Millimeter-wave rate-splitting multiple access (mmWave-RSMA) technology combines the wideband characteristics of millimeter waves with the rate-splitting mechanism of RSMA, significantly improving the communication efficiency of the system. However, this feature also poses greater challenges to its secure transmission, especially in networks with dual-identity nodes which act as legitimate users while also being able to eavesdrop on information from other users. This behavior will simultaneously weaken the security of both public and private streams. In this regard, the active reconfigurable intelligent surface (RIS) offers a promising method by suppressing eavesdropper channels and enhancing the quality of legitimate links. Focusing on the physical layer security (PLS) challenge posed by dual-identity users in mmWave-RSMA networks, this paper proposes an active RIS assisted secure beamforming scheme integrated with resource allocation optimization. Specifically, while ensuring the quality of service (QoS) for dual-identity user and power amplification constraints of the active RIS, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming vector, reflection coefficient matrix, common rate allocation, and power allocation coefficients. To solve this non convex problem, we divide it into three subproblems. Then, successive convex approximation (SCA) and semidefinite relaxation (SDR) are used to solve these subproblems. Simulation results validated the critical role of active RIS in reducing eavesdropping, and emphasized the importance of joint resource allocation for secure beamforming in mmWave-RSMA.

This paper investigates the uplink massive grant-free random access in low-earth-orbit (LEO) satellite-based Internet-of-Things systems, where accurate joint activity detection and channel estimation (JADCE) is essential for reliable data recovery. However, the resource constraint and high-dynamic characteristic of LEO scenarios pose significant challenges to traditional JADCE schemes in terms of both estimation accuracy and computational complexity. To overcome these limitations, we propose a novel deep-unfolding JADCE framework based on the synergistic multi-stage optimization iterative shrinkage thresholding algorithm network, referred to as SMO-ISTA-Net, to facilitate efficient massive device access. Specifically, we first develop a synergistic attention module, where an inertia-guided optimization strategy is introduced into the gradient descent process to improve convergence stability and adaptability to fast-varying satellite channels. In order to mitigate the temporal feature inconsistency caused by asynchronous access and multi-path propagation, we further design a lightweight cross-attention mechanism that enables efficient channel feature fusion and facilitates multi-stage information interaction. Moreover, we propose a memory-enhanced proximal-mapping module that incorporates a high-throughput short-term memory mechanism into the unfolded structure, so as to significantly reduce information loss and maximize memory retention of the network. Extensive simulations under diverse LEO scenarios demonstrated that our scheme can achieve superior convergence speed, estimation accuracy, and preamble efficiency, while maintaining low computational complexity and short runtime, compared to the state-of-the-art model-driven JADCE schemes.

Recently, index modulated orthogonal time frequency space modulation combined with multi-input and multi-output (MIMO-OTFS) has been introduced to get superior bit error rate (BER) performance than conventional MIMO-OTFS schemes. In this paper, we propose a novel transmission scheme called generalized space-delay-Doppler index modulated OTFS (GSDDIM-OTFS) to further utilize the multi-domain resources and explore the potential benefits of the index modulated MIMO-OTFS. In this scheme, additional information bits are transmitted through the combined space-delay-Doppler resource units. We also derive the analytical expressions of average bit error probability (ABEP) to evaluate the performance of the proposed scheme. For multi-domain index modulation schemes, the traditional detection suffers a supreme complexity with a large size of look-up table. To address this issue, we propose a coarse-to-fine (CTF) network for the GSDDIM-OTFS detection, called the CTFIM detector. In the proposed detector, the characteristic of the transmit constellation of index modulated schemes is fully utilized and we explore the coarse-to-fine strategy to capture the general features more efficiently from different dimensions. Specifically, the coarse module is used to capture features based on the index pattern and the fine classification to establish the global relationships in each GSDDIM-OTFS subblock. Furthermore, we also employ feature fusion to increase the feature dimensions. Simulation results demonstrate the enhanced performance of the GSDDIM-OTFS over doubly-selective fading channels and the proposed DL-based detectors under perfect and imperfect channel conditions.

Recently, the application of deep learning (DL) technologies to communication systems has garnered significant attention. The Mamba architecture can adaptively adjust the model parameters based on the dynamic changes of the sequence, effectively capturing complex dependencies in long sequences. Furthermore, Mamba provides similar modeling capabilities to Transformers while ensuring near-linear scalability with respect to sequence length. In this paper, we propose a Mambabased detector for orthogonal time frequency space with index modulation (OTFS-IM), termed as MambaIM, that can extract features and establish the global input-output relations from the received signal. Simulation results demonstrate that the proposed MambaIM detector outperforms current DL-based detectors in terms of bit error rate (BER) performance.

This paper addresses hierarchical service placement, UAV trajectory design, access control, and resource scheduling issues in multi-access edge computing (MEC)-enabled time division multiple access (TDMA)-based air-ground integrated networks (AGINs) comprising multiple unmanned aerial vehicles (UAVs) and a ground base station. UAVs perform environmental sensing and data collection, but their limited onboard computing and energy resources pose challenges for handling heterogeneous, delay-sensitive tasks. To tackle this, we propose a two-tier actor-based deep reinforcement learning (DRL) algorithm for multi-timescale decision making. A high-level deep Q-Network (DQN) agent determines frame-level service placement, while a low-level improved deep deterministic policy gradient (IDDPG) agent manages time-slot-level UAV trajectory, access control, bandwidth allocation, and transmission power. A constraint-aware reward mechanism ensures learning stability and feasibility. Simulations show that the proposed method outperforms baseline schemes in task success rate, energy efficiency, and scheduling fairness.

This paper investigates the secrecy performance of an energy-causality wirelessly powered backscatter communication (WPBC) network under Nakagami-m fading channels. We first define a performance metric called as the strict secrecy outage probability (S2OP), which accounts for both the energy-causality constraint of the backscatter device (BD) and whether the BD’s transmitted codewords can be decoded by the legitimate receiver. We then derive closed-form expressions for the S2OP and the reliable transmission probability under on-off fixed-rate secure transmission scheme (FRTS) and on-off adaptive-rate secure transmission scheme (ARTS). We also derive the asymptotic expressions of the S2OP. Numerical simulations validate the theoretical analysis and demonstrate the impact of key system parameters on secrecy performance for both FRTS and ARTS.

With the rapid advancements in technologies such as autonomous driving, intelligent robotics, unmanned aerial vehicles, and low-altitude economy applications, the demand for high-precision and highly robust positioning systems has become increasingly critical. Multi-sensor fusion positioning has gained widespread adoption due to its superior accuracy and robustness, with factor graph optimization receiving particular attention for its efficient modeling of multi-source constraints. However, traditional approaches often assume fixed noise levels, neglecting the dynamic variations of uncertainty in complex environments. To address this limitation, this paper proposes an uncertainty-aware GNSS/IMU/Vision multi-sensor fusion positioning algorithm. Recognizing the distinct error characteristics of GNSS and IMU, the error state Kalman filter (ESKF) is first employed to develop dynamic uncertainty models for each sensor. Building upon this, an adaptive weighting factor based on the covariance trace is introduced to apply uncertainty weighting to the GNSS/IMU data, thereby mitigating the impact of error interference during high-noise periods. Finally, the weighted multi-source data, along with visual feature observations, are incorporated into the factor graph optimization framework, enabling global state estimation within a sliding window. The proposed method is validated using the GVINS public dataset, and experimental results demonstrate its superior performance in challenging low-altitude economy scenarios, such as weak GNSS signals and significant IMU drift. Compared to traditional factor graph optimization algorithms, the proposed method improves positioning accuracy by 33% and reduces velocity error by 40%.

Edge collaboration has emerged as a promising paradigm for Internet of Things (IoT) applications. However, achieving efficient cooperation among these server nodes still faces several critical challenges, including 1) secure node interaction, 2) online task scheduling, and 3) heterogeneous resource management. Unfortunately, most existing solutions address these issues in isolation, lacking an integrated framework that jointly considers security, task scheduling, and resource management. To address these limitations, this paper proposes a blockchain-based online collaboration framework for IoT, where blockchain serves as a trusted top-layer management platform to ensure secure information sharing and resource management. In the proposed framework, we introduce two dynamic queues to effectively manage randomly arriving tasks and develop an online collaboration mechanism tailored for heterogeneous edge servers. Furthermore, we formulate a long-term system utility maximization problem by jointly optimizing collaboration strategies, resource allocation, and block producer selection, subject to queue stability and security constraints. Due to the coupling among decision variables and across time slots, solving the optimization problem directly is challenging. Therefore, we design a novel Lyapunov-based algorithm that integrates convex optimization theory with deep reinforcement learning (DRL), significantly improving the solving efficiency. Extensive simulations demonstrate that the proposed method and algorithm outperform conventional baseline methods and pure DRL-based approaches in terms of system utility, stability, and security performance, making it a promising solution for secure and efficient edge collaboration in dynamic IoT environments.

As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.

Abstract In recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.

Nowadays, real-time interactive content services have been the most dazzling sector of next-generation Internet. The high-quality perceptions of virtual scenes have given rise to the strict requirements of high bandwidth and low latency, where the edge-cloud system promises several benefits. However, there still remain prominent challenges, when taking the economical efficiency into consideration, including location-heterogeneity, context-directability, and capacity-exploitation. In this paper, we propose a location-context-capacity aware bandwidth cost economizing strategy in the edge-cloud system, i.e., LoCoCa. LoCoCa adopts server pools partition mechanism, then achieving the optimal burstable billing in each pool. Here, a location-aware graph construction and partition algorithm is designed to solve the server pools partition problem. Then an improved burstable billing optimization mechanism, with a context index and an adaptive bandwidth capacity, is also proposed to economize bandwidth costs. Finally, the realistic edge-cloud company's trace-based experimental results verify LoCoCa reduces bandwidth costs by 81.83 %, compared with the baselines.

Computing Power Network (CPN) is an evolution of multi-access edge computing. Since the skyrocketing proliferation of CPN s, energy consumption aggravates explosively. However, majority of energy is wasted due to the incomplete analysis of tasks and resources, such as coarse-grained tasks consideration, coarse-grained resources integration, and unfocused complex information. In this paper, we propose a broad attention based fine-grained task offloading approach in green CPNs, i.e., Bat-FG. Specifically, for fine-grained tasks, we establish directed acyclic graphs (DAGs) subtasks offloading problem for green CPNs under the dependency and service constraints. For finegrained resources, decentralized resources are integrated into resource pools. Bridging the gap between fine-grained tasks and resource pools, we design a novel broad attention meta-reinforcement learning approach, i.e., Bat-MRL to focus on the main information for reducing the tasks' latency and energy consumption. Finally, extensive simulations show that Bat-FG significantly reduces 25.6 % task latency and 72.9 % energy consumption.

Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are Line-of-Sight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make them vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. In this work, we investigate the security of aerial underlay Internet of Things (IoT) systems by jointly designing trajectory and user scheduling. An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial jammer transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the secondary user is maximized by optimizing multiple users’ scheduling, the UAVs’ trajectory, and transmit power. To solve the nonconvex optimization problem with a mixed multi-integer variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial in improving the secrecy performance of aerial underlay IoT systems.

This paper proposes a new scheme to enhance the secrecy performance of non-orthogonal multiple access (NOMA)-based coordinated direct relay transmission (CDRT) systems with an untrusted relay. The physical-layer network coding (PNC) and the NOMA schemes are combined to improve spectrum efficiency. Furthermore, inter-user interference and friendly jamming signals are utilized to suppress the eavesdropping ability of the untrusted relay without compromising the acceptance quality of legitimate users. Specifically, the far user in the first slot and the near user in the second slot act as jammers that generate jamming signals to ensure secure transmissions of confidential messages. We investigate the secrecy performance of the NOMA-based CDRT systems with the PNC scheme and derive the closed-form expression for the ergodic secrecy sum rate. The asymptotic analysis at a high signal-to-noise ratio is performed to obtain more insights. Finally, simulation results are presented to demonstrate the proposed scheme’s effectiveness and the theoretical analysis’s correctness.

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision-making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this article, we propose a proof-of-learning consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge’s reputation opinion by executing a smart contract, and partly adopts the winner’s learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts.

Nowadays, numerous primary technologies, like ultra-dense networks (UDNs) and Base Stations (BSs) sleeping state, are developed in fifth-generation (5G) networks. Due to the UDNs, the number of BSs in 5G networks is proliferating, along with the energy consumption. Therefore, it is necessary to cut down the energy attrition in 5G networks under the assurance of delay. Till now, some researchers have proved that the association of users and the sleeping states of BSs have a significant effect on energy consumption and latency in 5G networks. However, the traditional solutions associate users and select states nonadaptively without the dual consideration of energy-saving and delay. In view of this, we propose a dual-adaptive delay-aware and energy-saving system (DADEs) in 5G networks. To further optimize the energy and delay of 5G BSs, the model is split into two tandem problems: user association and BS state selection. Meanwhile, a tandem deep reinforcement learning (T-DRL) algorithm is presented to make decisions in these problems for optimizing and balancing performance between delay and energy adaptively. Additionally, the real datasets of 5G users and BSs are used and trained in this paper. Finally, simulation results show that the DADEs saves more than 50% of energy with an adaptive and satisfying latency.

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor-critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Unmanned aerial vehicles (UAVs) will be extensively utilized in various Internet of Things (IoT) scenarios due to their flexible mobility and rapid on-demand deployment. It becomes necessary and urgent to investigate the secrecy performance of the UAV IoT communication systems because of the open characteristics of wireless channels. In this article, the physical layer security of UAV IoT communication systems is studied, in which a ground IoT device transmits some confidential messages to a UAV hovering in the air, while a random number of eavesdroppers are randomly positioned around the ground source. The ground-to-air channel is assumed to experience Rician fading for the case of line-of-sight propagation and Rayleigh fading for the case of nonline-of-sight propagation, respectively. In addition, in order to improve the security of the system, we also investigate the secrecy performance of the UAV IoT system with a friendly UAV that generates jamming signals to distract the eavesdroppers. Utilizing the stochastic geometry theory, the cumulative distribution functions for the signal-to-interference-plus-noise ratio of the main and eavesdropping links are derived, and then the analytical expressions of the secrecy outage probability and the average secrecy rate are obtained. Finally, the accuracy of the analytical results is verified by Monte Carlo simulation.

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

In this paper, we propose a Bayesian cooperative detection algorithm for ambient backscatter assisted generalized spatial modulation (AB-GSM) system, which recovers information from both the ambient backscatter sensor (ABS) and the generalized spatial modulation (GSM) source. To exploit the inherent sparsity of GSM, we adopt a two-layer hierarchical prior model for source symbol. Moreover, we derive linear detectors for comparison. Simulation results show that the proposed algorithm can achieve a superior detection accuracy than linear detectors in under-determined AB-GSM systems.

Codebook design is one of the core technologies in limited feedback multi-input multi-output (MIMO) communication systems. However, the conventional codebook designs usually assume MIMO vectors are uniformly distributed or isotropic. Motivated by the excellent classfication and analysis ability of clustering algorithms, we propose a K-means clustering based codebook design. First, large amounts of channel state information (CSI) is stored as the input data of the clustering, and finally divided into N clusters according to the minimal distance. The clustering centroids are used as the statistic channel information of the codebook construction which the sum distance is minimal to the real channel information. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms conventional schemes, especially in the non-uniform distribution of channel scenarios.