComp) is considered a transformative enabler for next-generation artificial intelligence (AI) services and wireless data aggregation via the electromagnetic waveform-superposition property of wireless multi-access channels (MAC). However, the conventional distortion metric, minimum square error (MSE), is imperfect and not universally applicable in specific AirComp scenarios in a low-signal-to-noise ratio (SNR) regime and under power budget constraint. Conversely, the average discriminant gain is studied for task-oriented AirComp AI services like classification but with inaccurate performance indication. To solve these problems, this work establishes a novel framework for AirComp systems from the mutual information (MI) perspective. First, we categorize the AirComp model into two distinct classes based on the source (sensing) data independence, namely diverse-targets (DT) AirComp and homogeneous-target (HT) AirComp. Both categories with different inputs like classical Gaussian and classification-based Gaussian mixture model (GMM), can be unified and assessed via MI criterion. Next, for the DT AirComp system, we introduce a novel MI-aided AirComp beamforming scheme employing majorization-minimization (MM) relaxation. As for the HT AirComp, we present a heuristic successive approximation (SA)-based beamforming method considering complex GMM inputs. We also provide the feedback and update protocol for AirComp tracking. Simulations validate the superior performance on AirComp throughput and task-oriented metrics such as classification accuracy with our proposed MI-aided beamforming schemes.

In this paper, we explore the integration of coordinated multipoint (CoMP) transmission and non-orthogonal multiple access (NOMA) in downlink cellular-connected UAV networks, which include both aerial users (AUs) and terrestrial users (TUs). AUs are categorized into CoMP-AUs and Non-CoMP AUs based on a comparison of the desired signal strength and the dominant interference strength. CoMP-AUs receive transmissions from two cooperative Base Stations (BSs) and form two exclusive NOMA clusters with two TUs, respectively. A Non-CoMP AU forms a NOMA cluster with a TU served by the same BS. Leveraging the tools of stochastic geometry, we propose an analytical framework to assess the performance of the CoMP-NOMA-based cellular-connected UAV network in terms of coverage probability and average ergodic rate. We demonstrate the superiority of the proposed CoMP-NOMA scheme by comparing it with three benchmark schemes, and further quantify the impacts of key system parameters on network performance. By harnessing the benefits of both CoMP and NOMA, we prove that the proposed scheme can provide a reliable connection for AUs using CoMP and enhance the average ergodic rate through the application of NOMA technique.

Secure communications are of paramount importance in spectrum sharing networks due to the allocation and sharing characteristics of spectrum resources. To further explore the potential of intelligent reflective surfaces (IRSs) in enhancing spectrum sharing and secure transmission performance, a multiple intelligent reflection surface (multi-IRS)-assisted sensing-enhanced wideband spectrum sharing network is investigated by considering physical layer security techniques. An intelligent resource allocation scheme based on double deep Q networks (D3QN) algorithm and soft Actor-Critic (SAC) algorithm is proposed to maximize the secure transmission rate of the secondary network by jointly optimizing IRS pairings, subchannel assignment, transmit beamforming of the secondary base station, reflection coefficients of IRSs and the sensing time. To tackle the sparse reward problem caused by a significant amount of reflection elements of multiple IRSs, the method of hierarchical reinforcement learning is exploited. An alternative optimization (AO)-based conventional mathematical scheme is introduced to verify the computational complexity advantage of our proposed intelligent scheme. Simulation results demonstrate the efficiency of our proposed intelligent scheme as well as the superiority of multi-IRS design in enhancing secrecy rate and spectrum utilization. It is shown that inappropriate deployment of IRSs can reduce the security performance with the presence of multiple eavesdroppers (Eves), and the arrangement of IRSs deserves further consideration.

Spectrum sensing and communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, we utilize intelligent reflecting surfaces (IRS) to simultaneously enhance spectrum sensing accuracy and the secrecy performance of secondary users (SUs) through physical layer security (PLS) techniques. Additionally, we employ IRS as a novel approach to achieve the target probability of detection. We formulate a joint sensing and transmission security optimization problem to maximize the sum secrecy rate of SUs under both perfect and imperfect channel state information (CSI). To transform the probability of detection into a tractable expression, we adopt a safe approximation for theQ-function. We use a computationally-efficient block coordinate descent (BCD)-based algorithm to optimize the beamforming design and IRS phase shifts alternately. Specifically, we employ theS-procedure to handle the semi-infinite constraints under the imperfect CSI case. Simulation results demonstrate that by leveraging IRS for spectrum sensing, we can significantly reduce the sensing time while achieving the required probability of detection and the probability of false alarm. Furthermore, our proposed scheme improves both sensing accuracy and secrecy rate in both cases compared to the benchmark schemes.

Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Federated learning (FL) has emerged as a suitable and appealing distributed learning paradigm to deploy these applications at the network edge. Despite the many successful efforts made to apply FL to wireless edge networks, the adopted algorithms mostly follow the same spirit as FedAvg, thereby heavily suffering from the practical challenges of label deficiency and device heterogeneity. These challenges not only decelerate the model training in FL but also downgrade the application performance. In this paper, we focus on the algorithm design and address these challenges by investigating the personalized semi-supervised FL problem and proposing an effective algorithm, named FedCPSL. In particular, the techniques of pseudo-labeling, and interpolation-based model personalization are judiciously combined to provide a new problem formulation for personalized semi-supervised FL. The proposed FedCPSL algorithm employs novel strategies, including adaptive client variance reduction, local momentum, and normalized global aggregation, to combat the challenge of device heterogeneity and boost algorithm convergence. The convergence property of FedCPSL is also thoroughly analyzed and shows that FedCPSL is resilient to both statistical and system heterogeneity, obtaining a sublinear convergence rate. Experimental results on image classification tasks are presented to demonstrate that the proposed approach outperforms its counterparts in terms of both convergence speed and application performance.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

Non-terrestrial networks (NTN) provide a revolutionary solution to bridge the digital divide in areas underserved by terrestrial network (TN). Particularly, low Earth orbit (LEO) constellations can substitute for offering data services to mobile devices like UAVs when flying into TN service-deficient areas. In this paper, viewing TN and NTN as both competitors and collaborators, we present a novel approach to optimize UAV mobility management in integrated TN and NTN, thereby improving network service continuity. Specifically, we enable UAVs to opportunistically handover (HO) between TN and NTN during flight to maintain reliable data reception while minimizing HO overhead. The decision to switch from TN to NTN involves comparative assessments of service capabilities and HO rates between two segments over time, considering their link quality variations during UAV flight, TN coverage distributions, and orbital dynamics of LEO satellites. Our system-level case studies, based on a practical LEO constellation, demonstrate the significant advantages of UAV HO planning in integrated TN and NTN over standalone TN or NTN for HO numbers and service rates. We also demonstrate that in various scenarios, our UAV mobility management solution consistently outperforms existing heterogeneous HO methods that underrate the dynamic differences in service capabilities between TN and NTN.

Spectrum sharing is promising as a solution to address the spectrum crunch by enabling the coexistence of different networks in the same frequency band. However, interference from concurrent transmissions remains an obstacle to further enhance spectral efficiency. Therefore, to overcome the bottleneck caused by multi-user interference, both rate-splitting multiple access (RSMA)-enabled underlay and overlay spectrum-sharing strategies are proposed in this paper. To facilitate a robust resource allocation design, the common and the private beamforming vectors as well as the common rate allocation are jointly optimized under the norm-bounded channel state information (CSI) error model to maximize the worst-case weighted sum rate (WSR) of the secondary networks. To address the formulated challenging non-convex quadratically-constrained resource allocation optimization problems, a computationally efficient successive convex approximation (SCA)-based algorithm capitalizing on semidefinite relaxation (SDR) is proposed. Simulation results demonstrate that the proposed algorithms outperform non-orthogonal multiple access (NOMA)-based benchmark schemes in worst-case WSR and robustness. Moreover, the results indicate that the proposed novel RSMA-enabled overlay spectrum-sharing strategy can offer a higher flexibility in resource allocation compared to their underlay counterparts. Furthermore, the tradeoff between interference management and spectral performance enhancement for the proposed RSMA-enabled overlay spectrum-sharing strategy is unveiled.

To realize an intelligent cooperative vehicle infrastructure system and high-level autonomous driving, the introduction of the joint communication and sensing (JCS) technique in vehicular networks is indispensable. With directional beamforming, the vehicles equipped with JCS systems could utilize unified radio-frequency transceivers and frequency band resources to achieve vehicle-to-infrastructure (V2I) communication and sensing functions in different directions, respectively. In this concept, we study the computation offloading problem for JCS-based vehicular networks. Specifically, we formulate a long-term multi-objective problem that jointly optimizes the task execution latency and the sensing performance of multiple vehicles. Owing to the time-varying V2I channel gain, the time-varying impulse response of sensed target, and the stochastic traffic, we reformulate it as a Markov decision process and propose a double-stage deep reinforcement learning-based offloading and power allocation (DDOPA) strategy to determine the task offloading and power allocation for each vehicle. Simulation results demonstrate the efficacy of the proposed strategy compared with different strategies, and show that the proposed DDOPA strategy can achieve a trade-off between execution latency and sensing performance.

To realize higher-level autonomous driving and advanced transportation applications, the introduction of the integrated sensing and communication (ISAC) technique in vehicular networks is indispensable. Different from the existing works, this paper investigates the power allocation problem for onboard ISAC systems of vehicles, during the vehicle-to-infrastructure communication, vehicle-to-vehicle communication and sensing progress, in case of the time-varying communication channel gains, the time-varying impulse responses of sensed targets, and the stochastic traffic. Note that both the inter-beam interference of a single vehicle and the inter-vehicle interference are important considerations. Specifically, we formulate a stochastic programming problem, which optimizes the sensing performance, subject to constraints on the network stability, power limits and quality-of-service requirements. Leveraging the Lyapunov optimization technique, this stochastic programming problem is transformed into a single-time slot non-convex problem. Taking advantages of genetic algorithm and particle swarm optimization (PSO), a hybrid meta-heuristic algorithm is designed to solve the non-convex problem. Typically, we improve the traditional PSO to balance the global search ability and local search ability of particles. Finally, a dynamic power allocation strategy is proposed. The theoretical analysis and simulation results show that this strategy achieves a communication performance-sensing performance tradeoff of [$ {\mathrm {O(}}1/V{\mathrm {)}} $,$ {\mathrm {O(}}V{\mathrm {)}} $] with$ V $being a control parameter.

Intelligent reflecting surface (IRS) is considered as an enabling technology for millimeter wave (mmWave) communications since it can provide an extra reflection link to increase the macrodiversity gains and improve the transmission performance. These advantages principally rely on the assumption that the blocking of direct and reflection links is mutually independent. However, due to the non-negligible sizes of blockages in some practical cases, both links may be simultaneously blocked by the same blockage, which is the so-calledblockage correlation. To this end, in this work we investigate the impact of blockage correlation in the IRS-assisted mmWave communication systems. Firstly, we provide a new joint line-of-sight/non-line-of-sight (LOS/NLOS) probability model by considering blockage correlation. The correlation coefficient of direct and reflection link states is also derived. Secondly, we study the effects of blockage correlation on the system performance of IRS-assisted mmWave communication systems by deriving the expression of transmission success probability. Moreover, we provide a deployment optimization algorithm for IRS when blockage correlation is considered. Finally, simulations verify our theoretical results and validate the IRS deployment optimization algorithm. It is shown that the widely-used independent blocking assumption not only always incur an overestimation of the performance of IRS-assisted mmWave communication systems, but also misdirect the optimal deployment of IRS.

Compared to reflective-only or transmissive-only reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) has the capability to simultaneously serve users on its both sides. Then, simultaneous indoor-and-outdoor communication (SIOC) has been commonly regarded as one of the most evident and promising applications of STAR-RIS in the next generation wireless networks. However, it introduces intricate interference for wireless networks, i.e., transmission and reflection interference, which would impact the network coverage and transmission capacity drastically. Unfortunately, there is a dearth of research investigating STAR-RIS-enabled SIOC from a network-level perspective. To fill the gap, by leveraging stochastic geometry, we provide the first comprehensive analytical framework for STAR-RIS-enabled SIOC networks. We investigate three commonly used STAR-RIS protocols, i.e., energy splitting (ES), time switching (TS) and mode switching (MS), and consider both unicast communication (UC) and multicast communication (MC) schemes. Exact and approximate expressions of transmission success probability and transmission capacity are derived. Furthermore, we also optimize the key parameters of STAR-RIS protocols to maximize the transmission capacity. Finally, via simulations, the accuracy of theoretical expressions is confirmed, the optimization of the key parameters is investigated, and the advantages of STAR-RIS over conventional RISs are also demonstrated. Moreover, we also conclude the recommended STAR-RIS protocols to UC and MC schemes, respectively.

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs.

A novel dynamic hybrid beamforming architecture is proposed to achieve the spatial multiplexing-power consumption tradeoff for near-field multiple-input multiple-output (MIMO) networks, where a switch module is integrated between the baseband digital and analog phase-shift module to control the number of activated RF chains. Based on this architecture, an optimization problem is formulated that maximizes the sum of achievable rates while minimizing the hardware power consumption. Both continuous and discrete phase shifters are considered. 1) For continuous phase shifters, a wavenumber-domain weighted minimum mean-square error (WD-WMMSE) algorithm is proposed, which exploits the sparsity of WD near-field channels to achieve the low-dimensional beamformer design. 2) For discrete phase shifters, a penalty-based layered iterative (PLI) algorithm is proposed. The closed-form analog and baseband digital beamformers are derived in each iteration. Simulation results demonstrate that: 1) the proposed dynamic beamforming architecture outperforms the conventional fixed hybrid beamforming architecture in terms of spatial multiplexing-power consumption tradeoff, and 2) the proposed algorithms achieve better performance than the other baseline schemes.

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

The growth of vehicular applications will inevitably require Base Stations (BSs) to simultaneously serve more Connected Vehicles (CVs) within limited bandwidth resources, which imposes a great challenge in interference management. Effective management of this interference is crucial for reliable Vehicle-to-Everything (V2X) communication, and necessitates accurate Channel State Information at the Transmitter (CSIT). In practice, the dynamic and unpredictable nature of CV movements prevents BS from obtaining perfect CSIT, leading to outdated information and threatening communication performance. In this study, we propose a Rate-Splitting Multiple Access (RSMA)-enabled V2X communication system to efficiently manage interference channels. We leverage a 1-layer RSMA scheme to relax the stringent requirement for perfect CSIT and enhance robustness to outdated information. Furthermore, we introduce Gruformer, a transformer-based model for improved CSIT prediction utilizing historical data. While longer forecasting horizons decrease accuracy, we present a game theory-based approach that significantly reduces processing time for power allocation, enabling timely decisions before CSIT becomes outdated. Simulation results reveal that Gruformer allows for more accurate predictions during rapid changes in channel conditions. Leveraging this high-quality CSIT, the proposed V2X system achieves a 20% increase in Weighted Ergodic Sum-Rate (WESR). Furthermore, the game theory-based approach delivers a 60% reduction in processing time while maintaining near-optimal performance.

Existing Rate-Splitting Multiple Access (RSMA) techniques offer a promise for Non-Terrestrial Networks (NTNs) by managing interference and ensuring reliable data transmission. However, precoder design remains a crucial bottleneck, demanding accurate Channel State Information (CSI) feedback and complex optimization, which are challenging in practical deployment. Motivated by this, this paper proposes a novel Deep Learning (DL)-based method to predict the precoder design from the historical CSI directly. In particular, we first establish a predictive beamforming protocol for precoder design using historical CSI, bypassing the need for constant feedback and reducing complexity. Subsequently, we formulate a general problem for precoder design, with the Weighted Ergodic Sum Rate (WESR) serving as the objective function. Solving this problem is particularly challenging due to the dynamic nature of wireless channels in NTNs. To address this, we designed a fusion model, named TranCN, which harnesses the strengths of Transformers and Convolutional Neural Networks (CNNs) to extract spatial-temporal features from historical CSI, thereby enhancing precoder performance. Simulation results demonstrate that our predictive beamforming scheme enables RSMA to adapt to dynamic channel conditions using historical CSI, surpassing baseline methods and improving data transmission resilience.

This paper focuses on leveraging coordinated multi-point (CoMP) to improve the sum downlink rate in air-terrestrial integrated networks. Considering the CoMP transmission, the problem of maximizing the sum downlink rate possesses two-timescale characteristics, i.e., trajectory planning varies of aerial base station in a long-timescale manner whereas CoMP resource allocation varies in a short-timescale manner. To solve it, a two-timescale parallel framework is proposed. Specifically, the initial two-timescale problem is decomposed into multiple single-timescale subproblems via the alternating direction method of multipliers and then all subproblems are parallelly solved. Furthermore, to resolve the high complexity arising from continuous-discrete hybrid variables in each single-timescale subproblem, we propose an online algorithm that embeds optimization programming (OP) into deep reinforcement learning (DRL). Particularly, discrete CoMP cluster variables in sequential time slots are optimized with DRL to eliminate the need for large-scale combinatorial optimization. Then, the continuous resource allocation variables in each time slot are solved by OP, which is embedded between the output and reward of DRL, to speed up the convergence. Simulation results show that the proposed algorithm achieves less than a 7% total downlink data gap while decreasing the computation time by more than an order of magnitude compared with numerical optimization.

We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. Using stochastic geometry tools, we derive analytical expressions for the transmission success probability, mean peak AoI, and time-average AoI. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the mean peak and time-average AoI, respectively. In addition, we establish a scaling law for the mean peak AoI and time-average AoI in mobile networks, revealing that the optimal mean peak AoI and time-average AoI increase linearly with the deployment density. Notably, the growth rate of time-average AoI under TSA is half of that under SA. When considering the optimal mean peak AoI, the TSA protocol exhibits comparable performance to the traditional slotted ALOHA protocol. These findings conclusively affirm the advantage of TSA in reducing higher-order AoI, particularly in densely deployed networks.

Federated learning is a promising technique to implement network intelligence for the sixth generation (6G) communication systems. However, the collected data in wireless networks is non-independent and identically distributed (non-IID), which leads to severe deterioration of model performance. Although various enhanced schemes are proposed, it is still challenging to balance the communication cost and the model performance, due to the scarcity of radio resource for model update in wireless networks. In this paper, an ensemble federated learning paradigm is proposed for handling non-IID data, which is also optimized for its deployment in wireless networks in a cost efficient way. First, the framework of ensemble federated learning is designed. By formulating individual user clusters, intra-cluster federated learning models can be generated to reduce the impact of non-IID data, which can be integrated to adapt to various learning data via model ensemble. Second, the optimization of user cluster formation is studied to improve the performance of ensemble federated learning, which is modeled as a coalition formation game to design a Nash-stable algorithm. Finally, the simulation results on the public data sets are provided to verify the performance gains of our proposed schemes for deploying federated learning with non-IID data in wireless networks.

In existing federated learning (FL), the base station (BS) coordinates devices to collaboratively train a shared model by avoiding the transmission of raw data. To achieve communication-efficient model uploading, over-the-air computation (AirComp) is often employed to aggregate model parameters. However, in conventional AirComp assisted FL, the BS’s abundant computation resources are underutilized due to its non-involvement in model training. Meanwhile, transmission failures resulting from fluctuating wireless channels impair the quality of model aggregation. In this paper, we propose a retransmission-based semi-federated learning (SemiFL) framework, wherein devices upload model parameters and public privacy-free data for enabling a hybrid implementation of FL and centralized learning (CL). In our new framework, the BS leverages its abundant computation resources to aid CL model training, which mitigates the resource wastage while alleviating local computational burden of devices. Moreover, the proposed new retransmission mechanism effectively overcomes detrimental transmission failures resulting from the fluctuating quasi-static channel, aiming to guarantee improved learning performance of SemiFL. Successful transmission probabilities of both retransmission-based AirComp and retransmission-based digital communication are provided in closed forms. To attain deep insights, we derive an optimality gap to capture the convergence behavior of retransmission-based SemiFL. Then, we formulate a non-convex long-term problem to minimize a weighted sum of overall latency and energy consumption by jointly optimizing communication, computation, and learning parameters. Extensive experimental results show that our retransmission-based SemiFL obtains 21.9%, 30.5%, and 44.1% accuracy gains on three datasets, while efficaciously reducing latency and energy consumption compared to benchmarks. Meanwhile, our scheme enhances learning performance on the fluctuating quasi-static channel compared to state-of-the-art schemes.

As a novel distributed learning paradigm, federated learning (FL) faces serious challenges in dealing with massive clients with heterogeneous data distribution and computation and communication resources. Various client-variance-reduction schemes and client sampling strategies have been respectively introduced to improve the robustness of FL. Among others, primal-dual algorithms such as the alternating direction of method multipliers (ADMM) have been found being resilient to data distribution and outperform most of the primal-only FL algorithms. However, the reason behind remains a mystery still. In this paper, we firstly reveal the fact that the federated ADMM is essentially a client-variance-reduced algorithm. While this explains the inherent robustness of federated ADMM, the vanilla version of it lacks the ability to be adaptive to the degree of client heterogeneity. Besides, the global model at the server under client sampling is biased which slows down the practical convergence. To go beyond ADMM, we propose a novel primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. In addition, FedVRA unifies several representative FL algorithms in the sense that they are either special instances of FedVRA or are close to it. Extensions of FedVRA to semi/un-supervised learning are also presented. Experiments based on (semi-)supervised image classification tasks demonstrate superiority of FedVRA over the existing schemes in learning scenarios with massive heterogeneous clients and client sampling.

The ultra-wide Terahertz (THz) band with jointly high-speed transmission and precise sensing has come into vision to realize integrated sensing and communication (ISAC) for emerging immersive applications. However, THz networks face a coverage bottleneck. Narrow beams are exploited to compensate for the limited signal power and path loss. But they bring in beam misalignment that degrades link connectivity and affects the THz network coverage, characterized by coverage probability. ISAC-THz networks can benefit from the sensing-aided beam alignment to improve the coverage probability. But there exists a trade-off between sensing assistance and its cost, that requires efficient resource allocation. This paper provides time-frequency resource allocation for sensing signal mapping schemes that maximize the coverage probability of the ISAC-THz networks with reduced sensing costs. Results show the effectiveness of the scheme in reducing the sensing cost with near-ideal coverage. We reveal design insights into the sensing signal insertion and preferable THz transmission band selection that achieves the desired coverage with the least sensing overhead. Wider coverage requires more sensing resources, which are more allocated to bandwidth for accurate long-range sensing. The high angular resolution of narrow beams helps reduce the sensing cost, sparing resources in the time domain for velocity estimation.

In integrated sensing and communication (ISAC) systems, communication signals are exploited to achieve high-accuracy sensing. Multiband Wi-Fi sensing, which jointly utilizes Wi-Fi signals from multiple non-contiguous frequency bands to improve the sensing performance, has recently emerged as a promising technology for ISAC. However, the multi-dimensional non-convex likelihood function associated with the multiband WiFi sensing contains many local optimums due to the existence of high frequency components and phase distortion factors in the signal model, making it difficult to exploit the multiband gain for high-accuracy parameter estimation. To address this, we divide the target parameter estimation into two stages equipped with different signal models derived from the original model, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only particle probability is optimized and the per-iteration computational complexity increases exponentially with particle count, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, the proposed SPVBI can achieve a better performance than the conventional PVBI with a much lower complexity. Finally, simulations verify the advantage of the proposed algorithm over various baseline algorithms.

This paper investigates a low-complexity downlink transmission to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by jointly optimizing the transmit power and beamforming (BF) weight vector at the base station (BS), and the phase shift vector at the intelligent reflecting surface (IRS), while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first use the LoI-CSI and propose a low-complexity BF algorithm to obtain the transmit power and BF weight vector. Then, employing IRS and non-orthogonal multiple access (NOMA) in the terrestrial network, we derive its closed-form expression for the OP of the terrestrial network, which is explored to calculate the IRS phase shift vector. Finally, simulation results confirm the validity of the theoretical formulas and reveal the proposed algorithms superiority in system performance.

Non-terrestrial networks (NTN) offer potential for efficient content broadcast in remote regions, thereby extending the reach of digital services. In this paper, we introduce a novel approach to optimize wireless edge content placement using NTN. Specifically, we dynamically select content for placement via NTN links based on popularity and suitability for delivery through NTN, while considering the orbital motion of LEO satellites. Our comprehensive system-level case studies, based on a practical LEO constellation, demonstrate the significant improvement in placement speed compared to existing methods that neglect network mobility. We further show that the advantages of NTN links over standalone wireless TN solutions are more pronounced in the early stages of content delivery and are amplified by higher content popularity correlation across geographical regions.

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

In this paper, we propose a semi-federated learning (SemiFL) framework to ameliorate the performance of conventional federated learning. The base station and devices are coordinated to collaboratively train a shared model. However, due to the rapidly fluctuating channels and irrationally assigned local learning workloads, SemiFL encounters excessive latency. To overcome the challenges, we propose a retransmission-based over-the-air computation mechanism to facilitate model aggregation and data mixing over quasi-static channels. The closed-form probability of successful aggregation is derived, while the communication latency is modeled based on the Pascal distribution. Further, we establish an optimality gap to characterize the convergence performance of SemiFL, wherein the minimum number of iterations for attaining a specific local target accuracy is identified. Next, a joint resource allocation and local target accuracy assignment problem is formulated to minimize the latency of each round, subject to the decay rate, central processing unit (CPU) frequency, and transmit power. To address this non-convex problem, we develop an algorithm using the closed-form solutions for the normalizing factors and CPU frequencies. Simulation results on two real-world datasets confirm the superiority of SemiFL over benchmarks in terms of latency and learning performance.

Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often hindered by challenges arising from limited communication and data heterogeneity. In this paper, we present a distributed training paradigm that employs analog over-the-air computation to alleviate the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue. As a result, it enhances the generalization and robustness of each client’s local model. We elaborate on the model training procedure and its advantages over conventional frameworks. We provide a convergence analysis that theoretically demonstrates the training efficiency. We also conduct extensive experiments to validate the efficacy of the proposed framework.

Recently, federated learning (FL) has been increasingly appealing in distributed signal processing and machine learning. Nevertheless, the practical challenges of label deficiency and client heterogeneity form a bottleneck to its wide adoption. Although numerous efforts have been devoted to semi- supervised FL, most of the adopted algorithms follow the same spirit as FedAvg, thus heavily suffering from the adverse effects caused by client heterogeneity. In this paper, we boost the semi-supervised FL by addressing the issue using model personalization and client-variance-reduction. In particular, we propose a novel and unified problem formulation based on pseudo-labeling and model interpolation. We then propose an effective algorithm, named FedCPSL, which judiciously adopts the schemes of a novel momentum-based client- variance-reduction and normalized averaging. Convergence property of FedCPSL is analyzed and shows that FedCPSL is resilient to client heterogeneity and obtains a sublinear convergence rate. Experimental results on image classification tasks are also presented to demonstrate the efficacy of FedCPSL over the benchmark algorithms.

This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL’s data heterogeneity issue. Specifically, we first analyze the effect of CS in FL and show that FL training can be accelerated by adequately choosing participants to diversify the training dataset in each round of training. Based on this, we lever-age data profiling and determinantal point process (DPP) sampling techniques to develop an algorithm termed Federated Learning with DPP-based Participant Selection (FL-DP3S). This algorithm effectively diversifies the participants’ datasets in each round of training while preserving their data privacy. We conduct extensive experiments to examine the efficacy of our proposed method. The results show that our scheme attains a faster convergence rate, as well as a smaller communication overhead than several baselines.

As the superior improvement on wireless network coverage, spectrum efficiency and energy efficiency, Intelligent reflecting surface (IRS) has received more and more attention. In this work, we consider a large-scale IRS-assisted heterogeneous cellular network (HCN) consisting of$K\ (K\geq 2)$tiers of base stations (BSs) and one tier of passive IRSs. With tools from stochastic geometry, we analyze the coverage probability and network spatial throughput of the downlink IRS-assisted$K$-tier HCN. Compared with the conventional HCN, we observe the significant gain achieved by IRSs in coverage probability and network spatial throughput. The proposed analytical framework can be used to understand the limit of gain achieved by IRSs in HCN.

In recent years, reliable and timely data collection from underwater acoustic sensor networks (UASNs) has attracted widespread attention in academia. For this proposal, we study the autonomous underwater vehicle (AUV)-based real-time mobile relaying network in UASNs. Relay placement determines the reliability of communication in the relay network. We first formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate the FRA with a three-dimensional ellipsoid mathematical formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the different-sized FRA constraints. To this end, we propose a nearest-community (N-C) trajectory planning algorithm and further propose a member grouping method to form communities. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the proposed N-C can successfully receive more packets per minute and consume fewer sensors' energy than other algorithms.

In wireless caching networks, users' content request behavior is a compelling aspect for maximizing the achievable revenue. Multinomial logit (MNL) choice model is commonly used to characterize the relationship between users' request behavior and the assortment decision. However, conventional MNL model assumes that the systems show an assortment of content items to users, and a user can purchase the items among the assortment or leave without consuming anything. Yet in most cases, users tend to observe the assorted items, and select within the list in accordance with their personal preference or just search for the items they are interested in by closing the assortment set directly. To address this issue, a revised MNL (RevMNL) choice model is proposed in this paper, wherein we presume that all the remaining items will be shown to the user if the pre-determined assortment set is unsatisfactory. Under which, we mathematically derive the content demanding probability distribution per user. Thereafter, the assortment decision-making problem is studied to maximize system's revenue, which is a non-convex integer programming problem. By using structure-oriented geometric properties, we design an iterative algorithm with quadratic time complexity to obtain the globally optimal solution to the formulated optimization problem. Extensive simulation results validate the superiority of our devised scheme in terms of system revenue and cache hit ratio when compared against various baselines under the conventional MNL model.

Mobile edge computing (MEC) is a promising paradigm to meet the quality of service (QoS) requirements of latency-sensitive IoT applications. However, attackers may eavesdrop on the offloading decisions to infer the edge server's (ES's) queue information and users' usage patterns, thereby incurring the pattern privacy (PP) issue. Therefore, we propose an offloading strategy which jointly minimizes the latency, ES's energy consumption, and task dropping rate, while preserving PP. Firstly, we formulate the dynamic computation offloading procedure as a Markov decision process (MDP). Next, we develop a Differential Privacy Deep Q-learning based Offloading (DP-DQO) algorithm to solve this-problem while addressing the PP issue by injecting noise into the generated offloading decisions. This is achieved by modifying the deep Q-network (DQN) with a Function-output Gaussian process mechanism. We provide a theoretical privacy guarantee and a utility guarantee (learning error bound) for the DP-DQO algorithm and finally, conduct simulations to evaluate the performance of our proposed algorithm by comparing it with greedy and DQN-based algorithms.

Emerging interaction-based Internet of Things (IoT) applications have stringent demand for timeliness, imposing critical challenges to the design of status update system. Using redundant devices to update the status of the same process is a promising way to improve timeliness, but this approach can result in out of order update arrivals, making it difficult to analyze timeliness. To that end, the present paper conducts a theoretical study toward the Age of Information (AoI) of a multi-queue status update system where multiple sensors observe one physical process and update a common monitor. Based on the stochastic hybrid systems method, the average AoI of the considered system is derived in closed form. The theoretical results are consistent with the simulation results, verifying the correctness of the theoretical analysis. It is shown that the logarithm of the average AoI is linearly decreasing with the logarithm of the number of sensors.

Spectrum sensing and the communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, intelligent reflecting surface (IRS) is exploited in CRNs to simultaneously enhance the spectrum sensing accuracy and the secure performance achieved by using physical layer security (PLS) techniques. The sum secrecy rate of the secondary users (SUs) is maximized by jointly optimizing the sensing time, the beamforming design and the IRS phase shifts. A safe approximation is adopted to transform the probability of detection into a tractable expression. A computationally efficient block coordinate descent (BCD)-based algorithm with the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR) is exploited to optimize the beamforming and the phase shifts alternately. Simulation results demonstrate that our proposed algorithm can significantly improve both the sensing performance and the secrecy rate compared with the benchmark schemes.

Node-similarity distributions not only characterize different types of complex networks, but also offer insights in the structural predictability of complex networks, and even facilitate prediction tasks in complex networks. By means of the generating function, we propose a framework to calculate the common neighbor based similarity (CNS) distributions, offering theoretical results of similarity distributions of various complex networks. Furthermore, we apply node-similarity distributions to link prediction, a key task in network analysis. Specifically, by deriving analytical solutions for two metrics: i) precision and ii) area under the receiver operating characteristic curve (AUC), we give theoretical evaluation of link prediction. Also, by analyzing i) the expected prediction accuracy of similarity scores and ii) optimal prediction priority of unconnected node pairs, we optimize link prediction with similarity distributions. Simulation results confirm our findings and also validate the proposed methods for evaluating and optimizing link prediction.

Personalized federated learning (PFL) has been widely investigated to address the challenge of data heterogeneity, especially when a single generic model is inadequate in satisfying the diverse performance requirements of local clients simultaneously. Existing PFL methods are inherently based on the idea that the relations between the generic global and personalized local models are captured by the similarity of model weights. Such a similarity is primarily based on either partitioning the model architecture into generic versus personalized components or modeling client relationships via model weights. To better capture similar (yet distinct) generic versus personalized model representations, we propose $\textit{spectral distillation}$, a novel distillation method based on model spectrum information. Building upon spectral distillation, we also introduce a co-distillation framework that establishes a two-way bridge between generic and personalized model training. Moreover, to utilize the local idle time in conventional PFL, we propose a wait-free local training protocol. Through extensive experiments on multiple datasets over diverse heterogeneous data settings, we demonstrate the outperformance and efficacy of our proposed spectral co-distillation method, as well as our wait-free training protocol.

We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open problem whether such discrete-valued mechanisms provide any privacy protection. In this paper, we study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP). More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms, including the binomial noise and the binomial mechanisms that are proposed for privacy preservation, and the sign-based methods that are proposed for data compression, in closed-form expressions. We further investigate the amplification in privacy by sparsification and propose a ternary stochastic compressor. By leveraging compression for privacy amplification, we improve the existing methods by removing the dependency of accuracy (in terms of mean square error) on communication cost in the popular use case of distributed mean estimation, therefore breaking the three-way tradeoff between privacy, communication, and accuracy.

This work studies the average Age of Information (AoI) of a remote monitoring system in which two sensors observe the same physical process and update the status to a common monitor using orthogonal channels. While using redundant devices to update the status of a process can improve the information timeliness at the monitor, the out-of-order arrivals of updates impose a challenge to the AoI analysis. We first model the system as two parallel M/M/1/1 queues. By leveraging tools from stochastic hybrid systems, we obtain analytically the average AoI of the system. In particular, when the arrival or service rates are the same for the two sensors, the average AoI is given in closed form. Our analysis reveals that the average AoI of the considered system is reduced by 16.44% compared to the single-sensor system when the arrival and service rates are equal to 1. Numerical results show that the considered system outperforms the M/M/2 system in average AoI at high arrival rates.

We investigate the performance of a random access network consisting of source-destination dipoles. The source nodes transmit information packets to their destinations over a shared spectrum. All the transmitters in this network adhere to an age threshold slotted ALOHA (TSA) protocol: every source node remains silent until the age of information (AoI) reaches a threshold, after which the source accesses the radio channel with a certain probability. We derive a tight approximation for the signal-to-interference-plus-noise ratio (SINR) meta distribution and verify its accuracy through simulations. We also obtain analytical expressions for the average AoI. Our analysis reveals that when the network is densely deployed, employing TSA significantly decreases the average AoI. The update rate and age threshold must be jointly optimized to fully exploit the potential of the TSA protocol.

Energy saving plays an important role in designing AI-native 6G networks. Radio Access Network (RAN) slicing is a fundamental tool to save energy through resource multiplexing. However, as the AI services required by users become more heterogenous than ever in 6G network, service-oriented RAN slicing naturally consumes a lot of energy, leading to a tradeoff between QoS guarantees and energy saving for the network scheduler to decide. In this paper, we propose sustainable service-oriented (SSO) RAN slicing scheduler for 6G networks to jointly optimize workload distribution and resource allocation. The target is to minimize the long-term average energy consumption using the meta reinforcement learning (MRL) method. To be specific, each type of services is treated as an independent optimization problem, where the workload distribution is solved by convex optimization and the resource allocation is solve by Q-learning policy. Numerical results show that SSO effectively reduces the system energy consumption while satifying QoS requirements, as compared with benchmarks.

Extra large-scale multiple-input multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this paper, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view.

As a promising framework for distributed machine learning (ML), wireless federated learning (FL) faces the threat of eavesdropping attacks when a trained ML model is sent over a radio channel. To address this threat, we propose channel-sharing-based artificial jamming to increase the secrecy throughput of FL clients (FCs). Specifically, when an FC performs local model training, a selected device such as a sensor node (SN) not involved in the FL opportunistically accesses the FC’s channel to transmit its sensing data. In return, when the FC sends its locally trained model to the FL server (FLS), the selected SN provides artificial jamming to increase the FC’s secrecy throughput. Considering multiple FCs and SNs, we first consider a given pairing of FCs and SNs and optimize the local training time, the model uploading time, and the transmit-power of the FCs to minimize the total latency of FL training. After proving the convexity of this optimization problem, we propose an efficient algorithm to derive the semi-analytical solution. Then, we further investigate the pairing of the FCs and the SNs to minimize a system-wise cost reflecting both energy consumption and latency. The resulting problem is a bicriteria pairing problem, and we propose an efficient algorithm to compute the optimal pairing solution. Numerical results demonstrate the efficiency and performance advantage of our proposed channel-sharing-based approach with artificial jamming in comparison with different benchmark schemes.

Federated learning (FL) can be used in mobile-edge networks to train machine learning models in a distributed manner. Recently, FL has been interpreted within a model-agnostic meta-learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous data sets. However, existing research simply combines MAML and FL without explicitly addressing how much benefit MAML brings to FL and how to maximize such benefit over mobile-edge networks. In this article, we quantify the benefit from two aspects: 1) optimizing FL hyperparameters (i.e., sampled data size and the number of communication rounds) and 2) resource allocation (i.e., transmit power) in mobile-edge networks. Specifically, we formulate the MAML-based FL design as an overall learning time minimization problem, under the constraints of model accuracy and energy consumption. Facilitated by the convergence analysis of MAML-based FL, we decompose the formulated problem and then solve it using analytical solutions and the coordinate descent method. With the obtained FL hyperparameters and resource allocation, we design an MAML-based FL algorithm, called automated FL (AutoFL), that is able to conduct fast adaptation and convergence. Extensive experimental results verify that AutoFL outperforms other benchmark algorithms regarding the learning time and convergence performance.

Constrained application protocol (CoAP) for low-power low-rate data transport in Industrial Internet of Things (IIoT) networks is typically running with two modes, namely, confirmable mode and nonconfirmable mode, respectively. Confirmable mode relies on retransmission to ensure a guaranteed Quality of Service (QoS) in terms of packet loss rate at the cost of increased latency and power consumption. Whereas nonconfirmable mode consumes less power, it is known to be packet loss prone. To enrich the CoAP transport for IIoT networks, we propose a packet-level forward error correction (FEC) mechanism based on systematic coding with an adaptive code rate to provide energy efficient and reliable packet delivery. We mathematically analyze packet loss and cost of energy consumption of the proposed mechanism and compare it with the confirmable CoAP transport scheme in the Gilbert–Elliott channel model. We demonstrate that the proposed mechanism can enhance the performance of nonconfirmable CoAP to be comparable to confirmable CoAP in terms of packet loss rate, while outperforming it in energy consumption. The analytical and simulation results verify that the proposed mechanism is suitable for IIoT networks especially in high-erasure burstiness scenarios.

Satellites and civil aircrafts (CAs) with computing ability are valuable access platforms, making it possible for Internet of Things (IoT) devices to offload their computation-intensive tasks in remote areas without network infrastructures. Unlike existing works mainly focused on the static scenarios or the interaction between any two types of local, edge and cloud nodes, we propose an innovative multi-tier hybrid parallel computation architecture in CA-augmented space-air-ground integrated networks (CAA-SAGIN). Specifically, devices perform local computing, CAs and satellites act as edge servers, and ground stations of satellite networks operate cloud computing. Aiming to minimize the weighted sum of end-to-end (E2E) delay and energy consumption, we formulate a partial computation offloading problem by jointly considering access strategy, transmit power, computing resource allocation, offloading ratio and delay tolerance. The platform selection exists both within and between layers, and there are inner- and inter-coupling relationships between communication and computing resources. The issue is solved by the proposed multi-tier partial task offloading (MPTO) algorithm. The original problem is firstly decomposed into primal and master subproblems by generalized benders decomposition (GBD) method, and parallel successive convex approximation (SCA) theory is utilized to transform the multi-variable NP-hard master problem into a convex one. Simulation results demonstrate the convergence and optimality of the MPTO algorithm and the advantages of this multi-tier hybrid computation offloading system. Also, the optimal tradeoff between E2E delay and energy consumption can be achieved by the MPTO algorithm.

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first presentMobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we proposeSemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method$\texttt {SemMixed}$to harden the ESC againstSemAdvattacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., ourMobileSCcan even be more robust than classical block-wise communication systems in the low SNR regime.

AS ONE of the new communication scenarios in 5th-generation (5G) mobile communication systems, ultra-reliable and low-latency communications (URLLC) have stringent requirements on latency (around 1 ms) and reliability (up to 99.99999%). Nevertheless, existing 5G URLLC alone cannot fulfill all the Key Performance Indicators (KPIs) in emerging mission-critical applications like industrial automation, intelligent transportation, telemedicine, Tactile Internet, and Virtual/Augmented Reality (VR/AR). The 6th generation (6G) communication systems need to meet additional requirements on some of the following KPIs in combination with URLLC: high spectrum efficiency (SE)/throughput/energy efficiency (EE)/network availability/security as well as low Age of Information (AoI)/jitter/round-trip delay. These new requirements pose unprecedented challenges in terms of design methodologies and enabling technologies in 6G. To fill the gap between 5G URLLC and the diverse KPI requirements of the neXt generation URLLC (xURLLC), novel methodologies and innovative technologies are much needed.

Space information network (SIN) can provide global coverage in 6G network. Furthermore, SIN with network function virtualization (NFV) can achieve flexible deployment of network functions and improve the utilization of resources. In SIN with NFV, network functions can be virtualized into virtual network functions (VNFs). However, in SIN with NFV, the mission flow must satisfy the service function chain (SFC) constraint, i.e., the mission flow must be processed by all VNFs in the predefined order. Furthermore, each VNF can be deployed on multiple physical nodes. Moreover, different kinds of services may have the diverse quality of service (QoS) requirements. In this paper, we investigate the joint VNFs deployment and flow routing strategy (VNF-R) to maximize the number of completed missions with the guaranteed end-to-end latency under SFC constraints in time-varying SINs. Specifically, the problem can be formulated as a mixed integer linear programming (MILP) problem, which is proved to be NP-hard. In order to effectively solve the problem, we propose a novel low-complexity near-optimal penalty successive upper bound minimization rounding LP relaxation iterative rounding (PSUM-R-LRIR) algorithm. The simulation results show that the PSUM-R-LRIR algorithm can achieve near-optimal performance, and our proposed VNF-R scheme significantly outperforms the fixed VNF deployment scheme.

Space-air-ground integrated networks (SAGIN) have become a research hotspot facing the next generation of communications. The theoretical analysis for non-terrestrial networks (NTN) is significant before applying them in practical scenarios, but the existing works failed to provide a general analysis approach for NTN. Against this background, multiple satellites and civil aircrafts (CAs) are modeled as 3-D binomial point processes (BPPs) in the given finite space in this paper, and we desire to investigate the coverage performance of downlink CA augmented-SAGIN (CAA-SAGIN). Considering the sectorized beam pattern of platforms, we provide a detailed analysis of the different distributions of the serving and interfering platforms and derive the Laplace transform of the interference under shadowed-Rician fading channels. Then, the exact and closed-form expressions are obtained for the general cases with interference and the particular cases without interference via stochastic geometry. The approximations and boundary values are derived by adopting the existing mathematical theories. We analyze the effects of different parameters on the coverage probability of satellite and CA networks, and prove the validity of the derived analytical expressions, approximations, and bounds. Moreover, this work paves the way from the system level to exploit the generic coverage performance of NTN.

Federated learning is a new artificial intelligence technology with which an edge server can orchestrate with multiple end users to train a global model collaboratively. Under this setting, users only upload the locally trained parameters instead of their local data, substantially reducing communication costs and boosting data privacy. Nonetheless, federated learning mainly relies on users’ local training, overlooking the abundant computing resources owned by the edge server. To exploit the edge server’s processing power, we propose a hybrid learning paradigm that consists of centralized and federated learning components. This scheme uploads a portion of users’ data for centralized learning when the local model is trained under federated learning. We derive a theoretical upper bound for the model accuracy, which can be used to assess the performance of the proposed new learning paradigm. To balance the computation and communication resources for a good model accuracy performance, we establish a joint optimization problem of model accuracy, latency, and energy consumption. We also devise the corresponding joint optimization algorithm to solve the problem. Experiment results show that compared with centralized and federated learning, the proposed hybrid learning algorithm can effectively improve the model accuracy and significantly reduce computation and communication resources.

Reconfigurable intelligent surface (RIS) has been envisioned as an innovative technology to assist millimeter wave (mmWave) communications. Thanks to both advantages of low hardware cost and low power consumption, the hybrid transceiver structure also becomes an integral component of mmWave systems. However, due to practical limitations of hardware components, the RIS-assisted mmWave communications usually suffer unavoidable hardware impairments (HWIs). In this paper, we aim to minimize the (sum) MSE and maximize the average rate of the hardware-impaired RIS-assisted point-to-point mmWave MIMO system, respectively, by jointly optimizing the hybrid transceiver and RIS reflection coefficients under the realistic discrete phase shift constraints. We firstly consider the single-antenna user case and propose efficient alternating optimization (AO) algorithms to solve the two intractable problems. A binary-oriented exact penalty (BEP) method is developed for the involved discrete optimization, which is able to strike a good trade-off between performance and complexity. Moreover, we analyze the optimality of AO algorithms under the cascaded line-of-sight (LoS) channel condition, and reveal both the MSE floor effect and average rate saturation effect in the high-SNR regime. The above studies are then extended to the general multi-antenna user case, where a low-complexity two-phase scheme with the aim of creating the favorable RIS-cascaded channel in the first phase and enhancing system performance in the second phase is proposed. This two-phase scheme is also demonstrated to attain the optimal performance in the LoS scenario. Numerical results validate our theoretical analysis and illustrate superior performance of the proposed algorithms over various benchmark schemes.

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in this work, we consider an IRS-assisted cellular-based RF-powered IoT network, where the cellular base stations (BSs) broadcast energy signal to IoT devices for energy harvesting (EH) in the charging stage, which is utilized to support the uplink (UL) transmissions in the subsequent UL stage. With tools from stochastic geometry, we first derive the distributions of the average signal power and interference power which are then used to obtain the energy coverage probability, UL coverage probability, overall coverage probability, spatial throughput and power efficiency, respectively. With the proposed analytical framework, we finally evaluate the effect on network performance of key system parameters, such as IRS density, IRS reflecting element number, charging stage ratio, etc. Compared with the conventional RF-powered IoT network, IRS passive beamforming brings the same level of enhancement in both energy coverage and UL coverage, leading to the unchanged optimal charging stage ratio when maximizing spatial throughput.

Developing non-terrestrial networks (NTN) in future wireless networks has been widely recognized to bring advanced communication services to remote and unserved areas. The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for NTN to provide seamless and fast global connectivity. However, since natural dynamic features, the mobility management, in particular the handover (HO) between satellites, plays an important role in ensuring a stable and continuous data service for NTN. Motivated by this fact, this paper proposes a HO optimization strategy based on conditional handover (CHO) mechanism to enhance service continuity in LEO-based NTN. A reward function, related to link service time and service capability, is firstly designed to modify the monitoring conditions of target satellite candidates. The optimal target selection algorithm is proposed to obtain the maximum reward for each CHO. Then, a service continuity performance graph (SCG) model is constructed to predict different potential CHO combinations in service duration. On the basis of SCG, the HO sequence supporting a high-quality and stable data service is predictively calculated for each accessing user. Simulation results demonstrate that the proposed HO optimization scheme can obviously reduce handover rate under different NTN conditions and can better enhance NTN service continuity.

Reconfigurable intelligent surface (RIS) is able to enhance the capacity of wireless communication systems with low overhead. Extremely large (XL)-RIS-aided millimeter-wave (mmWave) communication has become a promising key technique for future 6-th Generation (6G) systems. The performance gain brought in by XL-RIS relies on the accurate channel state information (CSI). However, channel estimation requires huge training overhead and high computational complexity due to the XL number of passive elements at RIS. Moreover, the unknown visual region (VR) infomation caused by the sensitivity of mmWave signal to random blockages makes the channel estimation more difficult. In this paper, we consider the channel estmation for XL-RIS-aided mmWave uplink system. We firstly model the XL-RIS-aided channel as a hybrid one composed of near-field RIS-to-user channel and far-field RIS-to-base station (BS) channel, where the VR issue of XL-RIS has been taken into consideration. Then we formulate the channel estimation problem as a sparse recovery problem. To solve this problem, we propose a two-stage algorithm for joint channel estimation and VR detection. Finally numerical results show that the proposed algorithms outperform the existing benchmark schemes in terms of normalized mean-squared error (NMSE) due to the VR detection and the utilization of shift common-support property among sub-channels.

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among multiple graphs. However, in practice, a more intricate topological pattern, comprising simultaneously ofhomogeneousandheterogeneouscomponents, would exhibit in multiple graphs. In this paper, we propose a general graph estimator based on a novel structural fusion regularization that enables us to jointly learn multiple graphs with such complex topological patterns, and enjoys rigorous theoretical guarantees. Specifically, in the proposed regularization term, the structural similarity among graphs is characterized by a Gram matrix, which enables us to flexibly model different types of network structural similarities through different Gram matrix choices. Algorithmically, the regularization term, coupling the parameters together, makes the formulated optimization problem intractable, and thus, we develop an implementable algorithm based on the alternating direction method of multipliers (ADMM) to solve it. Theoretically, non-asymptotic statistical analysis is provided, which precisely characterizes the minimum sample size required for the consistency of the graph estimator. This analysis also provides high-probability bounds on the estimation error as a function of graph structural similarities and other key problem parameters. Finally, the superior performance of the proposed method is demonstrated through simulated and real data examples.

In China, some e-commerce platform (EP) companies such as Alibaba and JD are now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players, with EP companies being VNOs, through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage static Stackelberg game. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Besides, for the scenario lack of knowledge on the interaction between the NO and VNO in a dynamic game, we propose a deep Q-network (DQN) based algorithm to derive the optimal strategies of the NO and VNO. Simulation results show impact of system parameters on the utilities of game players and social welfare. We also study the impact of system parameters on different algorithms and discover that the proposed DQN-based algorithm can learn a good strategy as compared with the Stackelberg equilibrium solution.

In this paper, we explore the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks. A revenue maximization perspective is provided. To this end, we first examine the quantitative impact of bundle recommendation on the content request probability of different users. We then specify the definition of system revenue, showing its dependence on bundle recommendation and caching policies. With that, a joint bundling, caching and recommendation decision problem is formulated to maximize the achievable system revenue, taking into account the constraints of user-distinguished recommendation quality, recommendation amount, and the cache capacity budget. To solve this non-tractable optimization problem, a divide-then-conquer methodology is adopted. Specifically, we first determine the bundle state per user, on which basis we perform the joint bundle recommendation and caching decision-making, wherein several bundling strategies with different time-complexity are devised. Last but not least, we provide detailed properties analysis for our proposed bundling and joint optimization algorithms. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.

The boom in Internet of Things has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination and aroused widespread attention from both academia and industry. In this paper, we develop a theoretical framework to evaluate the statistics of AoI, including its average and violation probability, in wireless networks under different types of sources and updating patterns. The analyses account for the randomness that arises from both the spatial deployment and temporal queueing dynamics, and its accuracy is verified through simulations. Based on the analytical results, we design a locally adaptive power control policy to optimize the sum of average AoI of all nodes, which allows each node to assign transmit power according to its local observation. The proposed scheme has low implementation complexity. Numerical results show that the proposed power control policy can significantly improve information freshness. The scheme is well adapted to variants of network environment and heterogeneous source-destination distance. Further, we evaluate the effect of the retransmission mechanism and updating patterns on the AoI performance.

We consider a homogeneous Poisson bipolar network in which the bipoles represent source-destination pairs. The source nodes need to update their destinations about the new status perpetually, and the communications are taken place over a shared spectrum. The common goal of the source nodes is to minimize the network-wide age of information (AoI). We develop a policy by which every source node can adapt its frequency of generating status updates in a local and decentralized manner. At the same time, the network average AoI is minimized by reducing interference amongst transmitters located in geographical proximity. Following this policy, we also derive mathematical expressions to characterize the distribution of the optimal updating rate at each source node, the network average AoI, and the AoI violation probability, i.e., the probability that the AoI of a typical source node exceeds an age threshold. The analytical results are combined with discrete event simulations to provide a detailed evaluation of the performance of the proposed scheme. Particularly, it is shown that our policy is able to adaptively adjust the updating rate of each source node according to the variant of the network topology. In this manner, it is instrumental in decreasing both the network average AoI and AoI violation probability. Additionally, the scheme can maintain the AoI at a low level even when the network grows in size.

Various middleboxes are ubiquitously deployed in networks to perform packet processing functions, such as firewalling, proxy, scheduling, etc., for the flows passing through them. With the explosion of network traffic and the demand for multiple types of network resources, it has never been more challenging on a middlebox to provide Quality-of-Service (QoS) guarantees to grouped flows. Unfortunately, all currently existing fair queueing algorithms fail in supporting hierarchical scheduling, which is necessary to provide QoS guarantee to the grouped flows of multiple service classes. In this paper, we present two new multi-resource fair queueing algorithms to support hierarchical scheduling, collapsed Hierarchical Dominant Resource Fair Queueing (collapsed H-DRFQ) and dove-tailing H-DRFQ. Particularly, collapsed H-DRFQ transforms the hierarchy of grouped flows into a flat structure for flat scheduling while dove-tailing H-DRFQ iteratively performs flat scheduling to sibling nodes on the original hierarchy. Through rigorous theoretical analysis, we find that both algorithms can provide hierarchical share guarantees to individual flows, while the upper bound of packet delay in dove-tailing H-DRFQ is smaller than that of collapsed H-DRFQ. We implement the proposed algorithms on Click modular router and the experimental results verify our analytical results.

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process through two independent channels. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed; the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that when the two servers have the same service rate, compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system decrease by 33.3% and 37.5%, respectively, and those of the M-D system decrease by 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI.

Relaying is an effective method to achieve reliable and timely data collection, which is one of the most important parts of underwater acoustic sensor networks (UASNs). Considering that the relay position determines the reliability of relay communication, we study the problem of relay placement and place an autonomous underwater vehicle (AUV) to mobile relay data transmission and realize the reliable, low-latency, and low-energy data collection in UASNs. First, we formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate FRA with a three-dimensional geometry formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the FRA constraint. To this end, we propose a nearest-community (N-C) trajectory planning algorithm to design the AUV relay trajectory. A member grouping method and the necessity of position (NoP) concept are proposed to further reduce the relay positions and trajectory length of the AUV. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the N-C using NoP-based member grouping can successfully receive more packets per minute and consume fewer sensors’ energy than other algorithms.

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

Providing satisfactory quality of service (QoS) in high-speed railway (HSR) network is being strangled by external interference as well as jamming. To address this issue, we study the reconfigurable intelligent surface (RIS)-aided HSR network, where one RIS is deployed nearby the onboard mobile relay (MR) to suppress the interference as well as jamming in HSR system. Aiming at enhancing the HSR network capacity against the interference, we formulate an optimization problem for designing the phase shifts at the RIS. Since the HSR environment is time-varying and complicated, the optimization problem is challenging to settle. Inspired by the recent advances of deep reinforcement learning (DRL), we propose a deep deterministic policy gradient (DDPG)-based scheme to settle the problem through designing the action space, the state space as well as the reward function. Simulation results present that 1) deploying the RIS nearby the onboard MR is strongly facilitative of suppressing the interference; 2) the proposed DDPG scheme can achieve better capacity than the baseline schemes, and be gradually close to the upper boundary with the number of RIS elements increasing.

Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). However, most existing PFL implementations rely on synchronous training to ensure good convergence performances, which may lead to a serious straggler problem, where the training time is heavily prolonged by the slowest UE. To address this issue, we propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS2), over mobile edge networks. By jointly optimizing the wireless bandwidth allocation and UE scheduling policy, it not only mitigates the straggler problem but also provides convergent training loss guarantees. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. On this basis, the bandwidth allocation problem can be solved using analytical solutions and the UE scheduling policy can be obtained by a greedy algorithm. Experimental results verify the effectiveness of PerFedS2 in saving the training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization.

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resources at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks.

Federated learning (FL) is a privacy-preserving distributed learning paradigm that enables clients to jointly train a global model. In real-world FL implementations, client data could have label noise, and different clients could have vastly different label noise levels. Although there exist methods in centralized learning for tackling label noise, such methods do not perform well on heterogeneous label noise in FL settings, due to the typically smaller sizes of client datasets and data privacy requirements in FL. In this paper, we propose FedCorr, a general multi-stage framework to tackle heterogeneous label noise in FL, without making any assumptions on the noise models of local clients, while still maintaining client data privacy. In particular, (1) FedCorr dynamically identifies noisy clients by exploiting the dimensionalities of the model prediction subspaces independently measured on all clients, and then identifies incorrect labels on noisy clients based on persample losses. To deal with data heterogeneity and to increase training stability, we propose an adaptive local proximal regularization term that is based on estimated local noise levels. (2) We further finetune the global model on identified clean clients and correct the noisy labels for the remaining noisy clients after finetuning. (3) Finally, we apply the usual training on all clients to make full use of all local data. Experiments conducted on CIFAR-10/100 with federated synthetic label noise, and on a real-world noisy dataset, Clothing1M, demonstrate that FedCorr is robust to label noise and substantially outperforms the state-of-the-art methods at multiple noise levels.

Welcome to the 20th IEEE international conference on embedded and ubiquitous computing (EUC 2022) organized by Huazhong University of Science and Technology, 28-30 October 2022, on behalf of the organizing committee of EUC 2022. Due to the Covid-19, the conference is organized in a hybrid mode, both physically and online.

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

Federated learning (FL) framework enables user devices collaboratively train a global model based on their local datasets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Then, aiming at optimizing the model training performance, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two sub-problems. Specifically, a coalition formation algorithm is proposed for the device clustering sub-problem, and the sub-problem of bandwidth allocation and transmit power control is solved directly due to its convexity. Finally, simulation experiments are conducted on the MNIST dataset to validate the performance of the proposed algorithm in terms of test accuracy.

Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data. We show that running stochastic gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process. Based on this finding, we further analyze the convergence rate of a federated learning system by accounting for the effects of parameter staleness and communication resources. These results advance the understanding of the Federated SGD algorithm, and also forges a link between staleness analysis and federated computing systems, which can be useful for systems designers.

Hierarchical federated edge learning (H-FEEL) has been recently proposed to enhance the federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients. Each helper collects the trained gradients from users nearby, aggregates them, and sends the result to the server for model update. Due to limited communication resources, only a portion of helpers can upload their aggregated gradients to the server, thereby necessitating a well design for helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for H-FEEL which involves local gradient computing, weighted gradient uploading, and model updating phases. By characterizing these phases mathematically and analyzing the one-round convergence bound of the training algorithm, we formulate a problem to achieve the scheduling and resource allocation scheme. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: bit and sub-channel allocation problem and helper scheduling problem. For the first subproblem, we obtain a low-complexity suboptimal solution by using a four-stage method. For the second subproblem, we obtain a stationary point by using the penalty convex-concave procedure. The efficacy of our scheme is demonstrated via simulations, and the analytical framework is shown to provide valuable insights for the design of practical H-FEEL system.

Opening the unlicensed bands provides additional spectrum resources for the next generation wireless network, while severe unfairness and performance degradation occur when one coexists with the incumbent users of these bands. Therefore, plenty of efforts have been made towards fair coexistence, mainly focusing on parameter tuning of listen-before-talk (LBT) and duty-cycle (DC) mechanisms. For better utilization of the unlicensed bands, it is of paramount importance to establish an access mechanism that guarantees the fairness objective among feasible mechanisms. Such access mechanism and the corresponding benchmark, nevertheless, remain largely unknown. To address this issue, this paper considers the coexistence between WiFi and the other unlicensed nodes, and aims to maximize the α-fairness between them. A benchmark is first given by solving the optimization problem. Then we propose a deep reinforcement learning (DRL) mechanism to help the unlicensed nodes make access decisions, such that they coexist with WiFi harmoniously. Extensive simulations have been carried out, and the results show that the DRL mechanism can approach the benchmark.

Analog over-the-air computing enables a swarm of end-user devices to efficiently conduct distributed learning, where the intermediate parameters of users, such as gradients, are modulated and transmitted via a group of orthogonal waveforms, and can be mixed directly at a server without individually detecting the feedback parameters of each user. Nonetheless, the scarcity of orthogonal waveforms, as well as communication resources of the end-user devices, are throttling this paradigm in adopting complex deep learning models. To balance the tradeoff between communication efficiency and accuracy performance, we study model pruning for analog over-the-air distributed learning in this paper. First, a model pruning scheme is proposed to improve the communication efficiency of analog over-the-air training. An importance measure for model parameter pruning is also designed based on the analog over-the-air aggregated gradient, which can characterize the contribution of each parameter without removing channel fading and electromagnetic interference. Second, an analytical expression of the training error upper bound is derived, which shows the proposed scheme is able to converge even when the aggregated gradient is corrupted by heavy-tailed electromagnetic interference with an infinite variance. Finally, several experimental results are provided to show the performance gains achieved by our proposed scheme, and also verify the correctness of analytical results.

Federated Learning (FL), a promising privacy-preserving distributed learning paradigm, has been extensively applied in urban environmental prediction tasks of Mobile Edge Computing (MEC) by training a global machine learning model without data sharing. However, it is hard for the shared global model to be well generalized among local edge servers, due to the statistical data heterogeneity, especially in real-world urban environmental data. Besides, the existing FL approaches may result in excessive communication and computation overhead due to the frequent transmission and aggregation of model parameters between massive edge servers and remote cloud servers. To address the above issues, we propose HPFL-CN, a novel communication-efficient Hierarchical Personalized Federated edge Learning framework via Complex Network feature clustering, aiming to cluster edge servers with similar environmental data distributions and then high-efficiently train personalized models for each cluster via hierarchical architecture. Specifically, HPFL-CN introduces Privacy-preserving Feature Clustering (PFC) to extract privacy-preserving low-dimensional feature representations of each edge server via mapping the environmental data to different complex network domains for clustering similar edge servers accurately. According to the clustering results of PFC, HPFL-CN further introduces an edge-mediator-cloud architecture for hierarchical model aggregation by Effective Hierarchical Scheduling (EHS), in which every mediator coordinates the training of edge servers within each cluster and periodically uploads model to cloud server for global model aggregation. Meanwhile, each mediator server would find a trade-off between cloud and edge models to realize personalization within clusters. Our extensive experiments on real-world datasets demonstrate the effectiveness and generalization of HPFL-CN, which outperforms other state-of-the-art FL methods regarding personalization performance and communication efficiency.

To take advantages of the high spectral efficiency of full-duplex (FD) mode and control rate reduction caused by the self-interference introduced to the relay receiver, a novel hybrid duplex scheme is proposed where the relay works in FD mode following a duty cycle, and receives-only for the rest of time. After characterizing the achievable rate, a joint FD duty cycle and source power allocation problem is formulated to maximize the achievable rate. It is proved that the optimal source power allocation follows a water-filling algorithm over time. Moreover, the optimal FD duty cycle is obtained by considering low-, medium-, and high-source power cases. Specially, closed-form approximation of the optimal FD duty cycle for medium-source power case is presented. Besides, it is shown that the proposed hybrid duplex scheme degenerates to half-duplex and FD modes for low-and high-source power cases, respectively. Numerical results demonstrate that the proposed scheme can effectively improve the achievable rate for a wide range of parameters.

The boom in Internet of Things (IoT) has spawned many real-time applications, which have stringent requirements for the timeliness of information delivery. As a result, age of information (AoI) has emerged as a metric to evaluate information freshness at the destination node and aroused widespread attention from both academia and industry. In this paper, we develop a locally adaptive power control policy for wireless ad hoc networks, which adjusts each node’s transmit power according to its local observation so as to optimize the sum of average AoI of all destination nodes. The proposed scheme has a low implementation complexity. Numerical results show that the proposed scheme is well adapted to variants of network environment and can significantly improve information freshness.

Blockchain, as a constantly evolving Peer-to-Peer (P2P) distributed ledger technology with characteristics, such as decentralization, security, interoperation, and trust establishment, can potentially lower the costs of the underpinning infrastructure and maintenance compared with conventional centralized systems. Consequently, the distributed structure of blockchain is naturally suitable for the Internet of Things (IoT), which can be used to build secure and trusted IoT. Despite the advances made in applying blockchain to IoT in the past few years, some research challenges remain to be addressed, including the poor scalability, heterogeneous IoT devices, and the impact of integration on network performance.

Deep learning-enhanced Internet of Things (IoT) plays a pivot role in advancing the transformation toward smart manufacturing, and an essential component in many smart manufacturing IoT systems is the quality inspection. However, challenges, such as expensive data labeling, innumerable types of defects, and high costs for iterative optimization, hinder the industrial applicability of previous visual surface quality inspection methods. In this article, we present an edge-computing-enabled IoT system based on an innovative hybrid learning method for visual surface quality inspection using only few labeled data and minimum iterative optimization efforts. Our hybrid learning method first employs a deep neural network to synthesize global representations of real-world industrial images, which are subsequently analyzed via an unsupervised clustering algorithm for anomaly detection. Besides, enhancement strategies, such as fine-tuning and data augmentation, are proposed to improve the robustness against the noisy data set and support low-cost inference in multiple edge devices for manufacturing operation. On a holdout data set collected from real-world factories, our method achieves classification accuracies between 90% and 98%, outperforming the benchmark method by 7%–12%. Moreover, this hybrid learning method demonstrates the effectiveness in detecting new types of surface defects and achieves test recalls between 86% and 97%, outperforming the benchmark method by 11%–34%.

In this article, we consider a practical scenario for secure wireless-powered communication in the presence of imperfect channel state information (CSI) with simultaneous energy harvesting, in which it is required to keep information secret from an untrusted energy receiver allowed only to harvest energy from the transmitted signals. We aim to find the robust transmit power control (TPC) strategy to maximize the secrecy rate whilst ensuring the spectral efficiency of transceiver pairs and the amount of energy harvested by the energy receiver, even when the CSI is inaccurate. To deal with the nonconvexity of the formulated optimization problem, we first derive a suboptimal form of TPC in an iterative manner by adopting dual methods. In order to overcome the drawbacks of the conventional optimization-based approach regarding the suboptimality of performance and requiring long computation time, we devise a deep learning (DL)-assisted TPC as an alternative means of deriving the TPC. In the considered DL-assisted TPC, a deep neural network (DNN) is trained to compensate for the distortion caused by channel errors in an unsupervised manner. More specifically, artificially distorted CSI, which reflects the difference between actual and estimated CSI, is fed into the DNN during training and used to update the weights and biases of the proposed DNN using a bounded loss function, which allows a robust TPC strategy to be approximated by the DNN. Simulation results reveal the robustness of the proposed DL-assisted TPC against channel errors, such that it achieves a near-optimal performance with a lower computation time, even when the CSI is incorrect.

Future Internet of Things (IoT) communication trends toward heterogeneous services and diverse quality-of-service requirements pose fundamental challenges for network management strategies. In particular, multiobjective optimization (MOO) is necessary in resolving the competition among different nodes sharing limited wireless network resources. A unified coordination mechanism is essential such that individual nodes conduct the opportunistic maximization of heterogeneous local objectives for efficient distributed resource allocation. To such a problem, this article proposes an artificial intelligence (AI)-based framework, which is termed as MOO strategy for AI-aided IoT communications (MOSAIC). This framework enables to tackle numerous MOO tasks in IoT network management with simple reconfiguration of learning rules. In this strategy, a component unit associated with an individual network node includes a pair of deep neural networks (DNNs) to learn optimal local functions responsible for calculation and distributed coordination, respectively. The resultant AI module swarm called DNN tiles realizes the node cooperation that collectively seeks distributed MOO calculation rules. The advantage of MOSAIC is characterized by Pareto tradeoffs among conflicting performance metrics in diverse wireless networking configurations subject to severe interference and distinct criteria for multiple targets.

The dilemma between the ever-increasing demands for data processing, and the limited capabilities of mobile devices in a wireless communication system calls for the appearance of federated learning (FL). As a distributed machine learning (ML) method, FL executes in an iterative manner by distributing the global model parameters and aggregating the local model parameters, which avoids the transmission of huge raw data and preserves data privacy during the training process. However, since FL cannot control the local training and transmission process, this gives malicious users the opportunity to deteriorate the global aggregation. We adopt a reputation model based on beta distribution function to measure the credibility of local users, and propose a reputation-based scheduling policy with user fairness constraint. By taking into account the impact of wireless channel conditions and malicious attack features, we derive tractable expressions for the convergence rate of FL in a wireless setting. Moreover, we validate the superiority of the proposed reputation-based scheduling policy via numerical analysis and empirical simulations. The results show that the proposed secure wireless FL framework can not only distinguish malicious users from normal users but also effectively defend against several typical attack types featured in attack intensity and attack frequency. The analysis also reveals that the effect of average attack intensity on the convergence performance of FL is dominated by the percentage of malicious user equipments (UEs), and imposes even greater negative effect on the convergence performance of FL as the percentage of malicious UEs increases.

Timeliness is an emerging requirement for many Internet of Things (IoT) applications. In IoT networks with a large number of nodes, severe interference may incur that leads to Age-of-Information (AoI) degradation. It is, therefore, important to study how to optimize the AoI performance. This article focuses on the AoI minimization in random-access Poisson networks. By considering the spatiotemporal interactions amongst the transmitters, an expression of the peak AoI is derived, based on which the optimal peak AoI and the corresponding optimal packet arrival rate and channel access probability are further characterized. The analysis shows that when the channel access probability (resp., the packet arrival rate) is given, the optimal packet arrival rate (resp., the optimal channel access probability) is equal to one when nodes are sparsely deployed, and decreases as the node deployment density increases. With a joint tuning of these two system parameters, the optimal channel access probability always equals one. Moreover, with the sole tuning of the channel access probability, the optimal peak AoI is improved with a smaller packet arrival rate only when the node deployment density is high. In contrast, a higher channel access probability always improves peak AoI performance when the packet arrival rate is solely tuned. The analysis in this article sheds important light on freshness-aware design for large-scale networks.

Age of Information (AoI), a notion that measures the information freshness, is an essential performance measure for time-critical applications in Internet of Things (IoT). With the surge of computing resources at the IoT devices, it is possible to preprocess the information packets that contain the status update before sending them to the destination so as to alleviate the transmission burden. However, the additional time and energy expenditure induced by computing also make the optimal updating a nontrivial problem. In this article, we consider a time-critical IoT system, where the IoT device is capable of preprocessing the status update before the transmission. Particularly, we aim to jointly design the preprocessing and transmission so that the weighted sum of the average AoI of the destination and the energy consumption of the IoT device is minimized. Due to the heterogeneity in transmission and computation capacities, the durations of distinct actions of the IoT device are nonuniform. Therefore, we formulate the status updating problem as an infinite horizon average cost semi-Markov decision process (SMDP) and then transform it into a discrete-time Markov decision process. We demonstrate that the optimal policy is of threshold type with respect to the AoI. Equipped with this, a structure-aware relative policy iteration algorithm is proposed to obtain the optimal policy of the SMDP. Our analysis shows that preprocessing is more beneficial in regimes of high AoIs, given it can reduce the time required for updates. We further prove the switching structure of the optimal policy in a special scenario, where the status updates are transmitted over a reliable channel and derive the optimal threshold. Finally, simulation results demonstrate the efficacy of preprocessing and show that the proposed policy outperforms two baseline policies.

Statistical priority-based multiple access (SPMA) protocol has attracted much attention in virtue of its support for multi-priority traffic, and the guarantee of low-latency and high-reliability transmissions for high-priority. In this work, we propose an analytical framework to study the performance of SPMA from spatial perspective with tools from the stochastic geometry. We consider two kinds of priority traffic, including high-priority traffic and low-priority traffic. In SPMA, a packet is split into multiple bursts to reduce the collision probability, and the turbo coding, frequency hopping, and time hopping are employed to further decrease the packet loss rate. We first derive the analytical expressions for the medium access probability (MAP) and burst success probability of two priority users in closed form, taking into account the potential transmitters (PTs) density, ratio of different traffic users, amount of orthogonal resources, channel occupancy statistics (COS) threshold, and statistical sliding window (SSW). Based on the derived MAP and burst success probability, we further obtain the packet success probability and spatial throughput. After evaluating the effect of key parameters on the above performance metrics, we provide guidelines on optimal design of several key system parameters, such as the COS threshold and PTs density, to guarantee the high-priority user a 99% packet success probability.

As one of the key communication scenarios, ultrareliable low-latency communication (uRLLC) has become an important pillar to promote the vigorous development of intelligent mobile communications. In the practical scenarios, uRLLC services have strict and diverse Quality-of-Service (QoS) requirements. However, the existing networks are difficult to meet the various delay and reliability requirements of uRLLC services. Moreover, the improvement of performance should not ignore the shortage of resources. A flexible and on-demand network solution is quite necessary, which could provide customized services according to the specific requirements and maximize the utilization efficiency of network resources. In this article, we propose an intelligent and flexible network solution (IFNS) based on the stochastic network calculus (SNC) model. Three key technologies are considered in the IFNS, that are flexible transmission time interval scheduling, flexible packet duplication transmission, and rate-adaptive reliable transmission. While providing customized services for users with various requirements, it realizes the balance between system energy efficiency and spectral efficiency and improves the resource utilization efficiency of the network. Based on the basic domain knowledge and the past experience, we propose the knowledge-assistance meta actor–critic (K-MAC) algorithm to solve the complex optimization problem caused by SNC modeling. Finally, simulation results show that the performance of the IFNS is improved 23.15%, the K-MAC algorithm has good convergence performance and reduces the complexity up to 89.4% compared with common learning algorithms.

We exploit a behavior-shaping proactive mechanism, namely, recommendation, in cache-assisted non-orthogonal multiple access (NOMA) networks, aiming at minimizing the average system’s latency. Thereof, the considered latency consists of two parts, i.e., the backhaul link transmission delay and the content delivery latency. Towards this end, we first examine the expression of system latency, demonstrating how it is critically determined by content cache placement, personalized recommendation, and delivery associated NOMA user pairing and power control strategies. Thereafter, we formulate the minimization problem mathematically taking into account the cache capacity budget, the recommendation-oriented requirements, and the total transmit power constraint, which is a non-convex, multi-timescale, and mixed-integer programming problem. To facilitate the process, we put forth an entirely new paradigm nameddivide-and-rule. Specifically, we first solve the short-term optimization problem regarding user pairing as well as power allocation and the long-term decision-making problem with respect to recommendation and caching, respectively. On this basis, an iterative algorithm is developed to optimize all the optimization variables alternately. Particularly, for solving the short-timescale problem, graph theory enabled NOMA user grouping and efficient inter-group power control manners are invoked. Meanwhile, a dynamic programming approach and a complexity-controllable swap-then-compare method with convergence insurance are designed to derive the caching and recommendation policies, respectively. From Monte-Carlo simulation, we show the superiority of the proposed joint optimization method in terms of both system latency and cache hit ratio when compared to extensive benchmark strategies.

Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

We consider a resource allocation and offloading decision-making problem in a mobile edge computing (MEC) network. Since the locations of user equipments (UEs) vary over time in practice, we consider a dynamic network, where the UEs could leave or join the network coverage at any location. Since the joint offloading decision that minimizes the network cost also varies with the topology, the expected best offloading decision for the previous topology would not match the new topology. Consequently, the system suffers from recurring cost peaks due to the topology change. Thus, we propose a robust distributed hierarchical online learning approach to enhance the algorithm’s robustness and reduce the cost peaks. Specifically, the UEs learn the utility of each offloading decision via deep Q networks (DQNs) from their interaction with the MEC network. Meanwhile, the computational access points (CAPs) train their deep neural networks (DNNs) online with the real-time data collected from the UEs to predict their corresponding Q-value vectors. Therefore, the UEs and CAPs form a hierarchical collaborative-learning structure. When the topology changes, each UE downloads its Q-value vector as the Q-bias vector and learns its difference from the actual Q-value vector via its DQN. With different agents learning distributedly, both the peak and sum costs are reduced as the joint offloading decision could start from a near-local-optimal point. In simulations, our robust approach successfully reduces the peak cost and sum cost by up to 50% and 30%, respectively. This demonstrates the need for a robust learning algorithm design in a practical dynamic MEC network.

With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrading, as well as into economic innovation and development.This will subsequently promote a global industrial integration.Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive-or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultrahigh reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity.Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity.With this background, several important tendencies have emerged in the development of 6G wireless communications

Full-duplex (FD) mode has great potential in improving the spectral efficiency. Mitigating the effect of self-interference becomes one key for performance enhancement of FD system. This work proposes a novel hybrid duplex scheme where the relay receives information for a fraction of time and simultaneously transmits and receives information for the rest, following a duty cycle. First, we formulate the achievable rate maximization of the proposed scheme as a joint FD duty cycle and source power allocation optimization problem. The optimal FD duty cycle, the optimal source power allocation, and the maximal achievable rate are explicitly given for some cases and characterized in detail for other cases. Then, the proposed scheme is applied to two-hop relaying systems. Specifically, the optimal source power allocation is proved to be a water-filling solution over the FD phase and the receives-only phase on the source-relay link. By dividing the system as low-, medium-, and high-source power cases, the optimal FD duty cycle and the maximal achievable rate are obtained in (approximate) closed-form case-by-case, where the source power thresholds among cases are clearly expressed. Numerical results validate that the proposed hybrid duplex scheme outperforms other benchmark schemes and can improve the achievable rate significantly.

Despite the great promises that the resistive random access memory (ReRAM) has shown as the next generation of non-volatile memory technology, its crossbar array structure leads to a severe sneak path interference to the signal read back from the memory cell. In this paper, we first propose a novel belief propagation (BP) based detector for the sneak path interference in ReRAM. Based on the conditions for a sneak path to occur and the dependence of the states of the memory cells that are involved in the sneak path, a Tanner graph for the ReRAM channel is constructed, inside which specific messages are updated iteratively to get a better estimation of the sneak path affected cells. We further combine the graph of the designed BP detector with that of the BP decoder of the polar codes to form a joint detector and decoder. Tailored for the joint detector and decoder over the ReRAM channel, effective polar codes are constructed using the genetic algorithm. Simulation results show that the BP detector can effectively detect the cells affected by the sneak path, and the proposed polar codes and the joint detector and decoder can significantly improve the error rate performance of ReRAM.

Federated learning (FL) has been considered as a promising paradigm for enabling distributed training/learning in many machine-learning services without revealing users’ local data. Driven by the growing interests in exploiting FL in wireless networks, this paper studies the Non-orthogonal Multiple Access (NOMA) assisted FL in which a group of end-devices (EDs) form a NOMA cluster to send their locally trained models to the cellular base station (BS) for model aggregation. In particular, we consider that the BS adopts wireless power transfer (WPT) to power the EDs (for their data transmission and local training) in each round of FL iteration, and formulate a joint optimization of the BS’s WPT for different EDs, the EDs’ NOMA-transmission for sending the local models to the BS, the BS’s broadcasting of the aggregated model to all EDs, the processing-rates of the BS and EDs, as well as the training-accuracy of the FL, with the objective of minimizing the system-wise cost accounting for the total energy consumption as well as the FL convergence latency. In spite of the strict non-convexity of the joint optimization problem, we analytically characterize the BS’s and all EDs’ optimal processing-rates, based on which we propose a layered algorithm for finding the optimal solutions for the joint optimization problem via exploiting monotonic optimization. Numerical results validate that our algorithm can achieve the optimal solution as LINGO’s global-solver (i.e., a commercial optimization package) while significantly reducing the computation-time. Moreover, the results also demonstrate that our NOMA assisted FL can reduce the system cost compared to the benchmark FL scheme with the fixed local training-accuracy by more than 70% and the conventional frequency division multiple access (FDMA) based FL by 78%.

The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: Complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this article, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network . We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices and implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.

Over the years, quantifying the similarity of nodes has been a hot topic in network science, yet little has been known about the distribution of node-similarity. In this paper, we consider a typical measure of node-similarity called the common neighbor based similarity (CNS). By means of the generating function, we propose a general framework for calculating the CNS distributions of node sets in various networks. Particularly, we show that for the Erdös-Rényi random network, the CNS distribution of node sets of any size obeys the Poisson law. Furthermore, we connect the node-similarity distribution to the link prediction problem, and derive analytical solutions for two key evaluation metrics: i) precision and ii) area under the receiver operating characteristic curve (AUC). We also use the similarity distributions to optimize link prediction by i) deriving the expected prediction accuracy of similarity scores and ii) providing the optimal prediction priority of unconnected node pairs. Simulation results confirm our theoretical findings and also validate the proposed tools in evaluating and optimizing link prediction.

Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users’ quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user’s viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

Implementing federated learning (FL) algorithms in wireless networks has garnered a wide range of attention. However, few works have considered the impact of user mobility on the learning performance. To fill this research gap, we develop a theoretical model to characterize the hierarchical federated learning (HFL) algorithm in wireless networks where the mobile users may roam across edge access points (APs), leading to incompletion of inconsistent FL training. We provide the convergence analysis of conventional HFL with user mobility. Our analysis proves that the learning performance of conventional HFL deteriorates drastically with highly-mobile users. And such a decline in the learning performance will be exacerbated with small number of participants and large data distribution divergences among users’ local data. To circumvent these issues, we propose a mobility-aware cluster federated learning (MACFL) algorithm by redesigning the access mechanism, local update rule, and model aggregation scheme. We also conduct experiments to evaluate the learning performance of conventional HFL, a cluster federated learning (CFL) with simple averaging, and our proposed MACFL. The results show that our MACFL can enhance the learning performance, especially for three different cases: ($i$) the case of users with non-independent and identically distributed (non-IID) data, ($ii$) the case of users with high mobility, and ($iii$) the case with a small number of users.

Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-reliable and low-latency communications. The bottlenecks of applying PLS in practice include 1) lack of accurate channel state information (CSI) of both the intended user and the eavesdropper; 2) high computational complexity for solving optimization problems. To address the first issue, we compare the secure transmission rates of short packets in different scenarios (i.e., with/without eavesdropper’s instantaneous CSI and with/without channel estimation errors) and derive the closed-form optimal power control policy in a special case. To find numerical solutions in general cases, we apply an unsupervised deep learning method, which has low complexity after the training stage. Through numerical results, we obtain the following three key findings: 1) The learning-based power control policy approaches the closed-form optimal policy in the special case and outperforms two existing power control policies in general cases. 2) Knowing the instantaneous CSI of the eavesdropper only provides a marginal gain of the secure data rate in the high signal-to-noise ratio regime. 3) In the presence of channel estimation errors, the learning-based policy trained by the estimated channels can guarantee the average transmit power constraint, while the closed-form policy cannot.

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.

The conventional federated learning (FL) framework usually assumes synchronous reception and fusion of all the local models at the central aggregator and synchronous updating and training of the global model at all the agents as well. However, in a wireless network, due to limited radio resource, inevitable transmission failures and heterogeneous computing capacity, it is very hard to realize strict synchronization among all the involved user equipments (UEs). In this paper, we propose a novel asynchronous FL framework, which well adapts to the heterogeneity of users, communication environments and learning tasks, by considering both the possible delays in training and uploading the local models and the resultant staleness among the received models that has heavy impact on the global model fusion. A novel centralized fusion algorithm is designed to determine the fusion weight during the global update, which aims to make full use of the fresh information contained in the uploaded local models while avoiding the biased convergence by enforcing the impact of each UE’s local dataset to be proportional to its sample share. Numerical experiments validate that the proposed asynchronous FL framework can achieve fast and smooth convergence and enhance the training efficiency significantly.

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement of federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients, in which each helper collects the trained gradients from clients nearby, aggregates them, and sends the result to the server for global model update. Due to limited communication resources, only a portion of helpers can be scheduled to upload their aggregated gradients in each round of the model training. And that necessitates a well-designed scheme for the joint helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for the H-FEEL system which involves local gradient computing, weighted gradient uploading, and machine learning model updating phases. By characterizing these phases mathematically and analyzing one-round convergence bound of the training algorithm, we formulate an optimization problem to achieve the scheduling and resource allocation scheme. The problem simultaneously captures the uncertainty of the wireless channel and the importance of the weighted gradient. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems:bit and sub-channel allocationandhelper scheduling, which are mixed integer nonlinear programming and continuous nonlinear problems, respectively. For the first subproblem, we obtain an optimal solution of exponential complexity and a suboptimal solution that has polynomial complexity. For the second subproblem, we obtain a closed-form optimal solution in a special case and a suboptimal solution in the general case. The efficacy of our scheme is amply demonstrated via simulations and the analytical framework is shown to provide valuable design insights for the practical implementation of the H-FEEL system.

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

This paper investigates the problem of providing ultra-reliable and power-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users’ feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users’ head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users’ accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users’ tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solve a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% power-efficient than the benchmark algorithms.

Towards the sixth generation (6G) wireless communications, multiple access is a potential technology to achieve thousand times of traffic capacity enhancement comparing with 5G. This paper investigates the feasible signal-to-interference-plus-noise-ratio (SINR) region for the multi-cell downlink and uplink non-orthogonal multiple access (NOMA) systems. Within a cell, the signals of different users are multiplexed on a channel with different power levels and successive interference cancellation is applied at the receivers to decode the signals. For the downlink, based on Perron-Frobenius Theory, we first derive a necessary and sufficient condition for an SINR vector to be feasible under a fixed decoding order. Then, a closed-form expression for the feasible SINR region is given by the union of the SINR regions under all possible decoding orders. Following a similar idea, the expression of the feasible SINR region for the uplink is also derived. Furthermore, the duality between the multi-cell downlink and uplink NOMA systems is explored. It is proved that the two systems have same feasible SINR region under dual channels. Finally, we propose an efficient algorithm to approximate the SINR region boundary. Simulation results are provided to validate the efficiency of the algorithm and compare the performance with orthogonal multiple access scheme.

Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated learning. To solve this problem, federated learning with non-IID data in wireless networks is studied in this paper. Firstly, based on the derived upper bound of expected weight divergence, a federated averaging scheme is proposed to reduce the distribution divergence of non-IID data. Secondly, to further harmonize the distribution divergence, data sharing is associated with federated learning in wireless networks, and a joint optimization algorithm is designed to keep a sophisticated balance between the model accuracy and the cost. Finally, the simulation results based on a common-used image data set are provided to evaluate the performance of our proposed schemes, which can achieve significant performance gains with a small price of latency and energy consumption.

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.

Terahertz (THz) communication is capable of providing ultra-wide bandwidth and high data rates. Therefore attracts widespread attention to its applications in next-generation networks. Highly directional antennas are used to compensate for the THz propagation loss, which also incurs beam management challenges. Specifically, caused by node mobility and blockage, frequent beam reselections and beam misalignment greatly degrade THz network performance in terms of reliability and spatial throughput. In this paper, using stochastic geometry, we fill the current research gap in system-level theoretical models for the analysis of beam misalignment and network spatial throughput by considering the effects of beamwidth, mobility, blockage, and molecular absorption. Our analyses show that an increase in nodes density or user mobility often results in severe beam misalignment, which in turn requires more signaling overhead and degrades THz network reliability and throughput. Although using wider beams reduces this impact, it increases THz network sensitivity to molecular absorption. To maximize spatial throughput, optimal beamwidth needs to be adjusted according to communication demand priority and network status. Our work provides useful insights into beamwidth adaptation according to parameters trade-off that helps THz network achieve higher reliability and throughput in different applications.

Recently, the implementation of federated learning (FL) in wireless networks becomes a hotspot due to its flexible collaborative learning methods and privacy-preserving benefits. However, most of the existing works overlook the impact of user mobility on the learning performance, which is critical. Specifically, the mobile users may roam among multiple edge access points (APs) during the local training procedures, leading to incompletion of inconsistent FL training. In this paper, we theoretically study the impact of user mobility on the FL in hierarchical wireless networks. In our system model, the network consists of one cloud server, several edge APs, and multiple mobile users that have their positions vary over time. During the local training process, users may stay in or move out of the coverage area of the originally attached edge AP. In such a practical context, we analyze the convergence rate of the FL algorithm and provide experiments to evaluate the learning performance under different network parameters. Our results provide insights in further improvements of FL in hierarchical wireless networks.

In this paper, we investigate the problem of edge caching (EC) optimization in a multi-user privacy-preserving mobile edge computing (MEC) system. The time-varying content popularity is considered and the primary objective is to maximize the EC hit rate on each caching entity in the distributed network. To this end, we introduce the concept of local and global popularities and cast the time-varying local popularities as model-free Markov chains. Next, an unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the popularities while achieving privacy-preserving goal. The underlying distributed optimization problem is then reformulated as a distributed Markov decision process and solved by the privacy-preserving distributed deep deterministic policy gradient algorithm incorporating the URFL algorithm. Simulation results demonstrate the superiority of the proposed scheme in terms of prediction error and hit rate over the baseline methods.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. While on-shore base stations (BSs) constitute a key infrastructure for maritime communications, the trap of low energy efficiency caused by long transmission distances must be delicately circumvented. In this paper, we try to utilize internet of vessels (IoV) to tackle the problem. Specifically, we investigate the joint link scheduling and rate adaptation problem for a maritime communication network with both shore-to-vessel and vessel-to-vessel links, with the target of minimizing the energy consumption while assuring a quality of service (QoS) guarantee for each vessel. With only large-scale channel state information available, the problem is shown to be an NP-hard mixed integer non-linear programming problem with a group of hidden nonlinear equality constraints. A process-oriented iterative scheme is proposed based on a relaxation and gradually-approaching method following the gentlest-ascent principle, as well as the divide-and-conquer strategy. Simulation results demonstrate that the proposed scheme can achieve a prominent gain in terms of network energy consumption reduction with a rather low complexity.

For classification tasks, deep neural networks are prone to overfitting in the presence of label noise. Although existing methods are able to alleviate this problem at low noise levels, they encounter significant performance reduction at high noise levels, or even at medium noise levels when the label noise is asymmetric. To train classifiers that are universally robust to all noise levels, and that are not sensitive to any variation in the noise model, we propose a distillation-based framework that incorporates a new subcategory of Positive-Unlabeled learning. In particular, we shall assume that a small subset of any given noisy dataset is known to have correct labels, which we treat as “positive”, while the remaining noisy subset is treated as “unlabeled”. Our framework consists of the following two components: (1) We shall generate, via iterative updates, an augmented clean subset with additional reliable “positive” samples filtered from “unlabeled” samples; (2) We shall train a teacher model on this larger augmented clean set. With the guidance of the teacher model, we then train a student model on the whole dataset. Experiments were conducted on the CIFAR-10 dataset with synthetic label noise at multiple noise levels for both symmetric and asymmetric noise. The results show that our framework generally outperforms at medium to high noise levels. We also evaluated our framework on Clothing1M, a real-world noisy dataset, and we achieved 2.94% improvement in accuracy over existing state-of-the-art methods.

In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might be excess demand at some locations and underutilized resources at other locations. To address such load-unbalanced problem in MEC systems, in this paper we propose virtual machines (VMs) sharing across base stations. Specifically, we consider the joint VM placement and pricing problem across base stations to match demand and supply and maximize revenue at the network level. To make this problem tractable, we decompose it into master and slave problems. For the placement master problem, we propose a Markov approximation algorithm MAP on the design of a continuous time Markov chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing auction, where all users are truthful. Furthermore, given users' potential untruthful behaviors, we propose an incentive compatible auction iCAT along with a partitioning mechanism PUFF, for which we prove incentive compatibility and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the original problem, and analyze the optimality gap. Simulation results show that collaborative base stations increases revenue by up to 50%.

In China, some e-commerce platform (EP) companies such as Alibaba and JD have been now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players after EP companies being VNOs through appropriate integration of the VNO business and the companies’ own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage Stackelberg game. In Stage I, the NO decides the price of mobile data for the VNO; in Stage II, the VNO decides its data plan fee for MUs and the ad price for e-commerce merchants (EMs); in Stage III, the MUs make their own decisions on the data plan subscription and the number of ads to be watched, while the EMs decide the number of ad slots they buy from the EP. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Simulation results show the impact of the system parameters on the utilities of game players and social welfare, and reveal that the solution can indeed lead to a quadri-win outcome in some cases. At the same time, we summarize some insights that have economic guidance.

Efficient data collection has been treated as a key challenge especially in the sparsely deployed Internet of Things (IoT) networks. Compared with the conventional data collection methods using static sinks, mobile data collectors (MDCs) are considered as a more efficient approach where MDCs transfer data from sensors to access points (APs) by roaming over different geographical regions. In this work, we propose an analytical framework to study the coverage performance of IoT with MDCs where MDCs follow a simple random waypoint (SRWP) mobility model. To characterize the interference distribution of the whole network, we first derive exact expressions for the average contact time (CT) and inter-contact time (ICT) between a typical sensor and its associated MDC. Then we determine the active probability of the typical sensor by using the derived CT and ICT. The coverage probability is finally derived by taking into account the communication range of sensors, velocity of MDCs, density of sensors and MDCs, and the SINR threshold. Our results reveal the fact that the velocity of MDCs has little effect on coverage probability while a higher velocity can significantly lower the end-to-end delay.

This paper envisions cyber-farm systems along the lines of cyber-physical systems. It is imperative for corporates and nations to maintain health of the crops to ensure food security. In order to avoid any adversarial attack on agriculture sensors in farms, we propose a collaborative sensing based authentication protocol. It assures that the spoofing and tampering can be detected with high probability. The data collected from the experimental deployment of sensor nodes supports the solution proposed by the paper.

Future wireless networks are envisioned to serve massive Internet of Things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, the theoretical analysis of the RACH procedure for massive IoT devices is challenging. To address this challenge, we first correlate the RACH request of an IoT device with the status of its maintained queue and analyze the evolution of the queue status by the probability theory. Based on the analysis result, we then derive the closed-form expression of the random access (RA) success probability, which is a significant indicator characterizing the RACH procedure of the device by the stochastic geometry theory. Besides, considering the agreement on converging different services onto a shared infrastructure, we investigate the radio access network (RAN) slicing for mIoT and bursty ultrareliable and low-latency communication (URLLC) service multiplexing. Specifically, we formulate the RAN slicing problem as an optimization one to maximize the total RA success probabilities of all IoT devices and provide URLLC services for URLLC devices in an energy-efficient way. A slice resource optimization (SRO) algorithm, exploiting relaxation and approximation with provable tightness and error bound, is then proposed to mitigate the optimization problem. Simulation results demonstrate that the proposed SRO algorithm can effectively implement the service multiplexing of mIoT and bursty URLLC traffic.

A massive number of Internet-of-Things (IoT) and machine-to-machine (M2M) communication devices generate various types of data traffic in cellular IoT networks: periodic or nonperiodic, bursty or sporadic, etc. In particular, bursty and nonperiodic traffic may cause an unexpected network congestion and temporary lack of radio resources. In order to effectively accommodate such bursty and nonperiodic traffic, we propose a novel recursive access class barring (R-ACB) technique to optimally utilize the available resources associated with the random access procedure (RAP) that consists of multiple steps in cellular IoT networks, while existing ACB schemes only considered the resource of the first step of RAP, i.e., the number of available preambles. The proposed R-ACB technique consists of two main parts: 1) online estimation of the number of active IoT/M2M devices who have data to transmit to an eNodeB and 2) adjustment of the ACB factor that indicates the probability that an active device sends a preamble to eNodeB. It is notable that the estimation and the adjustment recursively affect each other when R-ACB operates. In addition, we also propose mathematical models to analyze the performance of R-ACB in terms of total service time, average access delay, resource efficiency, and energy efficiency (EE). Through extensive computer simulations, we show that the proposed R-ACB technique outperforms the conventional ACB schemes.

Grant-free multiple access (GFMA) is an emerging technology to accommodate a massive number of devices for 6G-enabled Internet of Things (IoT) networks. The main advantages of GFMA are to efficiently reduce control signaling overhead for resource scheduling while improving resource efficiency. In this article, we propose anovelresource-hopping-based GFMA (RH-GFMA) framework with resource hopping schemes for providing massive connectivity in 6G cellular IoT networks, where each IoT device is allowed to access physical radio resources by using a preassigned resource hopping pattern without not only resource request but also grant procedure, which is the so-called “one-shot” noninteractive multiple access. We exploit three types of resource hopping schemes in the proposed RH-GFMA framework: 1) random hopping; 2) resource group hopping; and 3) Latin-square group hopping. We mathematically analyze the RH-GFMA system performance in terms of the hopping pattern collision probability, maximum allowable packet delay, and interference-over-thermal. Finally, we derive an accommodation capacity of the proposed RH-GFMA framework, which is defined as the expected number of IoT devices accommodated in a cell under a maximum allowable packet-delay requirement and an interference-over-thermal constraint. With the proposed GFMA, massive IoT devices are expected to be efficiently accommodated in 6G wireless networks, while satisfying strict latency and reliability requirements.

In this article, we investigate the analog–digital hybrid transceiver optimization for distributed Internet-of-Things (IoT) sensing networks consisting of a multiantenna fusion center (FC) and several multiantenna sensor nodes. Analog–digital hybrid transceiver is an economic way to realize tradeoffs between hardware cost and performance for multiantenna communications. Under the nonconvex unit modulus constraints and transmit power constraint at each sensor, two synchronization schemes are considered for the hybrid linear minimum mean-square error (LMMSE) transceiver optimization. First, a centralized algorithm is proposed, in which the hybrid transceivers are computed at the FC. Based on the framework of alternating direction method of multipliers (ADMMs), the unit modulus constraints can be satisfied by projecting the elements of analog transceivers onto the unit modulus circle. However, the centralized algorithm usually suffers from strict synchronous requirements and high communication overhead. In order to accommodate the inevitable computing and communication delays in distributed IoT sensing networks, an asynchronous distributed ADMM (AD-ADMM) algorithm is proposed. By using the aged information, the hybrid transceivers are computed at the sensors without the coordination of the FC. Thus, the AD-ADMM algorithm can greatly reduce the computation overhead of the FC and improve the scalability of IoT sensing networks. Simulation results are presented to show that both the centralized ADMM and AD-ADMM algorithms perform closely to the fully digital counterpart.

In this article, we develop a framework for the analysis of massive multiple-input-multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput, which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from the stochastic geometry and queuing theory, which captures all the key features of the devices in the Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices' delay constraints. It is found that when the base station (BS) has excessive number of antennas ( M), the BS should schedule all devices under its coverage, regardless of devices' variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.

In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lacks wide-area coverage capability. Due to these facts, the state-of-the-art MCN falls far behind terrestrial fifth-generation (5G) networks. To fill up the gap in the coming sixth-generation (6G) era, we explore the benefit of deployable BSs for maritime coverage enhancement. Both unmanned aerial vehicles (UAVs) and mobile vessels are used to configure deployable BSs. This leads to a hierarchical satellite-UAV-terrestrial network on the ocean. We address the joint link scheduling and rate adaptation problem for this hybrid network, to minimize the total energy consumption with quality of service (QoS) guarantees. Different from previous studies, we use only the large-scale channel state information (CSI), which is location-dependent and thus can be predicted through the position information of each UAV/vessel based on its specific trajectory/shipping lane. The problem is shown to be an NP-hard mixed integer nonlinear programming problem with a group of hidden non-linear equality constraints. We solve it suboptimally by using Min-Max transformation and iterative problem relaxation, leading to a process-oriented joint link scheduling and rate adaptation scheme. As observed by simulations, the scheme can provide agile on-demand coverage for all users with much reduced system overhead and a polynomial computation complexity. Moreover, it can achieve a prominent performance close to the optimal solution.

The unmanned aerial vehicle (UAV) network that is convinced as a significant component of 5G and emerging 6G wireless networks is desired to accommodate multiple types of service requirements simultaneously. However, how to converge different types of services onto a common UAV network without deploying an individual network solution for each type of service is challenging. We tackle this challenge in this paper through slicing the UAV network, i.e., creating logical UAV networks customized for specific requirements. To this end, we formulate the UAV network slicing problem as a sequential decision problem to provide mobile broadband (MBB) services for ground mobile users while satisfying ultra-reliable and low-latency requirements of UAV control and non-payload signal delivery. This problem, however, is difficult to be directly solved mainly due to the sequence-dependent characteristic and the lack of accurate location information of mobile users and accurate and tractable channel gain models in practice. To overcome these difficulties, we propose a novel solution approach based on learning and optimization methods. Particularly, we develop a distributed learning method to predict mobile users’ locations, where partial user location information stored on each UAV is utilized to train user location prediction networks. To achieve accurate channel gain models, we design deep neural networks (DNNs) that are trained by signal measurements at each UAV. To cope with the challenging sequence-dependent characteristic of the problem, we develop a Lyapunov-based optimization framework with provable performance guarantees to decompose the original problem into a sequence of separate optimization subproblems based on the learned results. Finally, an iterative optimization scheme joint with a successive convex approximation technique is exploited to solve these subproblems. Simulation results demonstrate the accuracy of the learning methods as well as the effectiveness of the Lyapunov-based optimization framework.

The combination of artificial intelligence and mobile edge computing (MEC) is considered as a promising evolution path of the future wireless networks. As a model-level coordination learning paradigm, federated learning can make full use of the distributed computation resource in the MEC systems, which allows the users to keep their private data locally. However, due to the unreliable wireless transmission circumstances and resource constraints in the MEC systems, both the performance and training efficiency of federated learning cannot be guaranteed. To solve this problem, the optimization design of federated learning in the MEC systems is studied in this paper. First, an optimization problem is formulated to manage the tradeoff between model accuracy and training cost. Second, a joint optimization algorithm is designed to optimize the model compression, sample selection, and user selection strategies, which can approach a stationary optimal solution in a computationally efficient way. Finally, the performance of our proposed optimization scheme is evaluated by numerical simulation and experiment results, which show that both the accuracy loss and the cost of federated learning in the MEC systems can be reduced significantly by employing our proposed algorithm.

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD's task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD's transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption.

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-to-digital converters, which are hard to implement due to their high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on single-bit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.

Mobile edge computing (MEC) has been envisioned as a promising approach for enabling the computation-intensive yet latency-sensitive mobile Internet services in future wireless networks. In this paper, we investigate the secrecy based energy-efficient MEC via cooperative Non-orthogonal Multiple Access (NOMA) transmission. We consider that an edge-computing device (ED) offloads its computation-workload to the edge-computing server (ECS) subject to the overhearing-attack of a malicious eavesdropper. To enhance the secrecy of the ED's offloading transmission, a group of conventional wireless devices (WDs) are scheduled to form a NOMA-transmission group with the ED for sending data to the cellular base station (BS) while providing cooperative jamming to the eavesdropper. We formulate a joint optimization of the ED's offloaded workload, transmit-power, NOMA-transmission duration as well as the selection of the WDs, with the objective of minimizing the total energy consumption of the ED and the selected WDs, while subject to the ED's latency-requirement and the selected WDs' required data-volumes to deliver. Despite the nature of mixed binary and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a three-layered algorithm for solving it efficiently. To further address the fairness among different WDs, we investigate a system-wise utility maximization problem that accounts for the fairness in the WDs' delivered data and the total energy consumption of the ED and WDs. By exploiting our previously designed layered-algorithm, we further propose a stochastic learning based algorithm for determining each WD's optimal data-volume delivered. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of the secrecy based computation offloading via NOMA.

Unmanned aerial vehicle (UAV) relay networks are convinced to be a significant complement to terrestrial infrastructures to provide robust network capacity. However, most of the existing works either considered enhanced mobile broadband (eMBB) payload communication or ultra-reliable and low latency communications (URLLC) control information communication. In this paper, we investigate resource allocation for the eMBB payload and URLLC control information communication multiplexing in a multi-UAV relay network. We firstly propose a multi-UAV relay model comprehensively considering path loss, small-scale channel fading and different quality of service requirements of eMBB and URLLC communications. Then we formulate the multiplexing problem as a joint user association, bandwidth and transmit power optimization problem to improve total transmission data rate and reduce power consumption. The solution of this problem is challenging due to different capacity characteristics of eMBB and URLLC communications, the coupling of continuous variables and integer variables, and the non-convexity. To mitigate these challenges, we equivalently decompose the original optimization problem into a URLLC problem and an eMBB problem. For the URLLC problem, we derive closed-form expressions of the optimal bandwidth and transmit power. For the eMBB problem, we develop an iterative solution framework of alternatively optimizing user association, bandwidth and transmit power.

Content caching is recognized as a promising solution to release the heavy burden of backhaul links and decrease the content transmission latency in Fog radio access networks (Fog-RANs). However, the content caching design is still a challenging problem with considering the user request patterns, the content delivery strategies, and the limited caching capacity. Recommendation has the capability of reshaping users' content requests for further prompting caching gain. The joint recommendation, caching, beamforming holds the potential to improve the system performance of Fog-RANs. In this paper, a joint recommendation, caching, and beamforming scheme is proposed for multi-cell multi-antenna recommendation aware Fog-RANs. Aiming at minimizing the content transmission latency, we formulate a joint recommendation, caching, and beamforming optimization problem. The minimization problem is a very challenging two-timescale mixed integer nonlinear programming problem, which is hard to solve in general. By exploring structural properties of the problem, we propose an alternative optimization algorithm with low complexity through decomposing the original problem into three sub-problems. Extensive simulations show that our proposed method can significantly reduce the content transmission delay.

We study the performance of wireless links for a class of Poisson networks, in which packets arrive at the transmitters following Bernoulli processes. By combining stochastic geometry with queueing theory, two fundamental measures are analyzed, namely the transmission success probability and the meta distribution of signal-to-interference-plus-noise ratio (SINR). Different from the conventional approaches that assume independent active states across the nodes and use homogeneous point processes to model the locations of interferers, our analysis accounts for the interdependency amongst active states of the transmitters in space and arrives at a non-homogeneous point process for the modeling of interferers' positions, which leads to a more accurate characterization of the SINR. The accuracy of the theoretical results is verified by simulations, and the developed framework is then used to devise design guidelines for the deployment strategies of wireless networks.

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword decoding error probability. In this paper, we propose to orchestrate network resources for a slicing system to guarantee more reliable bursty URLLC transmission. We re-cut physical resource blocks and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword decoding error probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem, which is challenging to be mitigated due to requirements of future channel information and of tackling a two timescale issue. To address the challenges, we develop a resource optimization algorithm based on a sample average approximate technique and a distributed optimization method with provable performance guarantees.

Vehicular networks allow vehicles to share information and are expected to be an integral part of future intelligent transportation systems (ITS). To guide and validate the design process, analytical expressions of key performance metrics such as packet reception probabilities and throughput are necessary, in particular for accident-prone scenarios such as intersections. In this paper, we present a procedure to analytically determine the packet reception probability and throughput of a selected link, taking into account the relative increase in the number of vehicles (i.e., possible interferers) close to an intersection. We consider both slotted Aloha and CSMA/CA MAC protocols, and show how the procedure can be used to model different propagation environments of practical relevance. The procedure is validated for a selected set of case studies at low traffic densities.

This paper considers a scenario in which an access point (AP) is equipped with a server of finite computing power, and serves multiple resource-hungry users by charging users a price. This price helps to regulate users' behavior in offloading jobs to the AP. However, existing works on pricing are based on abstract concave utility functions, giving no dependence on physical layer parameters. To that end, we first introduce a novel utility function, which measures the cost reduction by offloading as compared with executing jobs locally. Based on this utility function we then formulate two offloading games, with one maximizing individuals interest and the other maximizing the overall systems interest. We analyze the structural property of the games and admit in closed-form the Nash Equilibrium and the Social Equilibrium for the homogeneous user case, respectively. The proposed expressions are functions of user parameters such as the weights of time and energy, the distance from the AP, thus constituting an advancement over prior economic works that have considered only abstract functions. Finally, we propose an optimal price-based scheme, with which we prove that the interactive decision-making process with self-interested users converges to a Nash Equilibrium point equal to the Social Equilibrium point.

Optimization of information freshness in wireless networks has usually been performed based on queueing analysis that captures only the temporal traffic dynamics associated with the transmitters and receivers. However, the effect of interference, which is mainly dominated by the interferers' geographic locations, is not well understood. In this paper, we leverage a spatiotemporal model, which allows one to characterize the age of information (AoI) from a joint queueing-geometry perspective, for the design of a decentralized scheduling policy that exploits local observation to make transmission decisions that minimize the AoI. To quantify the performance, we also derive accurate and tractable expressions for the peak AoI. Numerical results reveal that: i) the packet arrival rate directly affects the service process due to queueing interactions, ii) the proposed scheme can adapt to traffic variations and largely reduce the peak AoI, and iii) the proposed scheme scales well as the network grows in size. This is done by adaptively adjusting the radio access probability at each transmitter to the change of the ambient environment.

Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user’s content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user’s personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks.

To maintain reliability of content-oriented wireless caching networks (CWCNs), repair mechanism is of necessity to be considered due to the natural that storage entities are individually unreliable and thus subject to failure on account of hardware error, network congestion or software updating. Meanwhile, recommendation is tunable for edge caching performance improvement. In this paper, we study the revenue maximization problem for CWCNs with both repair and recommendation considerations. The formulated problem is an integer non-convex and non-linear problem, and thus is difficult to be solved. The difficulties are intrinsically derived from the implicit weighted sum costs (WSCs) as regards storage and repair of each content and the coupling among the Boolean variables. For the sake of analytical tractability, a two-step methodology is developed. Specifically, we first explore the optimal storage and repair amount among the content providers to minimize the WSCs in terms of successfully fixing any occurred data corruption for the stored contents. Thereof, an explicit instance is provided to show how the contents can be coded, stored and then repaired in our network given that an error occurs. Based on the obtained storage and repair amount vectors, we solve the resultant joint caching and recommendation decision making problem (DMP). To be more specific, we decouple the DMP into a pair of subproblems, namely a cache placement and a recommendation optimization subproblems. For each subproblem, a globally optimal and a time-efficient suboptimal solutions are developed, respectively. Later, a versatile iterative paradigm is devised to do the decision making jointly. The convergence performance and the complexity analysis of the proposed algorithms are rigorously analyzed. Numerical results confirm the convergence performance of our iterative algorithms and illustrate their revenue improvements compared to various baseline schemes.

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.