Multi-beam satellites (MBS) exploit multiple spot beams and frequency reuse to achieve high spectral efficiency and flexible coverage, which are key characteristics of modern satellite communication systems. Among them, beam hopping (BH) leverages phased-array antennas to steer onboard beams and employs time-division multiplexing (TDM) to dynamically schedule illumination patterns in the time domain, with electronic beam position switching enabling rapid reconfiguration. However, existing works exhibit two critical limitations: (1) they rely on global decision strategies that incur high inference complexity, which further increases with the number of beams and ground cells, making it difficult to balance performance and computational complexity; (2) in pursuing high spectral reuse, they overlook beam overlap and co-frequency interference (CFI), and thus cannot effectively mitigate interference without sacrificing spectral efficiency. To address these challenges, this paper proposes a cell clustering-based BH (CCBH) algorithm with low complexity and interference avoidance. Specifically, we propose a region-growing cell-clustering method based on user demand load balancing in which each beam independently serves a cell cluster, and full-frequency reuse across beams maximizes spectral efficiency. In addition, based on the centralized training and decentralized execution (CTDE) paradigm, we propose a cooperative multi-agent soft actor–critic (SAC) framework with parameter sharing and centralized training, called CoopMASAC-PSCT. Among them, an SAC agent is deployed for each beam; Specifically, all actor networks share the same architecture and parameters, and the execution phase remains decentralized, with each actor making decisions solely on its local observations; To prevent inter-cluster interference, the shared global reward integrates system throughput, queueing delay fairness, and an interference-penalty term; Moreover, only a single global critic network is employed, which accesses the joint observations and actions of all agents during training, thus balancing individual beam performance with influence between all beams. Simulation and comparative analyses demonstrate that CCBH delivers better performance while significantly reducing inference complexity.

Low earth orbit (LEO) satellite networks are pivotal for sixth-generation (6G) wireless systems, yet their high-speed mobility induces frequent packet loss, causing severe head-of-line blocking delays under traditional retransmission mechanisms. While streaming forward erasure correction (FEC) can mitigate retransmissions, existing packet loss models fail to capture the unique dynamics of LEO networks, causing difficulties in the design and analysis of FEC schemes. This paper addresses this problem through the following contributions. First, based on real-world Starlink measurements, we reveal the inadequacy of conventional loss models such as those based on Markov chains. Second, we propose a Markovian arrival process (MAP) to model LEO packet loss. Using an expectation-maximization (EM) algorithm to fit Starlink traces, we demonstrate its superior accuracy over existing models. Third, based on MAP modeling, we show that the decoding delay of a typical streaming FEC scheme with fixed repair insertion intervals can be analyzed by approximating it as the busy period of a MAP/D/1 queue. Using matrix-analytic methods, we provide a numerical recipe to compute this delay. Simulations validate the precision of the model in predicting delay, offering practical guidelines for FEC design in LEO networks.

Massive multiple-input and multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) are foundational for downlink multiuser (MU) systems in future wireless networks, for their ability to enhance spectral efficiency and support a large number of users. However, high user density intensifies severe inter-user interference (IUI) and pilot overhead. Consequently, existing blind and semi-blind channel estimation (CE) and signal detection (SD) algorithms suffer performance degradation and increased complexity, and are further challenged by frequency-selective channels with high-order modulation demands. To this end, this paper proposes a novel semi-blind joint channel estimation and signal detection (JCESD) method. Specifically, the proposed approach employs a hybrid precoding architecture to suppress IUI. Furthermore, we formulate JCESD as a non-convex optimization exploiting constellation affine invariance. A few pilots are used to achieve coarse estimation for initialization and ambiguity resolution. For high-order modulations, a data augmentation mechanism utilizes the symmetry of quadrature amplitude modulation (QAM) constellations to increase the effective number of samples. To address frequency-selective channels, CE accuracy is then enhanced via an iterative refinement strategy that leverages improved SD results. Simulation results demonstrate an average throughput gain of 11% over pilot-based methods in MU scenarios, highlighting its potential for improving spectral efficiency.

Orthogonal time frequency space (OTFS) waveform has emerged as a promising technology for low-earth orbit satellite (LEO) communications owing to its capacity in mitigating Doppler effects. However, the OTFS modulation in LEO communications with massive multiple-input multiple-output (MIMO) techniques suffer from enormous training overhead due to the large number of satellite antennas. In this paper, we propose a hybrid model-data driven channel estimation scheme for massive MIMO-OTFS LEO satellite communications. We first derive the input-output relationship for massive MIMO-OTFS and formulate the channel estimation as a sparse signal recovery problem. Subsequently, we unroll the orthogonal approximate message passing (OAMP) algorithm into a model-driven deep unrolled network (DUN) to recover the sparse channel. On this basis, we design a side information and attention-enhanced OAMP network (SA-OAMPNet), which incorporates a data-driven self-attention mechanism into each iteration of unrolled OAMP network to further exploit the sparsity across the delay-Doppler-angle domain. Simulation results demonstrate that the proposed DUNs outperform conventional algorithms and deep learning methods. Remarkably, the proposed SA-OAMPNet reduces pilot overhead by about 30% without significant degradation in channel estimation accuracy.

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.

Data-assisted channel estimation (DA-CE) faces unique challenges in Low Earth Orbit (LEO) scenarios with large Doppler, where phase distortion and pilot sparsity degrade the utility of data symbols for refinement. Moreover, myopic, context-agnostic reliability metrics often fail to identify data symbols that are truly useful for improving estimation accuracy. To resolve these challenges, we innovatively reformulate the data selection as a pixel-level utility masking problem, similar to semantic segmentation tasks, where each resource element (RE) is evaluated for its utility in channel refinement. We introduce an integrated framework rooted in physics-informed reinforcement learning (PIRL), addressed by two symbiotic components. First, a Physics-informed Subspace Basis Expansion Models-LMMSE (PiSBEM-LMMSE) algorithm acts as the perception layer of our framework, which yields a high-fidelity, physics-consistent state representation by expressing the dominant Doppler-induced dynamics via a low-rank complex-exponential basis and decoupling them from residual stochastic fading. Second, a lightweight U-Net-based deep reinforcement learning (DRL) agent, acting as the cognitive decision core, learns an optimal, context-aware masking policy upon this structured representation. The U-Net architecture, with its encoder-decoder structure and skip connections, is specifically chosen to process the multi-channel, image-like state representation, capturing both global channel dynamics and local perturbations. Extensive simulations demonstrate that our framework achieves significant performance improvements over state-of-the-art methods, exhibiting remarkable robustness in LEO scenarios where conventional approaches fail.

Device-edge collaborative inference, which deploys well-trained artificial intelligence (AI) models at the network edge via the cooperation of edge devices and edge servers, emerges as a promising technique to provide ubiquitous intelligent services. In this paper, a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) scheme is proposed for the efficient implementation of device-edge collaborative inference. In the considered system, the technique of MIMO AirComp is utilized to aggregate local feature vectors, extracted from noise-corrupted sensory data on devices, at the server to efficiently derive a denoised global one for completing the downstream inference task. Device-edge collaborative inference features a task-oriented property, that concerns the effectiveness and efficiency of the task execution. In this case, the traditional AirComp criterion, i.e., minimum mean square error (MMSE), is not effective, since the same distortion level on different feature elements may have different influences on the inference performance. To this end, this paper directly adopts inference accuracy as the design objective. As the instantaneous inference accuracy is unknown during the design stage, an approximated but tractable metric, called discriminant gain, which measures the discernibility of different classes, is adopted. To maximize the inference accuracy measured by discriminant gain, a MIMO AirComp technique is proposed to jointly optimize all feature elements. The problem is nonconvex because of the complicated form of the objective function and the constraints. The solution based on semidefinite relaxation (SDR) and successive convex approximation (SCA) is employed to design a joint transmit precoding and receive beamforming scheme. Besides, to enhance the robustness of practical AI models in the inference stage, a post-processing design of feature magnitude normalization is proposed. Extensive experiments are conducted based on a practical human motion recognition task, which verifies our theoretical analysis and the superiority of our proposed scheme.

The development of satellite communication is driving the demand for device identification that can respond to security threats from illegal uplink terminals. Specific emitter identification (SEI) is a promising technology of physical layer device identification. Deep learning (DL)-based SEI can effectively learn features for distinguishing emitters through a large number of samples. In the satellite communication scenario, due to the high cost of labeling, wireless signals often face the dilemma of few labeled samples, leading to a decrease in recognition accuracy of existing methods. Therefore, we introduce an innovative FS-SEI method leveraging multi-domain fusion and metric learning (MDF-ML), eliminating the reliance on an auxiliary dataset. Specifically, MDF-ML is proposed to explore additional implicit samples within the sample space, enhancing generalization performance through multi-domain representation and phase rotation. It then uses metric learning to constrain feature distances in the feature space, thereby boosting discriminability. Our simulation results demonstrate that the accuracy of our proposed SEI method exceeds 90%, which outperforms the existing method by 24.21% under 5 sample shots.

The importance of UAV-satellite edge computing networks in disaster relief and scientific exploration has become increasingly prominent, attracting significant attention from both industry and academia. However, under a pre-planned task execution model, fluctuations in data volume often lead to inefficient offloading strategies, significantly increasing the energy consumption risk for UAV-satellite edge computing networks and, in extreme cases, resulting in system failure. Existing offloading approaches either disregard data volume uncertainty, adopt overly conservative robust optimization, or rely on unrealistic distribution assumptions, all of which limit their practicality. To address these limitations, we propose a historical data-driven distributionally robust optimization offloading scheme. Specifically, we first formulate an optimization problem to minimize the total energy consumption and leverage distributionally robust duality theory to derive a tractable formulation. Subsequently, we design an iterative solving algorithm based on the block gradient descent and successive convex approximation methods. Numerical simulations validate that our proposed scheme achieves lower system energy consumption compared to benchmark schemes.

In space-air-ground integrated systems, radar signal analysis is crucial for effective spectrum management. In recent years, time-frequency transforms (TFT) have gained significant attention for radar signal detection and identification. However, challenges such as cross-term interference and low signal-to-noise ratio (SNR) limit the effectiveness in multicomponent signal analysis. Therefore, this paper proposes a novel multitask learning-based TFT framework, named One-Stage TFT (OSTFT), which directly generates high-quality time-frequency representations (TFR) from raw in-phase and quadrature signals. OSTFT incorporates a generative network combined with classification and localization tasks to enhance feature extraction and image clarity. Experimental results demonstrate that OSTFT achieves superior performance in TFR quality and radar signal recognition, with an 81.4% detection rate using the You Only Look Once (YOLO) frame-work, outperforming existing TFT methods under various noise conditions. Compared to the best-performing TFR-denoising method, OSTFT improves the detection rate by 2.7%.

As a complement to terrestrial networks, low-Earth orbit (LEO) satellite networks are promising to provide ubiquitous and continuous services. To accommodate the dynamic topology of LEO satellite networks and increasing traffic demands, this article proposes a distributed routing approach to optimize the end-to-end delay relying on the multiagent deep reinforcement learning (MADRL), where each satellite node is deployed with an autonomous agent and makes its real-time next-hop decisions independently with local observation information. To promote the inner cooperation between decentralized agents, a centralized training scheme with a unified load-balancing reward is utilized by adopting a novel multiagent Transformer-MIX architecture. Moreover, to derive a better decision for each agent, we design an attention-involved agent network to capture more hidden information, and a Transformer-based parameter recurrent mechanism to generate the joint action-value function is used to enhance a more stable training. The simulation results indicate that our proposed scheme achieves faster convergence and demonstrates superior performance across several key performance metrics compared to existing benchmark schemes. Specifically, when the intersatellite link (ISL) failure rate in the network reaches 18%, our scheme achieves a reduction in end-to-end delay by 13.6% and an increase in packet successful delivery rate by 5.4% compared to the benchmark schemes.

The collaboration among multiple unmanned aerial vehicles (UAVs) can overcome the limitation of spatial sensing capabilities of individual UAVs has attracted extensive attention in the field of direction-of-arrival (DoA) estimation. Considering the constrained hardware resources in UAV swarm, maintaining coherence among all elements is extremely challenging. In this paper, we consider a collaborative UAV swarm with partly calibrated subarrays and propose a dual-driven joint-sparse pattern-coupled sparse Bayesian learning unfolding network (DD-JPC-SBLNet) for non-coherent DoA estimation. Specifically, we first propose a novel joint-sparse pattern-coupled sparse Bayesian learning (JPC-SBL) algorithm, which introduces a pattern-coupled model and a distributed noise variance estimation module, to improve the non-coherent DoA estimation accuracy. Then, the JPC-SBL algorithm is unfolded into cascaded customized neural networks, each of which learns the optimal coupling parameter configuration based on the pattern-coupled model and learns the current optimal signal hyperparameter update rule with a data-driven customized neural network. By effectively combining the advantages of model-driven and data-driven, the proposed dual-driven unfolding network exhibits superior convergence performance and speed. Simulation results demonstrate that the proposed method not only outperforms existing methods in terms of estimation accuracy and angular resolution, but also reduces computational complexity by more than 50% compared with other SBL-based algorithms.

In space–air–ground integrated networks (SAGINs), efficient task scheduling is critical to ensuring low latency and energy efficiency for computation-intensive applications. This paper proposes a hierarchical reinforcement learning (HRL)–based task scheduling framework for space–air integrated edge computing systems, consisting of low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs). The architecture is structured into two layers: UAVs handle task reception and local execution, while satellites assist in offloaded task processing. To enable intelligent and decentralized decision-making, we employ a multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm for UAVs and a TD3-based controller for satellite-level coordination. A shared reward mechanism is introduced to promote cross-layer optimization. Simulation results demonstrate that the proposed framework significantly reduces task execution delay and energy consumption compared to baseline schemes, achieving faster convergence. These results verify the effectiveness and practicality of the proposed method in dynamic and resource-constrained space–air environments. The proposed method reduces average total cost (ATC) by at least 13% compared to existing methods.

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated Quality of Service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the closed-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the Industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFSs) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

The rapid development of space-air–ground integrated technology has laid a foundation for achieving wide area wireless communication coverage, but its applicability is limited due to the limited computational capacity and energy storage of the terminal devices. This article thus proposes a space-air–ground integrated mobile-edge computing (MEC) task offloading and computing resource allocation (SIMOC) algorithm. First, a space-air–ground integrated MEC system is designed in which the terminal devices in the system can offload tasks to air- and space-based servers while performing energy harvesting. Second, an optimization objective of maximizing the task execution benefit minus the sum cost of task offloading and execution is established, for which the problem is transformed into a time-slot-based minimization problem of queue-stability minus revenue using Lyapunov optimization. Finally, the energy harvesting and task offloading subproblems of the queue-stability-minus-revenue problem are solved, respectively, to maximize the total revenue while ensuring device stability. Simulation results show that the SIMOC algorithm can reduce the all-task completion time by up to 98.16% compared with only local task execution algorithm in the absence of newly added tasks and shows good performance in handling newly added tasks. Meanwhile, the SIMOC algorithm has better performance compared to the particle swarm optimization algorithm.

Accurate estimation of channel state information (CSI) is crucial to realize the full potential of massive multiple-input multiple-output (MIMO) communication systems. Despite the high accuracy of existing CSI estimators, the high computational complexity makes them impractical for real-world massive MIMO systems. So, developing a highly accurate and low-complexity estimator has been a long-standing challenge in the field of massive MIMO. In this paper, we present a channel estimation scheme that significantly reduces computational complexity while achieving high accuracy. Firstly, we design a two-stage training protocol whereby only part of the transmit and receive antennas are activated for signal emission or reception. Secondly, we apply the low-complexity Least-Square (LS) estimator to acquire two sub-blocks of the channel matrix. Relying on the inherent low-rank property of channel matrix, the complete channel matrix is reconstructed via a randomized matrix approximation technique. Thirdly, we consider three different power allocation schemes to further optimize the pilot power in 2-stage training process. The theoretical bounds of estimation error for our CSI estimator are derived, and the optimal power allocation strategy is thus obtained by minimizing this error bound. Numerical simulations are provided to demonstrate our proposed method. As shown, the theoretical error bound is tight, and the optimal power allocation can achieve the substantial gain. Our CSI estimator incurs the same complexity as the popular LS estimator, whilst the estimation accuracy is improved by ~5 dB, which has the great promise to new-generation massive MIMO communications.

Adaptive bitrate (ABR) streaming provides an effective way to improve the Quality of Experience (QoE) of video users and is now the de facto standard for video delivery. Meanwhile, mobile edge computing (MEC) has been applied to assist ABR streaming, improving the performance of mobile networks and enabling efficient video delivery. However, smooth ABR streaming relies on the bidirectional adaptation between bitrate selection and bandwidth allocation, as they operate on distinct timescales and have different optimization goals. Moreover, since the constrained wireless resources available within a cell are shared by multiple users, their QoE should be optimized not only jointly but fairly. To this end, we propose a QoE-fairness-aware bandwidth allocation (QFA-BA) method for MEC-assisted ABR video transmission. With a novel perspective on buffer occupancy modeling, the relationship between bitrate selection and bandwidth allocation is studied. An enhanced QoE evaluation model is then proposed to correlate bitrate selection with bandwidth allocation and facilitate QFA-BA. Finally, a soft actor-critic (SAC) framework improving both the QoE and QoE-fairness is presented for QFA-BA. Compared with the state-of-the-art methods, our QFA-BA can perceive fine-grained buffer occupancy and stabilize it near a preset value with relatively more and larger bitrate switchings, exhibiting smoother convergence, better QoE (50.29%) and QoE fairness (54.81%).

Mobile edge computing (MEC) supported by Low Earth Orbit (LEO) satellite communication systems is a promising approach to improve the Quality of Service (QoS) of terrestrial users. Most existing offloading methods neglect the downlink connectivity of satellites during task offloading, which may bring in extra delay and affect the offloading efficiency. This paper considers satellite-terrestrial-MEC networks in which the tasks are generated by source devices, and the computing services are provided by destination devices or the LEO satellites. Furthermore, we propose a joint task offloading and beam hopping optimization method, which aims to minimize the average time delay of all computation tasks. We formulate the joint optimization problem as a zero-one integer programming problem, which is NP-hard, and provide a multi-agent deep reinforcement learning (MADRL) framework for the intelligent task offloading scheme. In this framework, each agent is responsible for either task offloading or beam hopping, with shared rewards to enhance cooperation between agents. Additionally, to reduce the training time of traditional reinforcement learning algorithms, an attention mechanism is incorporated into MADRL. Simulation results demonstrate that our method effectively reduces the average offloading delay compared with other baseline schemes.

The emergence of integrated positioning, communication, and computing (IPC2) technology has paved the way for advanced capabilities in physical-digital spatial positioning, intelligent communication, and computing. This article delves into an in-depth exploration of a federated learning-assisted multidimensionality fusion IPC2 system. Within this system, edge nodes collaboratively harness their locally distributed multidimensionality positioning and communication data to coordinate edge computing resources for model training. Throughout the process of fully distributed collaborative training, we focus on addressing two specific security concerns: 1) data tampering and 2) model tampering attacks. In pursuit of bolstering the system’s resilience against potential attacks, we introduce a novel federated-blockchain edge learning (FLBC) framework. This framework capitalizes on the inherent features of the blockchain, namely, its nontampering and traceability attributes. In addition, we present a meticulously designed verification algorithm tailored for the parameters aggregation process. Specifically, an aggregation algorithm is developed to enhance the efficiency and accuracy of the training model’s fitting. To assess the effectiveness of our proposed approach, comprehensive simulations are conducted using an openly accessible wireless artificial intelligence (AI) data set. The outcomes of these simulations clearly demonstrate that the proposed scheme adeptly combats data tampering attacks initiated by multiple malicious nodes and high-intensity model tampering attacks, all while maintaining minimal accuracy loss.

With the rapid development of cloud-edge–terminal (CET) technology, ubiquitous sensing devices are able to collaborate with edge terminals, enabling real-time, intelligent environmental awareness. For device-free sensing systems, the number of each human gesture category may vary (class imbalance), which makes previously distributed device-free sensing algorithms ineffective. In this article, we propose a novel monitoring scheme for device-free human action sensing for CET collaboration under class-imbalance conditions. Specifically, the body-coordinated velocity profile (BVP) features of wireless fidelity (WiFi) signals are used to detect human actions. To recognize human gestures, we develop a convolutional neural network (CNN) using a monitor to detect gradient changes under class imbalance. To mitigate the effects of class imbalance, a corresponding correction is applied to the loss function. To validate the effectiveness of the proposed method, we conduct numerical experiments under class-imbalance conditions. Different parameter settings and proportions of participating nodes are explored for their effects on experimental results. Additionally, numerical experiment results demonstrate that the proposed method improves recognition accuracy by 3.85%–34.1% compared to baseline algorithms. Overall, the proposed method addresses the challenge of distributed device-free sensing under class-imbalance conditions and achieves superior recognition accuracy performance.

The Industrial Internet of Things (IIoT) promotes the deep integration of new-generation communication technologies and industrial ecology. However, the popularity of computing and the proliferation of equipment scale make it a meaningful challenge to provide reasonable resource allocation for task offloading. Therefore, this article proposes a novel two-stage coordinated, distributed, and online multidomain virtual network embedding algorithm based on deep reinforcement learning (DRL)-assisted federated learning (FL) for task offloading in the IIoT. We model the IIoT as a dynamic multidomain structure and deploy local DRL servers in each factory domain combined with the distributed paradigm of FL to reduce the local resource fragmentation. Through local and global cooperation, the IIoT environment is controlled in a fine and macroscopic manner. In addition, the mechanisms of FL ensure the privacy of participant data. Finally, a comprehensive evaluation demonstrates the clear superiority of the proposed algorithm, which improves the long-term offloading revenue, resource utilization, and task offloading success rate by average 17.66%, 5.97%, and 4.52% compared to baselines, respectively.

Industry 4.0 integrates industrial Internet of Things (IIoT), artificial intelligence, and cloud computing. The advent of the 5G era has undoubtedly provided a new impetus for the development of many Industry 4.0 applications, but it also presents some key security hurdles. The network scale is becoming larger and larger, the network environment is becoming increasingly complex, and security risks are prominent. Frequent issues, such as malicious attacks, privacy information disclosure, and data transmission security. In order to improve the reliability and security of cyberspace, this article proposes a blockchain-based hierarchical IIoT security solution mechanism. In addition, we propose a random reputation voting mechanism and blockchain (RRV-BC) scheme based on verifiable random function and reputation voting to reduce the communication cost during blockchain consensus communication. Meanwhile, the node credit scoring mechanism is introduced to dynamically evaluate the node credit. The simulation results show that the scheme improves the reliability of data communication and the fault tolerance of consensus mechanism by an average of 5% compared with the traditional practical byzantine fault tolerance (PBFT) protocol method.

As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.

With the explosive growth in demands for mobile video services, the focus of cellular networks is evolving from the core network to the edge network to facilitate resource-intensive services and mitigate backhaul burdens. However, the overlapping in coverage and the user similarity in preferences lead to substantial cache redundancy. In addition, the mobility of users poses a significant challenge to smart devices for device-to-device (D2D) communications, which affects the content richness and hit ratio of the edge cache. In this paper, an adaptive cache partitioning and placement (ACPP) strategy is proposed for mobile edge computing (MEC) networks to minimize the average cost of content access. A practical two-layer collaborative caching model is presented, which comprises 5G base station (gNB) clusters and D2D caching. Besides, a public and private cache partitioning method is designed for gNBs to improve the content richness of local cache, and a static and dynamic cache partitioning method is developed for user devices to address the varying mobility patterns. Simulation results demonstrate the effectiveness of the proposed ACPP strategy in delivering content at a lower average cost, and achieve a better hit ratio with a relatively high energy consumption at user ends.

We consider the uplink connectivity for massive machine-type communications (mMTC) assisted by intelligent reconfigurable surfaces (IRSs), where device activity detection (DAD) and channel estimation (CE) are challenging due to limited pilot sequences. Moreover, differentiation among device types causes channels to deviate from the assumed characteristics, leading to performance degradation of conventional compressive sensing (CS) algorithms. To this end, two innovative networks driven by the hybrid of model and data are proposed exploiting the iterative frameworks and deep neural networks. We first present a hybrid driven iterative shrinkage thresholding algorithm, dubbed HISTA-Net, where a dual attention network (DAN) is embedded within the iterations to adaptively suppress iterative noise and enhance sparsity properties. Subsequently, we encapsulate data driven network and the intrinsic channel matrix knowledge, and derive a hybrid driven approximate message passing network (HAMP-Net) to further improve the sparse recovery performance. Our experiments demonstrate that the proposed networks outperform existing CS methodologies and deep learning strategies in accuracy, convergence, and generalization ability. Remarkably, the proposed HAMP-Net reduces pilot overhead by 30%, and achieves an NMSE gain of 3 dB for signal-to-noise ratios exceeding 15 dB.

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aided multiuser multiple-input single-output (MISO) systems with hybrid architectures. Combining the structure of model driven and data driven deep learning approaches, a hybrid driven learning architecture is devised for joint estimation and learning the properties of the channels. For a passive IRS aided system, we propose a residual learned approximate message passing as a model driven network. A denoising and attention network in the data driven network is used to jointly learn spatial and frequency features. Furthermore, we design a flexible hybrid driven network in a hybrid passive and active IRS aided system. Specifically, the depthwise separable convolution is applied to the data driven network, leading to less network complexity and fewer parameters at the IRS side. Numerical results indicate that in both systems, the proposed hybrid driven channel estimation methods significantly outperform existing deep learning-based schemes and effectively reduce the pilot overhead by about 60% in IRS aided systems.

Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing ambient intelligent services. For enhancing the utilization of limited network resources in edge-device co-inference tasks from a systematic view, we propose a task-oriented scheme of integrated sensing, computation and communication (ISCC) in this work. In this system, all devices sense a target from the same wide view to obtain homogeneous noise-corrupted sensory data, from which the local feature vectors are extracted. All local feature vectors are aggregated at the server using over-the-air computation (AirComp) in a broadband channel with the orthogonal-frequency-division-multiplexing technique for suppressing the sensing and channel noise. The aggregated denoised global feature vector is further input to a server-side AI model for completing the downstream inference task. A novel task-oriented design criterion, called maximum minimum pair-wise discriminant gain, is adopted for classification tasks. It extends the distance of the closest class pair in the feature space, leading to a balanced and enhanced inference accuracy. Under this criterion, a problem of joint sensing power assignment, transmit precoding and receive beamforming is formulated. The challenge lies in three aspects: the coupling between sensing and AirComp, the joint optimization of all feature dimensions’ AirComp aggregation over a broadband channel, and the complicated form of the maximum minimum pair-wise discriminant gain. To solve this problem, a task-oriented ISCC scheme with AirComp is proposed. Experiments based on a human motion recognition task are conducted to verify the advantages of the proposed scheme over the existing scheme and a baseline.

Space-aerial-terrestrial integrated networks (SATINs) have gained widespread recognition. Due to the mobility of wireless access points (APs) and user terminals (UTs), handover plays an essential role in ensuring the continuity and quality of communication in SATINs. However, existing handover strategies only consider the instantaneous communication benefits, which makes it difficult to maintain link stability with lower handover frequency. Given that reasonable handover paths help improve the stability of communication, we propose a handover planning (HP) strategy in SATINs, which fully considers the time varying link quality between two handover in planning the handover paths of mobile UTs. Simulation results demonstrate that the proposed HP strategy can maintain high-quality communication links with the lowest handover delay while performing relatively low in handover frequency.

Integrated sensing, computing and communications (ISC2) is a promising technology to enable both physical-digital spatial sensing, intelligent communication and computing. This paper studies a federated learning-assisted ISC2system, in which edge nodes coordinate edge computing resource for model training based on their local integrated sensing and communications (ISAC) data. In the process of a completely distributed collaborative training, sharing and transmission of local parameters may lead to a serious Byzantine attack. To improve the system's anti-attack capability, we design a blockchain-federated edge learning framework, which utilizes the non-tampering and traceability features of the blockchain, and design a verification algorithm for federated aggregation. Particularly, an aggregation algorithm is designed to improve the fitting efficiency and accuracy of our model. Experiments based on the measured ISAC data show that the proposed scheme can effectively resist up to 30% of data tampering and up to 30% of model tampering attacks.

Space-aerial-ground integrated networks (SAGIN) is gaining eye-catching attention in 6G research. Comparing with terrestrial networks, SAGIN guarantees to provide three-dimensional (3D), seamless connectivity, global coverage and high resource usage efficiency. In addition, network softwarization (NetSoft) is recognized as the crucial attribute of 6G networks. With softwarization, traditional dedicated hardware will be decoupled into software blocks and general-purpose hardware. Tailored service requests can be implemented in the forms of chained software blocks (also called as slices) and coexist on top of these general-purpose hardware. The softwarization scheme can enhance the resource utilization and service diversity. Though SAGIN and NetSoft are separately studied well, their joint research is still in its infancy. In this paper, we focus on the research of softwarized SAGIN and propose one novel resource allocation and orchestration framework, labeled as Stice-Soft-SAGIN. The goal of our Stice-Soft-SAGIN framework is to provide reliable and efficient slicing service in quasi-static state. When receiving one slicing service request, our Slice-Soft-SAGIN will conduct the first procedure of available resource checking. After successfully doing the resource checking, our Stice-Soft-SAGIN will turn to conducting the slicing resource allocation and orchestration from three ordered parts (terrestrial part, aerial part, and satellite part). Take note that resources considered in Stice-Soft-SAGIN belong to wireless (spectrum) and wired (computing and storage) types. In order to validate the Stice-Soft-SAGIN, we conduct the evaluation in the simulation form. Evaluation results are illustrated and analyzed.

With the development of commercial off-the-shelf hardware, low Earth orbit (LEO) satellites are promising to provide flexible edge computing services. In this paper, we investigate a digital twin (DT)-empowered satellite-terrestrial cooperative edge computing network, where computation tasks from terrestrial users can be partially offloaded to the associated base station (BS) edge server, the associated LEO satellite edge server, and an adjacent LEO satellite edge server. We formulate a multi-tier computation offloading optimization problem to minimize the weighted sum of total system delay and satellite energy consumption, where a LEO-layer problem and a DT-layer problem are involved. The LEO-layer problem optimizes the three-tier computation resource allocation and task splitting ratio. From the multi-satellite network perspective, the DT-layer problem optimizes how many resources will be shared between adjacent satellites. We then propose a multi-agent double actors twin delayed deterministic policy gradient (MA-DATD3) algorithm to optimize the LEO-layer problem, and adopt a centralized training and decentralized execution (CTDE) paradigm. The proposed MA-DATD3 algorithm is extended to solve the DT-layer problem in a centralized way, and the resource sharing between adjacent satellites is optimized to maximize the time-averaged reward. Simulation results show that our algorithm achieves a better performance than the MADDPG algorithm, and effectively improves the computation offloading performance while balancing the energy consumption and the total delay.

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental human activity recognition system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network, which integrates an attention mechanism and dual loss function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets.

As the 6G network is evolving towards a space-air-ground integrated scale with ubiquitous long-distance non-terrestrial network (NTN) links, packet-level streaming forward erasure correction (FEC), which can achieve low end-to-end in-order delivery delay over lossy links with long propagation delay, has drawn increasing interest. However, the existing streaming FEC has a problem that full-length encoding windows (EWs) including all non-acknowledged source packets are used when generating repair packets, which incurs high computational cost when the link’s bandwidth-delay product is large. To address the problem, this paper proposes a new low-complexity streaming FEC design, where a mixture of short and full-length EWs are used. We propose a novel method to analyze the decoding window width observed by arriving repair packets, which is based on the analysis of the busy period of a virtual queue using renewal theory. Later, using the analysis as the key enabler, a design problem is formulated and solved to optimize parameters including the EW width and the fraction of short-length repair packets such that the computational cost is reduced. Evaluations using real-life code implementations show that the proposed design can significantly reduce the computational cost, while maintaining the key benefits of the original streaming FEC.

Network Virtualization (NV) is an emerging network dynamic planning technique to overcome network rigidity. As its necessary challenge, Virtual Network Embedding (VNE) enhances the scalability and flexibility of the network by decoupling the resources and services of the underlying physical network. For future multi-domain physical network modeling with the characteristics of dynamics, heterogeneity, privacy, and real-time, the existing related works perform unsatisfactorily. Federated learning (FL) jointly optimizes the network by sharing parameters among multiple parties and is widely used to address data privacy and data silos. Aiming at the NV challenge of multi-domain physical networks, this work is the first to propose using FL to model VNE, and presents a VNE architecture based on Horizontal Federated Learning (HFL) (HFL-VNE). Specifically, combined with the distributed training paradigm of FL, we deploy local servers in each physical domain, which can effectively focus on local features and reduce resource fragmentation. A global server is deployed to aggregate and share training parameters, which enhances local data privacy and significantly improves learning efficiency. Furthermore, we deploy the Deep Reinforcement Learning (DRL) model in each server to dynamically adjust and optimize the resource allocation of the multi-domain physical network. In DRL-assisted FL, HFL-VNE jointly optimizes decision-making through specific local and federated reward mechanisms and loss functions. Finally, the superiority of HFL-VNE is proved by combining simulation experiments and comparing it with related works.

With the technological developments of satellite manufacturing and rocket launching, the constellation of satellites is increasingly normal. Electronic reconnaissance using satellites group is easy to implement. Bearings-only geolocation is an important research subject in the electronic reconnaissance field. In this manuscript, we focus on the performance analysis of bearings-only geolocation based on constellation of satellites. The geolocation model is firstly established for the scenario of satellites group. Second, angles of arrival (AOAs) in measuring coordinate systems (m-system) for different satellites are established and transformed to the standard earth-centered earth-fixed (ECEF). Then, we derive the Cramer Rao lower bound (CRLB) for the geolocation precision with the effects of attitude error and position error of satellites. Finally, simulation results are provided to reveal the theoretical performance of bearings-only geolocation based on constellation of satellites.

Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.

Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.

We investigate multiuser detection under uplink asynchronous scenario, where the triangular successive interference cancellation (T-SIC) detection scheme is exploited. As the existing analysis was conducted under some ideal assumptions for simplicity, the asynchronous scenario is analyzed under rigorous assumptions in this paper so that important insights are revealed for practical systems. Specifically, the average symbol error rate (SER) formulas are derived for a two-user system with arbitrary$M$-ary quadrature amplitude modulation ($M$-QAM) over Rayleigh fading channels. These solutions are shown to have higher accuracies compared to the existing solutions. Furthermore, a novel iterative detection algorithm is proposed, i.e., the parallel T-SIC method, which is more efficient and effective compared to the iterative T-SIC method. Insightful discussion is conducted in terms of the potentials of the concerned iterative detection method. Simulation results show that the most significant improvement of the iterative detection reflects on the SER for the strongest user, and the reduction is always within 50% of the SER compared to the primary detection. The gains obtained by the iterative detection is negligible for higher order$M$-QAM cases.

Wireless sensing can enable human identification by quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric system, WirelessID, that explores the human fine-grained behavior and body physical signatures embedded in channel state information by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to identification performance. Thus, to extract robust features, we introduce an attention mechanism into our system. Particularly, commercial Wi-Fi devices are used for prototyping WirelessID in a laboratory with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

WiFi-based human activity recognition technology has attracted widespread attention for its prominent application value and theoretical significance. Existing approaches have made great achievements in the same domain sensing, which means the activity samples applied for training the model have a similar distribution with the testing data. However, in practical application, we hope that the same activity of different people with various states and habits in different locations can be accurately recognized and produce the same reaction. Therefore, cross-domain sensing technology is pretty important. Some studies explore the location-independent and environment-independent methods, but few attempts consider the influence of the initial states of the users, such as standing and sitting, which actually have very different effects on the transmission of the wireless signal. This paper presents a human activity recognition method adapted to different initial states. Meanwhile, we solve the accompanying issue of the small sample size sensing, obviating the need for the cumbersome wok resulting from the massive data collection. We take advantage of the idea of metric learning and few-shot learning to realize cross-domain sensing with very few samples. The experiments demonstrate the feasibility and excellent performance of our method, which could recognize human activities with different initial states as the training data.

Device-free human activity recognition based on WiFi signals has become a very popular research field. However, it still has one major problem that is activities of “unseen” humans cannot be accurately classified, which makes it infeasible in real-world application. To tackle this issue, in this paper, we present a human activity recognition (HAR) system based on identity (ID) transfer mechanism named CrossID, which can cross the boundaries of identity by taking the high-level personal characteristics of the source domain and target domain as IDs for training and transferring. Specifically, we employ the margin-based loss function to improve the training speed and accuracy. To fully evaluate the feasibility of the proposed approach for human activity recognition, a variety of the data samples have been taken at 16 locations conducted by six people performing four different types of activities. Through extensive experiments on our dataset, we verify the effectiveness, robustness, and generalization ability of proposed system. Our average recognition rate in the target domain is 95%, which is slightly lower than 98% in the source domain.

Currently, developments in wireless sensing technologies have shown that wireless signals can be employed to transmit information between wireless communication devices and are also able to realize passive target wireless sensing. Wireless sensing has diverse Internet-of-Things applications in indoor human detection, such as in device-free localization, activity recognition and fall detection, respiration detection, gait recognition, user identification, and so forth. Deep learning (DL), with the latest breakthroughs in machine learning (ML) and artificial intelligence (AI), seems to be a feasible technique for device-free wireless sensing (DFWS) and human detection in a more intelligent and autonomous manner. Although DL has attracted wide spread attention in computer vision (CV), AI games, speech recognition, automated vehicles, and other fields, its application in wireless sensing systems (WSSs) is relatively new, and little attention has been paid to it. Motivated by these developments, this article clarifies the motivation and mechanism of the DL-aided WSSs for human detection. First, we survey the most advanced architecture of DL that may be powerful for WSSs. We also review conventional ML and DL approaches to human detection based on red green blue (RGB)/depth camera and radar: one reason is to introduce the successful experience in these areas to the field of wireless sensing and another reason is that the possibility of combining and fusing information from the heterogeneous types of sensors is expected to improve the overall performance of practical human detection systems. We provide a comprehensive survey of the state-of-the-art research on wireless sensing for human detection with a focus on WSSs. Furthermore, a general structure of the DL-based WSS is introduced in detail for hitherto unexplored applications and future wireless sensing scenarios. We also discuss some open research issues in wireless sensing for human detection, including data acquisition for DL model training, calibration of signals from commercial devices, multimodal sensing, simultaneous user identification and activity recognition, multiuser human detection, and generalization ability of DL models, to indicate future research directions.

Transmit antenna selection (TAS) is an emerging technology in physical layer security. To provide new insights into the achievable secrecy performance of TAS in practical communication systems, this paper investigates the average secrecy rate (ASR) and secrecy outage probability (SOP) under practical modulation schemes in TAS aided multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channels over Rayleigh fading. Particularly, this research concentrates more on the square M-ary quadrature amplitude modulation (M-QAM). Furthermore, in the considered MIMOME channel, a single antenna is selected to transmit the secret message, and selection combining (SC) or maximal-ratio combining (MRC) is utilized at the legitimate receiver and the eavesdropper. Based on this system model, novel expressions for the ASR and SOP are formulated to characterize the secrecy performance of the finite-alphabet driven MIMOME channel. Besides exact analysis, an asymptotic analysis is performed using the considered performance metrics in high signal-to-noise ratio (SNR) regime. Theoretical analyses suggest that the asymptotic ASR and SOP converge to finite constants in high SNR regime due to the discrete constellation constraint, and we find that the asymptotic behaviour of discrete inputs differs from that of Gaussian inputs. Furthermore, we derive concise expressions to characterize the rate of convergence (ROC) of the ASR and SOP, respectively. To unveil more system design insights, we discuss the relationship between the ROC and several important system parameters such as the antenna number and the modulation order.

To analyze the achievable performance of antenna selection (AS) in practical multi-antenna systems, this paper studies the receive antenna selection (RAS) in single-input multiple-output (AS-SIMO) systems under discrete inputs. We first propose an approximate expression to evaluate the instantaneous mutual information (MI) of M-ary quadrature amplitude modulation (M-QAM) signaling over additive white Gaussian noise (AWGN) channels. Then, by exploiting this approximate formula, we develop a closed-form formula for the ergodic MI in AS-SIMO systems with M-QAM signaling. Additionally, we also analyze the asymptotic MI for a large number of receive antennas Nr. This asymptotic analysis suggests that the scaling rate of the MI with Nr becomes zero rate in contrast to the double logarithmic rate under Gaussian inputs. Besides, our result is also extended to discuss the mutual information of multiple-input multiple-output (MIMO) systems having discrete inputs with receive antenna selection, and an upper bound for the MI is derived. Finally, the derived result is applied to analyze several performance measures of the discrete inputs driven ASSIMO systems. Specifically, it is first used to discuss the relationship between the ergodic MI and the number of active antennas. Our investigation shows that this relationship follows Pareto principle, i.e., 80% of the MI of full-antenna selection can be achieved via 20% of the total antennas. Then, our proposed approximation is employed to analytically study the effective MI which takes channel estimation (CE) into consideration, indicating that CE is a main limit of large-scale systems. Moreover, the energy efficiency (EE) is explored on the basis of our results, and we find there exists an optimal number of active antennas to maximize the energy efficiency. In addition to theoretical derivations, all the analytical results are validated by numerical simulations.

Nowadays, more and more researchers are paying their attention to green routing. In this paper, we consider power consumption as a kind of QoS (quality of service) and apply a new learning-based approach for energy efficient transportation and QoS routing. Compared with traditional rule-based methods, the proposed method can learn additional information from the networks to improve routing performance, and have the flexibility to meet different QoS requirements. First, we propose a new identification of network nodes, namely node vectors, and a basic routing algorithm using node vectors is designed accordingly. Then, energy efficient transportation and QoS routing are proposed by adding QoS constraints into the routing decision. Link attributes such as power consumption, bandwidth and delay can be learned from these node vectors with neural networks. The learned link attributes together with the estimated distance can be used for routing decisions with QoS constraints. Simulation results show that the proposed method is reliable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

In the scenario of frequency coexistence between the GEO (geostationary) and NGEO (non-geostationary) satellite networks, the NGEO system should not incur harmful interference to the GEO system according to the policy of the Radio Regulations. Therefore, spectrum sensing as a promising solution is applied widely in this scenario. With the increasing number of NGEO satellites in the space, one NGEO system could be affected by other NGEO systems while sensing the signal from the GEO system. Given these preconditions, the cognitive radio (CR) scenario considered in this paper is that: the GEO system is regarded as the primary user, one NGEO system is regarded as the secondary user, while another NGEO system is regarded as the interfering user. Meanwhile, all the satellite systems are supposed to operate with more than one discrete transmit power levels which is practical and fits the concept of adaptive power control. In our context, we propose a spectrum strategy using hypothesis testing as well as maximum a posterior (MAP) to differentiate the GEO signal from the interfering NGEO and noise, and then identify the specific power level utilized by the GEO system. Moreover, we derive the closed-form expressions for threshold of verifying the status of the GEO, and for decision regions to determine its power level. Finally, extensive simulations are provided to verify the proposed studies.

In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM), which iteratively reduces the GLM problem to a sequence of standard linear model (SLM) problems. This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms. Specific instances elucidated under such framework are the GLM versions of approximate message passing (AMP), vector AMP, and sparse Bayesian learning. It is proved that the resultant GLM version of AMP is equivalent to the well-known generalized approximate message passing. Numerical results for one-bit quantized compressed sensing demonstrate the effectiveness of this unified framework.

In millimeter wave (mm-wave) massive multiple-input multiple-output (MIMO) systems, acquiring accurate channel state information is essential for efficient beamforming (BF) and multiuser interference cancellation, which is a challenging task since a low signal-to-noise ratio is encountered before BF in large antenna arrays. The mm-wave channel exhibits a 3-D clustered structure in the virtual angle of arrival (AOA), angle of departure (AOD), and delay domain that is imposed by the effect of power leakage, angular spread, and cluster duration. We extend the approximate message passing (AMP) with a nearest neighbor pattern learning algorithm for improving the attainable channel estimation performance, which adaptively learns and exploits the clustered structure in the 3-D virtual AOA-AOD-delay domain. The proposed method is capable of approaching the performance bound described by the state evolution based on vector AMP framework, and our simulation results verify its superiority in mm-wave systems associated with a broad bandwidth.

With the rapid increase of mission demands for the tracking and data relay satellite system (TDRSS), the technical issue of high-efficient scheduling has attracted more attention in recent years. Most of previous scheduling algorithms are designed based on the assumption of missions' uniform time- space distribution, which have showed unsatisfactory performance in real scenarios with non-uniform distribution of mission demands. In this paper, we first transform the TDRSS scheduling problem into the heterogeneous inter- satellite link antenna (ILA) pointing route problem. Then, a two-stage heuristic algorithm with hierarchical scheduling strategies is proposed with the consideration of non-uniform time-space distribution of missions. Finally, we employ the TDRSS dataset to verify our proposed algorithm by comparing with the improved Rojanasoonthon's greedy randomized adaptive search procedure (GRASP) algorithm. Experimental results show that our proposed two-stage heuristic algorithm can schedule 2.41%, 4.43% and 6.02% more missions and consume 11.84%, 10.38% and 9.54% less setup times of SA antennas than the improved GRASP algorithm for the mission scale of 200, 400 and 600, respectively. In addition, setup times of SA antennas in those instances with non-uniform distribution in space can be more efficiently compressed by our proposed two-stage heuristic algorithm.

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

Multibeam satellites allow for significant boost in capacity by reusing the available spectrum and regularized zero-forcing (RZF) precoding promises to be one efficient technique to manage the inter-beam interference in the forward link. In this paper, the RZF precoding problem is first cast within an equivalent Bayesian inference framework. Then, we propose two kinds of message passing based precoding approaches, namely variational message passing based RZF (VMP-RZF) and approximate message passing based RZF (AMP-RZF). Compared with evaluating RZF directly, our proposed methods circumvent the matrix inversion operation and thus enjoy a much lower complexity. Simulation results demonstrate that the normalized mean square errors of both VMP-RZF and AMP-RZF with respect to the true RZF are negligible while AMP-RZF converges faster than VMP-RZF.

Iterative decoding of orthogonal convolutional code is widely used for its excellent performance. However, the iterations lead to high complexity and long decoding delay, which is unsuitable for low- rate voice service in mobile satellite communications with on-board processing. In this paper, a novel scheme of orthogonal convolutional coding as well as an associated non-iterative joint decoding algorithm based on factor graph are proposed. Due to its non-iterative nature, this novel scheme has low decoding complexity and short latency, which indicates its potential on-board use in the low-rate satellite voice service, or other kinds of services with a high bit error rate (BER) tolerance and a tight delay requirement. Simulation results demonstrate the efficacy of the proposed novel coding scheme and non-iterative decoding algorithm in both AWGN and Rician channels.

An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

In this paper, we propose an expectation propagation based iterative multi-user detection algorithm for multiple input multiple output interleave-division multiple access (MIMO-IDMA) systems with high-order modulation. The proposed detector can be well integrated into the traditional structure of turbo receivers for MIMO-IDMA systems. By formulating a scalar factor graph representation of the multi-user detector and choosing Gaussian distribution as the projection set for the symbol belief, the overall detection complexity can be reduced to scaling linearly with the number of users, the number of receive antennas and transmit antennas. Numerical results for coded MIMO-IDMA systems with 16-QAM modulation show that our proposed algorithm outperforms the factor graph based detection with Gaussian approximation in terms of the bit error rate (BER) performance with lower complexity.

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.