The growing complexity of urban transportation networks demands intelligent, data-driven systems capable of real-time perception, optimization, and prediction. Within the Internet of intelligent Vehicles (IoIV), optimal sensor placement is essential for enhancing distributed edge intelligence, as it significantly improves traffic observability, ensures accurate data collection, and enables dynamic decision-making. In this context, we propose a dual approach for hybrid quantum-classical (HQC) optimization—classical for quantum and quantum for classical. First, we formulate the optimal sensor placement problem within the quantum approximate optimization algorithm (QAOA) framework using a minimum vertex cover approach to address its NP-hard nature. Moreover, we enhance the QAOA by integrating long short-term memory (LSTM) networks to adaptively optimize its parameters, thereby achieving faster convergence and improved surveillance coverage in urban transport networks. Second, we employ quantum LSTM (QLSTM) networks to predict traffic flow from data collected by the optimally placed sensors. The QLSTM model reduces the number of learnable parameters while maintaining the expressive capability of classical LSTM architectures. This semantic information is then leveraged to manage traffic flow more effectively, predicting traffic patterns and supporting proactive decision-making. Experimental results demonstrate that the LSTM-guided QAOA outperforms conventional optimization methods such as stochastic gradient descent, achieving faster and more reliable convergence. Likewise, the QLSTM model attains superior predictive accuracy, as evidenced by significant improvements in the explained variance score and root mean squared error. Collectively, these advancements represent a substantial step forward in intelligent traffic management and highlight the practical potential of HQC machine intelligence in IoIV systems.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN)-assisted edge computing systems, with the goal of maximising the ratio of tasks successfully offloaded and executed within quality-of-service (QoS) constraints. In the considered system, ground users offload computation tasks to a satellite-mounted edge server via unmanned aerial vehicles (UAVs) acting as relays. The formulated optimisation problem jointly considers task offloading portions and bandwidth allocations across ground-to-air and air-to-space links, subject to constraints on transmission rates, total bandwidth, energy budgets, and the satellite’s computational capacity. The resulting problem is non-linear, non-convex, and mixed-integer, making it challenging to solve with traditional optimisation techniques. To this end, we propose a deep reinforcement learning (DRL)-based solution to learn optimal offloading and resource allocation policies in dynamic environments. Furthermore, to enhance scalability and decentralised coordination, we develop a multi-agent DRL framework that enables cooperative decision-making across UAVs. Simulation results demonstrate that both the single-agent and multi-agent approaches achieve stable training performance, and the proposed method improves the reliable task offloading ratio by up to two times compared to benchmark schemes, while also achieving more efficient resource utilisation in complex SAGIN scenarios.

The novel concept of switch-controlled dynamic metasurface antenna (DMA)-empowered near-field integrated sensing and communication (ISAC) is investigated, where a switch network is incorporated between the DMA and RF chains. By sharing the hardware and spectrum, the proposed DMA-based ISAC transmitter enables simultaneous communication and sensing with a reduced number of RF chains. For the single-user ISAC scenario, a joint microstrip selection and beamforming design is proposed for the system with one communication user and one sensing target. Based on this framework, the sensing signal-to-interference-plus-noise ratio (SINR) maximization problem is formulated by jointly optimizing the digital precoder, radar sensing covariance matrix, DMA weighting matrix, and the DMA microstrip selection matrix, while satisfying the communication SINR of the user. The resultant mixed-integer programming (MIP) problem is solved by a block coordinate descent (BCD)-based penalty dual decomposition (PDD) algorithm to find a high-quality near-optimal solution. Then, the system is extended to the multi-user scenario with the presence of clutters and scatterers. A radar signal-to-clutter-plus-noise ratio (SCNR) maximization problem is formulated, subject to the individual communication SINR constraint for each user. The resultant joint optimization problem is also efficiently solved via the proposed BCD-based PDD algorithm. Simulation results demonstrate that the proposed scheme can achieve a better balance between communication and sensing performance over the benchmark schemes.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has received significant attention as a potential technology for the sixth generation (6G) of wireless network due to its ability to boost signal coverage and enhance system efficiency. In this paper, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimize beamforming and the discrete phase shifts of the STAR-RIS assisted wireless network. In particular, we formulate a discrete optimization problem to maximize the total power transmitted to the ground users. This is achieved by optimizing the beamforming at the base station (BS) and the phase shift of the STAR-RIS under minimal power allocation for each user and the maximum power budget at the BS. Since the addressed problem is NP-hard, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamforming components and discrete phase shifts to search for the near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks in terms of runtime and system power, and highlight its potential for practical deployment when solving medium-to-large-scale networks.

This work proposes a novel quantum-assisted partial sorting algorithm, called multi-minima Dürr–Høyer (MMDH), designed to reduce query complexity in scenarios where only a small subset of elements must be sorted. Empirical results show that MMDH significantly outperforms classical algorithms in these settings, achieving over an order of magnitude reduction in query complexity. The algorithm is applied to signal detection in multiple-input multiple-output systems and is particularly effective when integrated into a newly introduced variable-complexity sphere decoder, called progressive tree expansion (PTE), which inherently benefits from partial sorting. Compared to fixed-complexity sphere decoders (FCSDs), the PTE algorithm substantially reduces the computational complexity, especially at high signal-to-noise ratios (SNRs). When augmented with MMDH, the quantum-assisted PTE decoder achieves near maximum-likelihood error performance while mitigating the query overhead commonly associated with tree-based decoders. In contrast, conventional FCSDs benefit less from MMDH, as they require selection of multiple minima rather than partial ordering, a task where classical methods such as heap-based selection remain competitive. Although MMDH introduces a small failure probability, the resulting error floor stays below practical thresholds in high-SNR regimes.

This paper investigates a drone (aka UAV)-assisted physical layer security framework for space–air–ground integrated networks (SAGINs) in the presence of multiple eavesdroppers. A single full-duplex UAV is deployed to support satellite-to-ground communications by simultaneously relaying desired signals to legitimate users and transmitting artificial noise to degrade the reception quality of eavesdroppers. To enhance secure connectivity, we formulate a max–min secrecy rate optimization problem that jointly considers sub-channel allocation and power distribution. The sub-channel allocation is optimized using a constrained genetic algorithm, which efficiently handles the combinatorial nature of the problem. Additionally, power allocation is optimized through a nested-loop approach, in which the outer loop employs Bayesian optimization to address complex objective functions, while the inner loop makes the allocation tractable using variable substitutions and approximation methods to overcome non-convexity. The simulation results demonstrate that the proposed method outperforms the benchmark schemes in terms of secrecy performance, particularly under stringent resource and security constraints in SAGINs.

Beam alignment for multiple-input and multiple-output fluid antenna systems (MIMO-FAS) is studied, where two-sided beamforming and port activation are optimized without channel estimation to enhance transmission rate. In contrast to conventional position-fixed MIMO setups, MIMO-FAS leverages flexible beamforming to achieve higher gains with a smaller number of antennas. However, realizing these gains typically requires high-complexity channel estimation methods, especially in MIMO scenarios. To overcome this challenge, a channel estimation-free active-sensing framework for beam alignment in MIMO-FAS is proposed, which consists of three components: 1) A new ping-pong transmission protocol is conceived, enabling full-dimensional pilot reception through sequential sub-array activation. 2) Based on this protocol, two learning-based active-sensing algorithms are proposed for full-dimensional beam alignment via online and offline learning, respectively. 3) A greedy-policy-based method is developed to design the port activation matrices and associated beamforming vectors based on the active-sensing results. Numerical results demonstrate that: i) the proposed active-sensing framework can effectively utilize the advantages of FAS over conventional MIMO systems without channel estimations; and ii) the online-learning method enhances generalizability by eliminating the need for extensive centralized offline training, while the offline-learning method ensures robustness and low-complexity beam alignment by leveraging prior knowledge from the training phase.

Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

This contribution focuses on the optimization of bit error rate (BER) performance in fluid antenna (FA)-aided wireless communication systems, which leverage the new degrees of freedom provided by positional flexibility, thus surpassing the limitations of conventional fixed-position antenna (FPA) systems. In this context, a theoretical analysis identifies key metrics for maximum likelihood (ML) and zero-forcing (ZF) detectors, specifically the minimum singular value and effective rank, as determinants of system performance. Then, for single-input single-output (SISO) channels, the optimization problem is formulated as channel gain maximization constrained by the predefined moving region and then solved using a mixed-integer linear programming (MILP) model. Furthermore, for multiple-input multiple-output (MIMO) channels, an alternating optimization (AO) algorithm incorporating the Frank-Wolfe method is proposed to optimize ML and ZF performances, targeting minimum singular value maximization for ML and singular value balancing for ZF, both subject to moving region and antenna spacing constraints. Finally, numerical results exhibit significant performance gains for the FA system over its conventional FPA alternative. In general, these findings highlight the potential of FA systems for addressing ultra-reliable communication challenges in the sixth generation (6G) wireless communications.

Integrated sensing and communication (ISAC) is emerging as a key enabler for spectrum-efficient and hardware-converged wireless networks. However, classical radar systems within ISAC architectures face fundamental limitations under low signal power and high-noise conditions. This paper proposes a novel framework that embeds quantum illumination radar into a base station to simultaneously support full-duplex classical communication and quantum-enhanced target detection. The resulting integrated quantum sensing and classical communication (IQSCC) system is optimized via a sum-rate maximization formulation subject to radar sensing constraints. The non-convex joint optimization of transmit power and beamforming vectors is tackled using the successive convex approximation technique. Furthermore, we derive performance bounds for classical and quantum radar protocols under the statistical detection theory, highlighting the quantum advantage in low signal-to-interference-plus-noise ratio regimes. Simulation results demonstrate that the proposed IQSCC system achieves a higher communication throughput than the conventional ISAC baseline while satisfying the sensing requirement.

Terahertz (THz) frequency band has been widely studied and is recognized as a promising candidate for centimeter-level localization. However, the limited coverage of THz networks may result in localization failures, while a heterogeneous deployment of millimeter-wave (mmWave) and THz radio units (RUs) offers a viable solution to mitigate this issue. This paper presents a theoretical framework for evaluating the performance limits of localization systems in mmWave and THz heterogeneous networks. In this architecture, the mmWave RUs serve as macro base stations (BSs), while the THz RUs function as micro BSs distributed around each mmWave RU. By leveraging the standard tools of stochastic geometry to model the spatial distributions of the RUs and ambient obstacles, the localizability of a target is computed to evaluate the probability of achieving sufficient signal-to-interference-plus-noise ratio for localization in both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. Furthermore, the Cram é r-Rao lower bounds in both LoS and NLoS scenarios are analytically derived to characterize the overall positioning performance. Numerical results demonstrate that the hybrid deployment strategy significantly improves both the network coverage and localization accuracy compared to mmWave-only and THz-only networks.

Edge computing enables distributed inference for computation-intensive applications. However, the autoregressive nature and large model size of large language models (LLMs) pose challenges for their deployment in wireless edge networks. Existing edge inference methods mainly assume an equivalent delay for each token generation step, which fails to capture the dynamic computational and memory overhead incurred during the decoding process. This paper proposes a latency-sensitive wireless edge inference framework for LLMs, where tasks are grouped into multiple batches and processed in parallel by partitioning the LLM into multiple pipeline stages across heterogeneous edge GPUs. An accurate latency model is established, where the latency of each token generation step increases during the autoregressive generation process. Based on this model, the end-to-end inference latency is minimized, which is formulated as a joint optimization problem of bandwidth allocation, model partitioning, and batch scheduling subject to heterogeneous GPU memory constraints. To solve this NP-hard problem with coupled variables, we develop a polynomial-time alternating optimization algorithm that iteratively optimizes model partitioning and batch scheduling via dynamic programming. The closed-form solutions of wireless bandwidth allocation are derived. Extensive simulations show that our approach reduces latency by up to 42.1% versus state-of-the-art baselines across diverse edge scenarios.

This paper examines the power consumption (PC) efficiency of a mixed near- and far-field (MF) simultaneous wireless information and power transfer (SWIPT) system underpinned by a hybrid beamforming (HB)-based modular extra-large multiple-input-multiple output (XL-MIMO) array. Multiple information decoding (ID) and energy harvesting (EH) users are served by multiple constituent subarrays in both the near-field (NF) and far-field (FF) region of the transmit array. A novel decision method is proposed for accurate classification of different field users using Frobenius norm-based frequency correlation of the least square (LS) channel estimates. The NF spatial non-stationarities (SnS) effects entail distinct electromagnetic (EM) visibility regions (VRs), which can be customized to employ strategic activation of the constituent XL-MIMO subarrays. We formulate a two-tier joint optimization problem to minimize the overall PC, considering the power allocation (PA) for both ID and EH users in addition to the subarray activation (SA). This challenging mixed-integer problem is transformed into computationally tractable formulations, accompanied by the development of well-optimized algorithms. Our simulation results demonstrate an overall PC reduction for our proposed PA-SA-HB scheme by up to 93% against the equal PA with full array (FA) and up to 18% with respect to the PA-FA-HB case.

This paper investigates near-field channel effects in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)–assisted uplink system with a multi-antenna base station. The considered system is based on generalized non-orthogonal multiple access (NOMA) with imperfect successive interference cancellation (SIC), encompassing space division multiple access (SDMA) and NOMA with perfect SIC as special cases. For this general setting, we develop sum-rate maximization algorithms with and without quality-of-service (QoS) constraints using an alternating optimization (AO) framework and a penalty-based nonlinear optimization (NLO) method, providing flexible performance–complexity trade-offs. We also derive an upper bound on the achievable sum rate to characterize multiplexing and beamforming gains under both near- and far-field channel conditions. The results show that AO initialized with the proposed NLO solution achieves the best performance with moderate computational complexity, aligning well with the upper-bound behavior. Furthermore, analysis and simulations demonstrate that near-field channels increase the available degrees of freedom and provide enhanced robustness to imperfect SIC and QoS constraints.

With the arrival of sixth-generation communication systems, advanced technologies like reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and non-orthogonal multiple access (NOMA) are poised to drive a broad range of Internet of Things (IoT) applications. Integrating ISAC into multi-input single-output (MISO) networks calls for a reassessment of performance metrics such as outage probability and sensing rate. Furthermore, a critical challenge is managing heterogeneous user deployments and dynamically adapting to varying user locations while minimizing interference. To address these challenges, this work proposes an innovative multi-sector RIS framework. By dividing the RIS into independently controlled sectors, the system dynamically selects the sector closest to each user. For downlink transmission, a dual-function base station (BS) utilizes NOMA to serve the user clusters and transmit a sensing signal. The RIS dynamically selects the sector closest to the close-proximity user based on their location. To support this, the proposed approach employs a nearest-sector selection strategy centered around a reference close-proximity user. Closed-form approximations for the outage probability are then derived, assuming a blocked direct link between the BS and the users. This framework also enables ISAC by transmitting sensing signals within the selected sector, facilitating both user communication and target detection. We characterize the sensing by the sensing rate, with results showing that increasing RIS elements in the multi-sector design enhances the sensing rate. Overall, the proposed system demonstrates superior performance over traditional simultaneously transmitting and reflecting (STAR)RIS configurations and space division multiple access (SDMA) systems. In particular, the proposed system achieves a gain of 8dB, 12dB and 2dB over SDMA, conventional RIS and STAR-RIS systems, respectively.

This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burdens via strategic model splitting, and periodically aggregating client-side and server-side models consistent with the principles of FL. Nevertheless, existing SFL frameworks encounter significant performance degradation arising from data heterogeneity and imbalance, client heterogeneity, as well as constrained wireless resources. To overcome these issues, this paper introduces a novel data distribution deviation-aware split federated learning (DA-SFL) framework. DA-SFL dynamically adjusts aggregation weights according to the deviation of clients’ data distributions from a global distribution, effectively mitigating biases induced by data imbalance and heterogeneity. Furthermore, we theoretically establish the convergence bound of DA-SFL under a non-convex loss function setting, demonstrating that minimizing the data deviation in each training round enhances learning efficacy. Motivated by this, we formulate a mixed-integer nonlinear programming to optimize learning performance under long-term energy constraints. Leveraging the Lyapunov optimization framework, we decompose the problem into a series of tractable subproblems in each learning round, and propose efficient algorithms to find the client scheduling, adaptive cut layer selection, bandwidth allocation, and aggregation weighting policies. Extensive experimental evaluations conducted on Fashion-MNIST, CIFAR-10, and CINIC-10 datasets across diverse scenarios of data heterogeneity and imbalance demonstrate that DA-SFL significantly outperforms baselines regarding test accuracy, time and energy efficiency, while exhibiting notable robustness and scalability.

A reliable log audit system is a fundamental tool for efficient security management and attack detection. Blockchain has emerged as a prominent technology for building log audit systems, thanks to its non-repudiation and immutability properties. However, current blockchain-based solutions are computationally intensive and typically rely on coarse-grained queries, making them impractical for real-world systems. Therefore, we propose a blockchain-based verifiable log audit system, BLAS, which adopts three novel techniques. Firstly, we propose a novel data structure called the Index-Object Merkle Forest (IOMF), which combines modified Merkle Tree data structures and keyword-range bitmap indexes to support efficient log auditing. Secondly, we propose a hierarchical extension of IOMF to create an Authenticated Layered Index Structure (ALIS). ALIS enables fine-grained auditing at the entry level. Finally, we propose two optimization techniques in ALIS to reduce computational and communication costs. The performance evaluation confirms that BLAS is consistently faster than the baseline solutions with similar settings in various experiments, achieving speedups of hundreds to over ten thousand times for the most challenging workloads.

Mobile Edge Generation (MEG) is presented as a distributed framework in which an identical diffusion model (DM) is deployed on both an edge server (ES) and user equipment (UE). In MEG, most computations and generation steps of UEs are offloaded to the ES. However, heterogeneous user preferences cannot be captured by a uniform DM. To address this, a Personalized Mobile Edge Generation (P-MEG) framework is proposed, where a lightweight personalized U Net is trained on the UE in collaboration with the pre-trained DM from the ES. During inference, pre-trained ES features are fused with UE features through scaling coefficients that encode user-specific preferences. The training stability of P MEG and the robustness of feature fusion under noisy wireless channels are theoretically investigated, where bounds are derived on forward and backward feature oscillations, backpropagation gradients, and feature fusion errors in the presence of additive white Gaussian noise (AWGN) noise. These bounds are shown to depend on the fusion scale, and robustness under AWGN follows the same dependence. A multi-U-Net training model with AWGN perturbations is introduced to emulate over-the air training. Inspired by these insights, a constant scaling connection (CSC) method is proposed to stabilize training by exponentially scaling the fusion coefficients, and a random mask training (RMT) strategy is introduced to reduce computational requirements by adjusting transmission ratios of personalized features. Experimental evaluations on MNIST, EMNIST and PACS demonstrate that: 1) P-MEG enables effective personalized image generation, 2) RMT alleviates computational demands with only slight training overhead, and 3) CSC stabilizes feature oscillations under noisy channels, yielding a 1.4-fold acceleration in training.

We consider a reconfigurable intelligent surface (RIS)-assisted extremely large-scale multiple-input multiple-output (XL-MIMO) downlink system, where an XL-MIMO array serves two groups of single-antennas users, namely near-field users (NFUEs) and far-field users (FFUEs). FFUEs are subject to blockage, and their communication is facilitated through the RIS. We consider three precoding schemes at the XL-MIMO array, namely central zero-forcing (CZF), local zero-forcing (LZF) and maximum ratio transmission (MRT). Closed-form expressions for the spectral efficiency (SE) of all users are derived for MRT precoding, while statistical-form expressions are obtained for CZF and LZF processing. A heuristic visibility region (VR) selection algorithm is also introduced to help reduce the computational complexity of the precoding scheme. Furthermore, we devise a two-stage phase shifts design and power control algorithm to maximize the sum of weighted minimum SE of two groups of users with CZF, LZF and MRT precoding schemes. The simulation results indicate that, when equal priority is given to NFUEs and FFUEs, the proposed design improves the sum of the weighted minimum SE by 31.9%, 37.8%, and 119.2% with CZF, LZF, and MRT, respectively, compared to the case with equal power allocation and random phase shifts design. CZF achieves the best performance, while LZF offers comparable results with lower complexity. When prioritizing NFUEs or FFUEs, LZF achieves strong performance for the prioritized group, whereas CZF ensures balanced performance between NFUEs and FFUEs.

Due to the increasing number of users and antennas in extremely large antenna arrays (ELAA) based integrated sensing and communication (ISAC) systems, the complexity of beamforming optimization becomes overwhelming, which impedes real-time and cost-efficient ISAC deployment in practice. Specifically, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a low complexity and transferable (LCT) solution for ISAC beamforming. The proposed LCT beamforming optimization framework includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed LCT framework outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming. Moreover, the LCT framework provides a solution for low-cost beamforming optimization in ISAC systems. The trained RL module can be transferred without retraining when the antenna or user number changes.

A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems. The beamforming optimization problem is first formulated, and it is mathematically proved that the optimal beamforming lies in the subspace spanned by users’ conjugate channel responses. Two challenges are encountered when directly applying deep neural networks (DNNs) for solving the formulated problem, i) both the input and output spaces are infinite-dimensional, which are not compatible with DNNs. The finite-dimensional representations of inputs and outputs are derived to address this challenge. ii) A closed-form loss function is unavailable for training the DNN. To tackle this challenge, two additional DNNs are trained to approximate the operations without closed-form expressions for expediting gradient back-propagation. To improve learning performance and reduce training complexity, the permutation equivariance properties of the mappings to be learned are proved. As a further advance, the DNNs are designed as graph neural networks to leverage the properties. Numerical results demonstrate that: i) the proposed DeepCAPA framework achieves higher spectral efficiency and lower inference complexity compared to existing numerical algorithms, ii) DeepCAPA approaches the performance upper bound of optimizing beamforming in the spatially discrete array systems as the number of antennas in a fixed-sized area tends toward infinity, and iii) DeepCAPA can be well-generalized to different system settings.

wireless communication environment may not experience direct line-of-sight propagation whereas the number of receivers (Rxs) is often limited. We propose the utilization of only non-line-of-sight (NLoS) received signal strength (RSS) measurements observed at a single Rx to locate a target, via a positioning algorithm accounting for data association ambiguity that may occur in a real-world scenario. Considering the stochastic nature of a network geometry, tractable expressions are derived for the probability of acquiring at leastLNLoS RSS measurements during localization. In light of the computational complexity of our solution, we investigate the minimum number of RSS samples required to meet the specified localization accuracy, thereby guiding system design. Furthermore, the probability distribution of the trace of the Cramér-Rao lower bound is obtained analytically, which offers a comprehensive understanding of the fundamental limits of the single-Rx localization scheme without resorting to intensive simulations.

Received signal strength (RSS) has been extensively studied for localization purposes, and the distributed nature of cell-free massive multiple-input multiple-output (CF-mMIMO) systems offers a new synergistic avenue for achieving high-precision localization. In this work, Open RAN and software-defined radio are used to realize the central and distributed units of a CF-mMIMO system to acquire the RSS measurements. By analyzing the experimental data, it is revealed that the RSS measured from the first-order reflection path can yield a sufficiently high signal-to-noise ratio for localization, enabling localization even without line-of-sight (LoS) paths. Inspired by this finding, a hybrid localization scheme, that can attain the best accuracy benchmarked by Cramér-Rao lower bound, is proposed to estimate the target position using both LoS and first-order non-line-of-sight RSS measurements. Furthermore, a theoretical framework is established to assess the fundamental limits of RSS-based localization in CF-mMIMO systems, offering a principled guideline for system designers to deploy and design localization systems in real-world scenarios.

Pinching-antenna systems (PASS) route energy through low-loss dielectric waveguides and radiate via reconfigurable pinching antennas (PAs), enabling large, shapeable apertures with minimal radio chains. We study a near-field multicast downlink network that extends single-waveguide PASS to a coordinated multi-waveguide array and jointly optimizes PA positions and beams. We first develop a cascaded channel that couples in-waveguide and free-space propagation, and pose a worst-case multicast objective under spacing, coupling span, and power constraints. A two-stage co-design then follows. Stage I performs layout planning as a constrained bi-objective placement that maximizes the worst-user signal-to-noise ratio (SNR) while minimizing a wrapped-phase residual; when solved with the non-dominated sorting genetic algorithm (NSGA) II, it yields feasible Pareto layouts. Stage II fixes a knee layout obtained from Stage I and refines the multicast beam via a convex semi-definite relaxation (SDR)-successive convex approximation (SCA) formulation with a feasibility warm start, thereby recovering rank-one beams. Numerical results reveal that over wide ranges of transmit power, coupling span, PA per waveguide, number of waveguides, user count, user range, and base-station height, the proposed design outperforms a single-waveguide PASS and$\boldsymbol {x}$or$\boldsymbol {y}$-aligned uniform linear arrays, delivering higher worst-user rates as well as sharply lowering the per-user rate variance. The study also identifies broad coupling-length ranges where gains saturate and shows that a moderate number of pinches and additional waveguides help improve spatial coverage until a geometry-limited plateau is reached. These effects arise from in-waveguide proximity and lateral phase control, positioning multi-waveguide PASS as a practical, flexible antenna option for next-generation communication.

This paper propose a two-stage quantum neural network (QNN) framework for cell-free multiple-input and multiple-output (MIMO) wireless communication systems. Cell-free MIMO, which has been regarded as a key technology for enhancing the performance of the next-generation wireless communication systems, leverages the collective capability of multiple distributed access points (APs), allowing collaboration between them. However, optimizing cell-free MIMO can pose challenges for centralized optimization schemes. In particular, complexities associated with the joint optimizations of user-transmission assignment and transmission precoding, two factors which are of much importance for determining the quality-of-service, grow with the number of APs and served users. To this end, a unified scheme employing distributed QNNs is used to optimize downlink transmitter-user assignment and transmit precoding with the goal of maximizing the achieved sum rate. Firstly, the cloud processing unit, which holds holistic information about the particular wireless communication network, employs QNN to assign each AP to its designated mobile terminal. Secondly, the edge processing units, which are computed in proximity relative to the AP in order to reduce latency, estimate transmission precoding for their corresponding APs. Moreover, numerical results are presented to showcase the performance of the proposed protocol.

Unlike conventional systems using a fixed-location antenna, the channel capacity of the pinching-antenna system (PASS) is determined by the activated positions of pinching antennas. This article characterizes the capacity region of multiuser PASS, where a single pinched waveguide is deployed to enable both uplink and downlink communications. The capacity region of the uplink channel is first characterized. i) For the single-pinch case, closed-form expressions are derived for the optimal antenna activation position, along with the corresponding capacity region and the achievable data rate regions under time-division multiple access (TDMA) and frequency-division multiple access (FDMA). It is proven that the capacity region of PASS encompasses that of conventional fixed-antenna systems, and that the FDMA rate region contains the TDMA rate region. ii) For the multiple-pinch case, inner and outer bounds on the capacity region are derived using an element-wise alternating antenna position optimization technique and the Cauchy-Schwarz inequality, respectively. The achievable FDMA rate region is also derived using the same optimization framework, while the TDMA rate region is obtained through an antenna position refinement approach. The analysis is then extended to the downlink PASS using the uplink-downlink duality framework. It is proven that the relationships among the downlink capacity and rate regions are consistent with those in the uplink case. Numerical results demonstrate that: i) the derived bounds closely approximate the exact capacity region, ii) PASS yields a significantly enlarged capacity region compared to conventional fixed-antenna systems, and iii) in the multiple-pinch case, TDMA and FDMA are capable of approaching the channel capacity limit.

This paper studies a cell-free massive multiple-input multiple-output (CF-mMIMO) proactive monitoring system in which multiple multi-antenna monitoring nodes (MNs) are assigned to either observe the transmissions from an untrusted transmitter (UT) or to jam the reception at the untrusted receiver (UR). We propose an effective channel state information (CSI) acquisition scheme for the monitoring system. In our approach, the MNs leverage the pilot signals transmitted during the uplink and downlink phases of the untrusted link and estimate the effective channels corresponding to the UT and UR via a minimum mean-squared error (MMSE) estimation scheme. We derive new spectral efficiency (SE) expressions for the untrusted link and the monitoring system. For the latter, the SE is derived for two CSI availability cases at the central processing unit (CPU); namely case-1: imperfect CSI knowledge at both MNs and CPU, case-2: imperfect CSI knowledge at the MNs and no CSI knowledge at the CPU. To improve the monitoring performance, we propose a novel joint mode assignment and jamming power control optimization method to maximize the monitoring success probability (MSP) based on the Bayesian optimization framework. Numerical results show that (a) our CF-mMIMO proactive monitoring system relying on the proposed CSI acquisition and optimization approach significantly outperforms the considered benchmarks; (b) the MSP performance of our CF-mMIMO proactive monitoring system is greater than 0.8, regardless of the number of antennas at the untrusted nodes or the precoding scheme for the untrusted transmission link.

Integrated sensing and communication (ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by malicious foes. In this paper, we propose a new wireless proactive monitoring paradigm, where a legitimate monitor intercepts a suspicious communication link while performing cognitive jamming to enhance the monitoring success probability (MSP) and simultaneously safeguard the target. To this end, we derive closed-form expressions of the signal-to-interference-plus-noise-ratio (SINR) at the user (UE), sensing access points (S-APs), and an approximating expression of the SINR at the proactive monitor. Moreover, we propose an optimization technique under which the legitimate monitor minimizes the success detection probability (SDP) of the legitimate target, by optimizing the jamming power allocation over both communication and sensing channels subject to total power constraints and monitoring performance requirement. To enhance the monitor’s longevity and reduce the risk of detection by malicious ISAC systems, we further propose an adaptive power allocation scheme aimed at minimizing the total transmit power at the monitor while meeting a pre-selected sensing SINR threshold and ensuring successful monitoring. Our numerical results show that the proposed algorithm significantly compromises the sensing and communication performance of malicious ISAC.

A novel mobile edge generation (MEG) framework is proposed to support latency-sensitive generation tasks in the social-aware device-to-device (D2D) underlaying cellular network. Within this framework, user devices (UDs) and base station (BS) are organized into socially cohesive communities based on content preference and spatial proximity, enabling cooperative generation tasks via both cellular and intra-community D2D communications. A joint seed-and-content based BS-D2D (JSCB) transmission protocol is proposed to dynamically orchestrate the transmission mode between seed acquisition with local generation and direct content sharing across multiple consecutive task rounds, incorporating the spillover mechanism for handling overdue transmissions. Based on this protocol, an average task delay minimization problem is formulated to jointly optimize the UD association between cellular and D2D communication, transmission mode, D2D pairing, and BS-side beamforming. To efficiently solve the hybrid and temporally coupled problem, a joint matching and proximal policy optimization (JMPPO) algorithm is developed, where the discrete and continuous actions are decoupled with specialized modules though a hierarchical deep reinforcement learning and matching design. Numerical results validate that 1) the JSCB protocol reduces delay through adaptive transmission scheduling and cellular/D2D coordination; 2) the JMPPO algorithm outperforms both learning-based and traditional baselines in terms of average delay under the spillover and hybrid action scenarios; 3) the proposed schemes demonstrate robustness across diverse network and system conditions.

A continuous-aperture array (CAPA)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. Within this framework, continuous operator-based signal models are employed to describe the sensing and communication processes. The performance of communication and sensing is analyzed using two information-theoretic metrics: the communication rate (CR) and the sensing rate (SR). 1) For downlink ISAC, three continuous beamforming designs are proposed: i) the communications-centric (C-C) design that maximizes the CR, ii) the sensing-centric (S-C) design that maximizes the SR, and iii) the Pareto-optimal design that characterizes the Pareto boundary of the CR-SR region. A low-complexity signal subspace-based approach is proposed to derive the closed-form optimal beamformers for the considered designs. On this basis, closed-form expressions are derived for the achievable CRs and SRs, and the downlink rate region achieved by CAPAs is characterized. 2) For uplink ISAC, the C-C and S-C successive interference cancellation-based methods are proposed to manage inter-functionality interference. Using the subspace approach closed-form expressions for the optimal detectors as well as the achievable CRs and SRs are derived. The uplink SR-CR region is characterized based on the time-sharing technique. Numerical results demonstrate that, for both downlink and uplink, CAPA-based ISAC achieves higher CRs and SRs as well as larger CR-SR regions compared to conventional spatially discrete array-based ISAC.

Suffering from serious rate degradation, how to improve transmission rate with low latency is a challenging issue in ultra-reliable and low-latency communications (URLLC), especially when there is no enough blocklength for data transmission. To handle this issue, we propose to integrate the reconfigurable intelligent surface (RIS) and superimposed pilot (SP) into massive multiple-input multiple-output (mMIMO) systems, where the SP ensures latency by simultaneously sending pilot and data while the RIS improves high transmission rate by reflecting the SP signals. Practically, we derive the finite blocklength ergodic achievable rate lower bound in closed form under imperfect channel estimation and pilot interference removal. Then, we maximize the weighted sum rate of all the users by jointly designing the power control of SP at each user and the phase shifts at the RIS. Due to the highly coupled variables, we first decompose the original problem into the phase shift design subproblem and the power control design subproblem, which are resolved by a genetic algorithm (GA) and an iterative algorithm based on geometric programming (GP). Then, a block coordinate descent algorithm is proposed. Correspondingly, the complexity and convergence of the proposed algorithms are analyzed. Finally, our numerical results demonstrate that the joint design scheme can bring effective rate improvement in stringent latency constraints.

With the proliferation of large language models (LLMs), cloud-based LLM serving mechanisms may cause network congestion and high serving delay. Edge computing offers a solution to alleviate backhaul pressure and reduce serving delay by deploying LLMs on edge servers and providing LLM inference services in users’ proximity. However, user accuracy requirements vary over time, and mismatches between these requirements and the deployed LLMs at the edge may lead to inefficient resource usage and increased serving delay. To address this, we formulate a joint LLM caching, inference task scheduling, and network resource allocation problem to minimize LLM serving delay under unknown time-varying user accuracy requirements. To solve the problem, we first derive closed-form solutions for optimal computation and communication resource allocation under any LLM caching and task scheduling policies. Then, we employ an improved branch-and-bound algorithm to obtain optimal task scheduling policies under any LLM caching strategies. Finally, we propose an improved double deep Q-network (DDQN)-based algorithm to determine the LLM caching decisions. It incorporates a state coding and action aggregation (SCAA) mechanism within the deep neural networks (DNNs) of the traditional DDQN. The SCAA-DNNs involve an input-layer gating mechanism to encode users’ request states for LLMs and a two-layer output architecture that dynamically aggregates LLM caching actions to generate the corresponding state-action values, thereby improving learning efficiency and accelerating convergence in large discrete action spaces. Experimental results show that the proposed scheme could rapidly converge and reduce average user delay by up to 20.8% compared to benchmarks.

Fluid antenna systems (FAS) utilize position reconfigurability to improve spatial diversity in wireless communications. However, a rigorous framework for error probability analysis under spatially correlated channels remains absent. This paper fills this gap by deriving a closed-form asymptotic expression for the symbol error rate (SER). This mathematical expression establishes the fundamental scaling law between the error performance and the spatial correlation matrix. A key insight from our analysis is that the achievable diversity gain depends entirely on the effective rank of the spatial channel, rather than the total number of antenna ports. To quantify this effective rank, we propose a dual approach: a theoretical derivation and a geometry-based algorithm. Both methods rigorously prove that the effective rank converges to a fundamental limit of$2W+1$, where$W$denotes the normalized aperture width. Specifically, the geometry-based algorithm extracts distinct performance thresholds from the eigenvalue spectrum of the channel. These thresholds perfectly match the derived theoretical limit. Furthermore, the proposed effective rank model demonstrates higher accuracy than existing approaches in the literature. Based on this robust framework, we offer a complete characterization of diversity gains and coding gains. The analytical results reveal a definitive design principle: enlarging the physical aperture increases the effective rank and drives performance improvements, whereas simply increasing port density within a fixed aperture yields diminishing returns.

Quantum sensing networks (QSNs) are expected to play a critical role in quantum networks by achieving measurement precision unattainable with classical methods, leveraging quantum properties such as superposition and entanglement. Distributed quantum sensing, a key application of QSNs, can reach Heisenberg-limited precision scaling with the number of sensors involved. However, practical implementation faces significant challenges due to noise effects, complicating the optimal selection of sensor configurations. In this paper, we propose applying a variational quantum algorithm (VQA) combined with a genetic algorithm to efficiently mitigate noise in quantum sensing protocols and to identify high-quality sensor configurations. Performance analysis demonstrates that our approach outperforms traditional sensor configuration methods in single- and multi-parameter sensing scenarios under dephasing and amplitude damping noises, significantly improving quantum sensing accuracy and scalability.

In this paper, we explore a 6G-enabled space-air-ground integrated network (SAGIN) framework that integrates ground communication hubs (CHs), a UAV with mobile edge computing (MEC) capabilities, and a constellation of low Earth orbit (LEO) nano-satellites. We formulate a joint optimization problem for UAV trajectory, task offloading, and satellite load balancing, modeled as a mixed-integer nonlinear programming (MINLP) problem. To solve this, we propose a quantum-enhanced advantage actor-critic (QEA2C) reinforcement learning algorithm that employs quantum neural networks and two quantum state encoding methods: amplitude encoding (AE) and higher-order encoding (HOE). Simulation results show that HOE achieves superior performance in terms of convergence speed, cumulative rewards, and learning efficiency, successfully serving all CHs with a well-optimized UAV trajectory. Meanwhile, AE achieves better cost minimization with lower resource consumption, making it a more practical option when computational efficiency is a priority. Moreover, these results highlight the trade-offs between learning performance and cost efficiency in quantum-enhanced decision-making for managing 6G-enabled SAGINs.

In this work, the benefits of employing quantum neural networks (QNNs) in reinforcement learning (RL)-based methods used in vehicular networks are explored. We substitute the classical-bit-based neural networks (NNs) in the multi-agent deep RL (MADRL) with QNNs and propose a QNN-based quantum MADRL (QMADRL) framework to solve a resource allocation (RA) problem in a cellular-vehicle-to-everything (C-V2X) network. The objective of the optimisation is to minimise the age of information (AoI) for vehicle-to-infrastructure (V2I) communications, maximise the delivery probability of the cooperative awareness messages (CAMs) for the vehicle-to-vehicle (V2V) communications, and jointly minimise the power and energy consumption to promote green communication practices. Compared to classical MADRL methods, the proposed QMADRL framework delivers substantially faster convergence while achieving comparable performance after convergence.

This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44 % compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches.

This work proposes the integration of the holographic active reconfigurable intelligent surface (HARIS) into a multi-user uplink near-field-driven wireless communication system. In order to provide efficient resource utilization, a sumrate maximization problem is formulated, where the equalizer design, the power allocation at each user, and the HARIS phase profile are jointly optimized under the strict constraint of QoS requirement and limited power budget at each user and HARIS. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR) to simultaneously optimize the equalizer at the BS, beamforming at the HARIS, and power allocation at each user. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum quality of service (QoS) constraint corresponding to each user, maximum receive power at the base station (BS), and maximum amplification factor.

In mobile wireless networks, data heterogeneity and resource constraints cause performance degradation in machine learning tasks on edge clients. To alleviate these issues, we propose a novel deviation-aware split federated learning (DevSFL) framework, which adopts an adaptive aggregation weight determination method for mitigating the effects of data heterogeneity across local datasets and improving overall learning performance. Leveraging Lyapunov optimization, we formulate a comprehensive optimization problem including client scheduling, cut layer selection, bandwidth allocation, and weight decisionmaking to enhance resource utilization and energy efficiency. To tackle this problem, we employ a sample average approximation based algorithm and a dichotomy method for optimizing cut layer selection and bandwidth allocation policies, respectively. Furthermore, a set expansion algorithm is employed to find the optimal client subset. Additionally, we introduce a deviationaware algorithm specifically designed to refine the weighting policy. Comparative analysis with benchmark schemes reveals that our proposed DevSFL framework not only achieves higher accuracy within fewer rounds but also significantly reduces the time required to reach a predefined accuracy level, thereby demonstrating the effectiveness of our proposed algorithms.

To reduce the serving delay of large language model (LLM)-based applications, the edge-based LLM serving mechanism offers a promising solution by caching LLMs at the edge to provide LLM inference services closer to users. Motivated by this, we propose an edge-based LLM caching and inference framework to support low-delay LLM-based services. Based on the framework, we formulate a joint LLM caching, inference task scheduling, and computation resource allocation optimization problem to minimize LLM serving delay, where time-varying LLM popularity is considered. Given an LLM caching policy, we first obtain the optimal solution for the computation resource allocation and task scheduling by using traditional optimization methods. Then, we propose an improved double deep Q-network (IDDQN) algorithm that effectively learns the optimal LLM caching strategy under unknown LLM popularity. The IDDQN algorithm integrates a state coding and action aggregation (SCAA) mechanism in the deep neural network structure, enabling it to efficiently capture users' preferences for LLMs and mitigate the slow convergence issues due to the large action space. Simulation results indicate that the proposed scheme achieves both lower average user delay and faster convergence than other benchmarks.

Beamforming optimization in 6G networks is essential for ensuring energy-efficient and interference-aware transmission, where ultra-reliable low-latency communication (URLLC) and scalable antenna technologies play a key role. Classical optimization methods face scalability challenges, making real-time beamforming infeasible. This paper applies the quantum approximate optimization algorithm (QAOA) to minimize transmit power while ensuring signal quality constraints. The problem is formulated as a quadratic unconstrained binary optimization (QUBO) and solved using quantum simulation. Simulation results demonstrate that QAOA achieves lower transmit power compared to classical solvers, with faster convergence and improved efficiency. These findings suggest that quantum computing can significantly enhance beamforming optimization, paving the way for its integration into future wireless networks.

Joint unicast and multicast transmissions are becoming increasingly important in practical wireless systems, such as Internet of Things networks. This paper investigates a cell-free massive multiple-input multiple-output system that simultaneously supports both transmission types, with multicast serving multiple groups. Exact closed-form expressions for the achievable downlink spectral efficiency (SE) of both unicast and multicast users are derived for zero-forcing and maximum ratio precoding designs. Accordingly, a weighted sum SE (SSE) maximization problem is formulated to jointly optimize the access point (AP) selection and power allocation. The optimization framework accounts for practical constraints, including the maximum transmit power per AP, fronthaul capacity limitations between APs and the central processing unit, and quality-of-service requirements for all users. The resulting non-convex optimization problem is reformulated into a tractable structure, and an accelerated projected gradient (APG)-based algorithm is developed to efficiently obtain near-optimal solutions. As a performance benchmark, a successive convex approximation (SCA)-based algorithm is also implemented. Simulation results demonstrate that the proposed joint optimization approach significantly enhances the SSE across various system setups and precoding strategies. In particular, the APG-based algorithm achieves substantial complexity reduction while maintaining competitive performance, making it well-suited for large-scale practical deployments.

This paper investigates a two-user downlink system for integrated sensing and communication (ISAC) in which the two users deploy a fluid antenna system (FAS) and adopt the non-orthogonal multiple access (NOMA) strategy. Specifically, the integrated sensing and backscatter communication (ISABC) model is considered, where a dual-functional base station (BS) serves to communicate the two users and sense a tag’s surrounding. In contrast to conventional ISAC, the backscattering tag reflects the signals transmitted by the BS to the NOMA users and enhances their communication performance. Furthermore, the BS extracts environmental information from the same backscatter signal in the sensing stage. Firstly, we derive closed-form expressions for both the cumulative distribution function (CDF) and probability density function (PDF) of the equivalent channel at the users utilizing the moment matching method and the Gaussian copula. Then in the communication stage, we obtain closed-form expressions for both the outage probability and for the corresponding asymptotic expressions in the high signal-to-noise ratio (SNR) regime. Moreover, using numerical integration techniques such as the Gauss-Laguerre quadrature (GLQ), we have series-form expressions for the user ergodic communication rates (ECRs). In addition, we get a closed-form expression for the ergodic sensing rate (ESR) using the Cramér-Rao lower bound (CRLB). Finally, the accuracy of our analytical results is validated numerically, and we confirm the superiority of employing FAS over traditional fixed-position antenna systems in both ISAC and ISABC.

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.

Intelligent radios play a pivotal role in optimizing communication resources for both commercial and military applications. Automatic signal identification (ASI) serves as a crucial component for intelligent radios, with likelihood-based and feature-based ASI algorithms being conventional approaches. Recent studies have explored the integration of machine learning (ML) algorithms for ASI, revealing their enhanced resilience to channel distortions compared to traditional methods. This article proposes the application of an extreme learning machine (ELM), a type of the ML algorithm, for the identification of cellular signals based on over-the-air measurements of power spectral density (PSD). The proposed ELM undergoes evaluation using two distinct datasets of PSDs to assess identification accuracy, with the first dataset utilized for hyperparameter optimization and the second unseen dataset employed to evaluate robustness and generality. The experimental results showcase improved performance in both accuracy and training complexity compared to recent work in the literature.

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided downlink network for efficient near-field communication. We formulate a sum-rate maximization problem that jointly optimizes the transmit beamforming at the base station (BS), the common rate of each receiving internet of things (IoT) node, and beamforming at the HRIS transmission design to ensure a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the BS. Since the optimization problem is non-convex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that efficiently solves the joint optimization problem utilizing analytical tools such as the successive convex approximation (SCA). Various numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as reflecting elements, minimum QoS constraint, transmit power budget, and number of IoT nodes. The dominance of RSMA over the counterpart, non-orthogonal multiple access (NOMA), is also demonstrated. It is shown that the use of RSMA can achieve up to 96% higher performance compared to NOMA. It is also highlighted that with near-field assumptions, the average sum rate increases around 69% compared to hybrid 66% and far-field model 64%.

Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging.

This work proposes a novel semi-non-orthogonal multiple access (NOMA) and data transmission technique, called Semi-NOMA, to enhance the spectrum utilization of aerial simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided symbiotic networks without using successive interference cancellation approaches as classical NOMA. In particular, the proposed scheme is investigated with active and passive STAR-RIS models combined with infinite blocklength (IBL) and finite blocklength (FBL) regimes under discrete phase-shift alignments. For IBL scenarios, the ergodic capacity and outage probability are derived under both approximation and asymptotic frameworks. Besides, a joint optimization problem of the power allocation factor and energy splitting coefficient is also formulated to maximize the ergodic sum capacity (ESC), where closed-form solutions are derived for both active and passive STAR-RIS models. For FBL scenarios, not only the approximation and asymptotic frameworks are derived for the average achievable rate and block-error rate, but also an approximated convex form is derived for a non-convexity optimization problem of min-max blocklength. Numerical results corroborate the efficacy of the proposed Semi-NOMA over the baseline schemes, the developed mathematical frameworks, and the solutions of the ESC maximization and min-max blocklength.

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach. Unlike traditional optimization approaches that suffer from exponential complexity growth, our DRL-based method achieves real-time decision-making with superior scalability for complex multi-target scenarios while maintaining computational efficiency.

Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FASs) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study an FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals from the primary user (PU). We aim to maximize the detection probability under the constraints of the false alarm probability and the received beamforming of the SU. To address this problem, we first derive a closed-form expression for the optimal detection threshold and reformulate the problem to find its solution. Then, an alternating optimization (AO) scheme is proposed to decompose the problem into several subproblems, addressing both the received beamforming and the antenna positions at the SU. The beamforming subproblem is addressed using a closed-form solution, while the fluid antenna positions are solved by successive convex approximation (SCA). Simulation results reveal that the proposed algorithm provides significant improvements over traditional fixed-position antenna (FPA) schemes in terms of spectrum sensing performance.

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas’ positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is nonconvex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas’ positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes.

In the shift toward the quantum computing era, the foundational principles of classical cybersecurity, particularly in the realm of cryptographic algorithms, are facing unprecedented challenges. This demands comprehensive reevaluation and redesign of cryptographic infrastructures to withstand quantum adversarial attacks. With the emergence of the quantum Internet, a new approach to secure communication is possible, utilizing quantum properties that have no counterpart in classical systems. As the quantum Internet facilitates the exchange of quantum information, data publication protocols become essential in anonymizing and protecting privacy-sensitive data in quantum communication networks. This paper proposes two controlled quantum anonymous communication (QAC) protocols for publishing classical and quantum information on an Internet server (IS) with the assistance of a communication service provider. The first protocol allows for the controlled publication of classical information without revealing the publisher’s identity such that an adversary, even with access to all network resources, cannot trace the publication source—i.e., achieving perfect untraceability. The second protocol enables anonymous publication of quantum information on an IS in a controlled and untraceable manner. These protocols serve as essential building blocks for advancing the quantum Internet, which has the potential to transform communication and information exchange methods. We provide a detailed anonymity analysis of these QAC protocols for data publication, ensuring that the published symbol or qudit information remains untraceable to its publisher. Moreover, the performance analysis in terms of publication error probability, fidelity, and degree of anonymity in noisy environments demonstrates the robustness of the protocols against noise and adversarial attacks.

Efficient and accurate channel estimation with low pilot overhead is essential for massive multiple-input multiple-output (MIMO) wireless communication systems to achieve high spectral and energy efficiency. This work proposes a novel variational inference method for channel estimation by utilizing the generative diffusion model as a prior. Specifically, we first train a generative diffusion model to learn the score, i.e., the gradient of the log-prior distribution, of MIMO channels to serve as a prior in the channel estimation process. The training process is unsupervised and does not rely on specific pilot structures and signal-to-noise ratios (SNRs). Thus, the learned prior is generalizable and can be directly used for channel estimation under different pilot signals and SNRs without requiring re-training. Then, we propose a variational inference method to infer the posterior distribution of the MIMO channel under given pilots and received measurements by incorporating the learned prior. Finally, we estimate the MIMO channels by sampling from the derived posterior distribution. Our simulations under various wireless propagation environments and antenna architectures demonstrate that the proposed approach achieves over 5 dB reduction in normalized mean square error and faster channel recovery compared to state-of-the-art channel estimators. Additionally, the proposed approach exhibits robust estimation performance when the test channel distribution shifts from the training distribution, even outperforming the benchmarks without distribution shifts.

Owing to the heterogeneous computation and communication capabilities among clients, the synchronous model aggregation in wireless federated learning (FL) is susceptible to the straggler effect and exhibits low learning efficiency, while asynchronous aggregation encounters delayed gradients that lead to convergence errors and learning performance degradation. To address these obstacles, this work proposes an adaptive semi-asynchronous FL (ASAFL) approach to incorporate the strengths of synchronous and asynchronous FL while mitigating their inherent drawbacks. Specifically, the edge server dynamically adjusts the synchronous degree, i.e., the number of local gradients aggregated in each round, to strike a balance between learning latency and accuracy. Recognizing that data heterogeneity among clients may induce biased global model updating, we propose calibrating the global update by leveraging historical gradients received at the edge server from clients. Following that, we theoretically investigate the impact of synchronous degrees in different rounds on the convergence bound of ASAFL. The results imply that allocating more learning time to the later learning stages to increase the synchronous degree contributes to better learning performance. Based on this, we develop an adaptive synchronous degree control and resource allocation algorithm to enhance the learning performance of FL while adhering to the overall learning latency and wireless resources constraint. Numerical results on the MNIST and CIFAR-10 datasets demonstrate that the proposed approach is capable of attaining faster convergence speed and higher learning accuracy compared to the benchmark FL algorithms.

Integrated sensing and communication (ISAC) technology has been explored as a potential advancement for future wireless networks, striving to effectively use spectral resources for both communication and sensing. The integration of reconfigurable intelligent surfaces (RIS) with ISAC further enhances this capability by optimizing the propagation environment, thereby improving both the sensing accuracy and communication quality. Within this domain, accurate channel estimation is crucial to ensure a reliable deployment. Traditional deep learning (DL) approaches, while effective, can impose performance limitations in modeling the complex dynamics of wireless channels. This paper proposes a novel application of conditional generative adversarial networks (CGANs) to solve the channel estimation problem of an RIS-assisted ISAC system. The CGAN framework adversarially trains two DL networks, enabling the generator network to not only learn the mapping relationship from observed data to real channel conditions but also to improve its output based on the discriminator network feedback, thus effectively optimizing the training process and estimation accuracy. The numerical simulations demonstrate that the proposed CGAN-based method improves the estimation performance effectively compared to conventional DL techniques. The results highlight the CGAN’s potential to revolutionize channel estimation, paving the way for more accurate and reliable ISAC deployments.

In this paper, we present the strengths of WiFi-enhanced SLAM over traditional SLAM systems. We minimize the Root Mean Square Error (RMSE) in localization and mapping by incorporating WiFi signal information. The work elegantly embeds WiFi signals in the well-studied SLAM optimization architecture without degrading the efficiency on the computational side, providing improved accuracy and reliability. We utilize a deep neural network (DNN) to learn the optimal fusion of WiFi and traditional sensors, which enhances system performance significantly. The presented LoC-WiFi-SLAM system is of moderate computational as well as spatial complexity. Simulation results show that the Loc-WiFi-SLAM system is able to use available WiFi infrastructure to complement traditional sensors, thus achieving an improvement in mapping and location accuracy. It not only increases localization accuracy but also makes it suitable for robotic systems, thus providing a robust and scalable solution for environments.

Fluid antenna system (FAS) is an emerging technology that uses the new form of shape- and position-reconfigurable antennas to empower the physical layer for wireless communications. Prior studies on FAS were however limited to narrowband channels. Motivated by this, this paper addresses the integration of FAS in the fifth generation (5G) orthogonal frequency division multiplexing (OFDM) framework to address the challenges posed by wideband communications. We propose the framework of the wideband FAS-OFDM system that includes a novel port selection matrix. Then we derive the achievable rate expression and design the adaptive modulation and coding (AMC) scheme based on the rate. Extensive link-level simulation results demonstrate striking improvements of FAS in the wideband channels, underscoring the potential of FAS in future wireless communications.

The learning of fundamental quantum properties—namely coherence, discord, and entanglement—benchmarks the security, computational, and metrological capability of noisy intermediate-scale quantum (NISQ) communication, computing, and sensing networks. The current learning techniques vary widely for these fundamental quantum properties, including standard tomographic procedures that involve exhaustive optimization. Fortunately, the fundamentally distinct quantum properties feature an intricate connection. In this paper, we put forth the concept of universal quantum witness machines (UQWMs) to develop a unified framework for quantum property learning (QPL) of a quantum system. We first formulate the certification and quantification of quantum properties based on quantum witnesses. The witness-based certification method is experimentally accessible and resource-efficient but lacks reliability and generality. To universalize the scope and circumvent the unreliability, we transform the certification task into a classification task by employing UQWMs with classical machine learning to construct quantum property classifiers. This formalism offers a unifying perspective on the certification, quantification, and classification of these enigmatically linked fundamental quantum properties. To demonstrate our UQWM approach, we provide a comparative numerical analysis of quantum property quantification with quantum witnesses and classification performance analysis of quantum property classification with convolutional neural networks, specifically for$4 \times 4$quantum systems.

The emerging quantum technology has highlighted the necessity for secure and efficient secret sharing in quantum networks. In this paper, we introduce a verifiable multiparty counterfactual quantum secret sharing (QSS) protocol, enhancing security and efficiency. This QSS protocol utilizes a low-depth quantum circuit to encrypt and decrypt information, which comprises a unitary operator constructed using a preshared secret key. To ensure the robustness and verifiability of the shared secret key, the protocol imposes constraints on the participants with the Chinese remainder theorem. The most significant advantage of our proposed QSS protocol is incorporating counterfactual communication, which considerably enhances the scheme’s security by enabling exchange-free information sharing among participants, thereby minimizing the risk of eavesdropping or intercept-and-resend attacks. Furthermore, we incorporate a weighted-threshold mechanism that provides flexibility, enabling diverse use cases to design security protocols for quantum networks. The security analysis of the counterfactual QSS protocol and its implementation on IBM Quantum computers reveals strong resilience to internal and external attacks, along with high efficiency and robustness, making it effective for quantum encryption in the noisy intermediate-scale quantum era.

Counterfactual quantum communication enables communication between remote parties without transmitting any information-carrying particle. In this paper, we propose four protocols for secure quantum communication networks utilizing such communication. The first protocol, counterfactual quantum secure direct communication (CQSDC), enables a sender to securely and counterfactually communicate a secret message. The second protocol, counterfactual quantum secure dialogue (CQSD), allows legitimate parties to transmit secret messages in each direction simultaneously, securely and counterfactually. The third protocol, counterfactual controlled quantum teleportation (CCQT), facilitates a sender to counterfactually teleport a quantum state to a receiver under the supervision of a controller. Finally, the fourth protocol, counterfactual quantum private comparison (CQPC), capacitates a third party to compare the private states of the end parties without the actual knowledge of the counterfactually transmitted states. We devise the CQSDC and CQSD protocols by exploiting the counterfactual Swap, dual chained quantum Zeno (CQZ), and distributed controlled NOT gates. For CCQT and CQPC protocols, we utilize CQZ gates with a horizontally polarized photon input. We show that the security of CQSDC and CQSD relies on counterfactual entanglement swapping, while that of CCQT and CQPC depends on establishing secure counterfactual communication channels and security validation with decoy particles, respectively.

The core idea of holographic MIMO (HMIMO) is to densely deploy numerous antenna elements within a given aperture size. However, with the denser distribution of antenna elements, stronger mutual coupling effects would kick in among antenna elements, which would eventually affect the communication performance. Meanwhile, as the holographic array usually has large physical size, the possibility of near-field communication increases. This paper investigates a near-field multi-user downlink HMIMO system and characterizes the spectral efficiency (SE) under the mutual coupling effect over Ricean fading channels. Both perfect and imperfect channel state information (CSI) scenarios are considered. (i) For the perfect CSI case, the mutual coupling and radiation efficiency model are first established. Then, a closed-form SE expression is derived under maximum ratio transmission (MRT). By comparing the SE between the cases with and without mutual coupling, it is unveiled that the system SE with mutual coupling might outperform that without mutual coupling in the low transmit power regime for a given aperture size. Moreover, it is also unveiled that the inter-user interference cannot be eliminated unless the physical size of the array increases to infinity. Fortunately, the additional distance term in the near-field channel can be exploited for the inter-user interference mitigation, especially for the worst case, where the users’ angular positions overlap to a great extent. (ii) For the imperfect CSI case, the channel estimation error is considered for the derivation of the closed-form SE under MRT. It shows that in the low transmit power regime, the system SE can be enhanced by increasing the pilot power and the antenna element density, the latter of which will lead to severe mutual coupling. In the high transmit power regime, increasing the pilot power has a limited effect on improving the system SE. However, increasing the antenna element density remains highly beneficial for enhancing the system SE. Finally, both analytical and simulation results confirm that reducing the antenna spacing will be accompanied by significant mutual coupling effects, which may potentially enhance the system SE. However, this enhancement is ultimately limited by the radiation efficiency of the antennas and the physical size of the array.

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive Cram´er-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC).

The emerging field of federated learning (FL) holds significant promise for advancing edge intelligence while preserving data privacy. However, as FL systems scale or become more heterogeneous, challenges such as spectrum scarcity and the straggler problem arise. To address these issues, this paper proposes SA-AirFed, a semi-asynchronous FL architecture compatible with Over-the-Air Computation (AirComp). We develop an efficient scheduling scheme that meets AirComp’s requirements and analyze the factors affecting convergence under the Lipschitz-Smooth condition. Building on insights from the convergence analysis, we design an adaptive algorithm that mitigates staleness from semi-asynchronous aggregation and noise from AirComp by dynamically adjusting aggregation weights, formulated as a convex quadratic programming problem. Experimental results on MNIST and CIFAR-10 demonstrate that SA-AirFed significantly reduces wall-clock training time while achieving greater robustness compared to baseline models.

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments.

We propose a distributed implementation of integrated sensing and communication (ISAC) underpinned by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations’ requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via a successive convex approximation approach. We further propose a low-complexity design, where the AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of 100% for different network setups with a satisfactory fairness among all UEs.

Fluid antenna system (FAS) is gaining attention as an innovative technology for boosting diversity and multiplexing gains. As a key innovation, it presents the possibility to overcome interference by position reconfigurability on one radio frequency (RF) chain, giving rise to the concept of fluid antenna multiple access (FAMA). While FAMA is originally designed to deal with interference mainly by position change and treat interference as noise, this is not rate optimal, especially when suffering from a strong interference channel (IC) where all positions have strong interference. To tackle this, this paper considers a two-user strong IC where FAMA is used in conjunction with simultaneous non-unique decoding (SND). Specifically, we analyze the key statistics for the signal-to-noise ratio (SNR) and interference-to-noise ratio (INR) for a canonical two-user IC setup, and subsequently derive the delay outage rate (DOR), outage probability (OP) and ergodic capacity (EC) of the FAMA-IC. Our numerical results illustrate huge benefits of FAMA with SND over traditional fixed-position antenna systems (TAS) with SND in the fading IC.

Fluid antenna enables position reconfigurability that gives transceiver access to a high-resolution spatial signal and the ability to avoid interference through the ups and downs of fading channels. Previous studies investigated this fluid antenna multiple access (FAMA) approach in a single-cell setup only. In this paper, we consider a cell-free network architecture in which users are associated with the nearest base stations (BSs) and all users share the same physical channel. Each BS has multiple fixed antennas that employ maximum ratio transmission (MRT) to beam to its associated users while each user relies on its fluid antenna system (FAS) on one radio frequency (RF) chain to overcome the inter-user interference. Our aim is to analyze the outage probability performance of such cell-free FAMA network when both large-and small-scale fading effects are considered. To do so, we derive the distribution of the received magnitude for a typical user and then the interference distribution under both fast and slow port switching techniques. The outage probability is finally obtained in integral form in each case. Numerical results demonstrate that in an interference-limited situation, although fast port switching is typically understood as the superior method for FAMA, slow port switching emerges as a more effective solution when there is a large antenna array at the BS. Moreover, it is revealed that FAS at each user can serve to greatly reduce the burden of BS in terms of both antenna costs and CSI estimation overhead, thereby enhancing the scalability of cell-free networks.

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system’s achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small.

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS.

As the complexity of deep neural networks escalates, traditional federated learning (FL) frameworks increasingly struggle since the training overhead of the full model is costly for resource-limited clients. In addition, the class imbalance among local datasets and client heterogeneity may lead to significant deterioration in learning performance. To address these challenges, we first propose a novel wireless split federated learning (SFL) framework to enhance learning efficiency and performance in resource-constrained networks, which adaptively splits the global model between the clients and server to alleviate the computation burden for clients. Then, we theoretically analyze how the client sampling and wireless network parameters impact on the convergence bound. Based on the analysis, we identify the extent of class imbalance that significantly impacts learning performance. Inspired by this, we formulate an optimization problem to strike a balance between latency and performance by jointly optimizing the client selection, model splitting, and bandwidth allocation policies. To solve this problem, we introduce a latency and class imbalance-aware double greedy algorithm to obtain client scheduling policy. Additionally, bisection-enabled optimal bandwidth allocation and model splitting algorithms are developed to adaptively determine bandwidth allocation and model splitting policies, respectively. Extensive experimental results demonstrate that our approach significantly reduces latency and enhances learning performance.

This work proposes an adaptive semi-asynchronous federated learning (FL) approach, namely ASAFL, to incorporate the strengths of synchronous and asynchronous FL while mitigating their inherent drawbacks. Specifically, the edge server dynamically adjusts the synchronous degree, i.e., the number of local gradients aggregated in each round, to strike a balance between learning latency and accuracy. Recognizing that data heterogeneity among clients may induce biased global model updating, we propose calibrating the global update by leveraging historical gradients received at the edge server from clients. Following that, we experimentally revealed that allocating more learning time to the later learning stages to increase the synchronous degree contributes to better learning performance. Inspired by this, we develop an adaptive synchronous degree control and resource allocation algorithm to enhance the learning performance of FL while adhering to the overall learning latency and wireless resources constraint. Numerical results demonstrate that the proposed approach is capable of attaining faster convergence speed and higher learning accuracy compared to the benchmark FL algorithms.

A multiple unmanned aerial vehicle (UAV) enabled integrated sensing and communication (ISAC) system is investigated. In contrast to existing UAV-enabled ISAC systems assuming static users or 2-D UAV trajectory, we consider a practical roaming user scenario and a 3-D deployment for UAVs. Then, a joint trajectory and beamforming optimization problem is formulated for maximizing the long-term sum data rate, subject to the transmitting power constraint and ensuring beam pattern gain constraint for sensing target. To address the challenge caused by the dynamic and high dimensionality features, multiagent reinforcement learning (MARL) is employed for this partial observation Markov decision process (POMDP) problem. We proposed a two-step approach for against the dynamic scenario: 1) a K-means-based hierarchical user association algorithm is proposed to renew the user association periodically and 2) a hybrid reward multiagent proximal policy optimization (HR-MAPPO) algorithm is proposed, which decomposes the complex combined reward into a team reward and an individual reward. HR-MAPPO introduces a hyperparameter to control the proportion of team/individual action. Numerical results demonstrate that the proposed HR-MAPPO algorithm can outperform the conventional single-agent and multiagent RL algorithms by maintaining high scores on both the sum data rate and beam pattern gain.

Network intrusion detection systems (NIDSs) are vital for identifying security attacks and predicting early invasion attempts, which is essential for protecting the Internet. Recently, deep learning (DL) has made significant achievements in enhancing intrusion detection accuracy. Nevertheless, the practical implementation of high-complexity DL models is limited by the constrained computational capabilities of the Internet of Things (IoT) devices, e.g., home routers and IoT gateways. This article introduces a novel NIDS approach explicitly tailored for IoT networks, leveraging a lightweight DL model. During the data preprocessing phase, we use a spatially enriched data conversion technique to decrease the dimensionality of high-dimensional raw traffic variables. This helps to offset the problem of increased model complexity. Furthermore, when spatial relationships often exist in the data, we can simplify the learning architecture by utilizing state-of-the-art vision transformer techniques in the computer vision field that can substantially reduce model complexity. The experimental results indicate that the proposed method achieves outstanding accuracy up to 99.57% with high-volume traffic input. Moreover, the proposed method reaches substantial reductions in learnable parameters by 55.35% and 82.07%, along with a remarkable decrease in floating point operations (FLOPs) by 93.56% and 99.28% compared to existing studies. The outstanding achievement highlights the proposed method’s ability to balance model complexity and accuracy performance, making it extremely appropriate for deployment on IoT gateways with limited resources.

In this article, we propose hybrid deep quantum-transformer networks (QTNs) to predict the optimal beam in integrated sensing and communication (ISAC) systems employing millimeter-wave (mmWave) band. In mobile applications, vehicle-to-infrastructure (V2I) communications at high frequency require large antenna arrays and narrow beams, which is associated with high-beam training overhead. In such a scenario, selecting an optimal beam to maximize the signal power at the receiver can be learned from the sensory data collected at the base station and guided by the position-based data provided by the user equipment. Such multimodal sensory data can be utilized by deep learning frameworks to create situational awareness for intelligently predicting optimal beams. We evaluate the proposed learning models in real-world V2I scenarios provided by the multimodal deepsense sixth generation data set and compare them with the existing works. The experimental results show a distance-based accuracy (DBA) score of 0.9124 for multimodal and 0.8832 for position-based data, respectively. Moreover, the hybrid QTN achieve the best DBA scores and the highest accuracy compared to other models on zero-shot testing. These QTN models exhibit low complexity and high performance, demonstrating their potential to address the challenges of beam management in mmWave ISAC systems.

The convergence of advanced communication technologies and powerful computing architecture has unlocked a plethora of opportunities for Internet-of-Things applications. To fully realize this potential, a synergistic design encompassing sensing, computing, and communication is crucial. This article investigates these critical technologies to facilitate service-oriented systems by minimizing end-to-end latency and the number of deployed services at edge servers in mobile edge computing, all within the confines of stringent ultrareliable and low-latency communication requirements and system budget constraints. The addressed optimization problem takes into account variables, such as service placement strategies, task offloading portions, and bandwidth allocation. Simulation results validate the effectiveness of our solution and highlight the impact of key parameters on system performance.

Digital twin technology is crucial to the development of the sixth-generation (6G) Internet of Vehicles (IoV) as it allows the monitoring and assessment of the dynamic and complicated vehicular environment. However, 6G IoV networks have critical challenges in network security and computational efficiency, which need to be addressed. Existing digital twin technologies in 6G IoV networks often suffer from limitations, such as reliance on static models and high computational demands, leading to unstable attack detection and inefficiencies. Their results for attack detection performance metrics, precision, detection rate, and F1-Score are insufficient for 6G IoV. Moreover, these systems concentrate all computational processes within the digital twin’s service layer, leading to inefficiencies. To address these challenges, we introduce a novel artificial intelligence (AI) enhanced digital twin framework designed to significantly improve 6G IoV network security and computational efficiency under dynamic conditions. Our framework employs an advanced feature engineering module that uses feature selection methods and stacked sparse autoencoders (ssAE) to reduce feature dimensions within the cyber twin layer, effectively distributing the overall computational load. It also utilizes an online learning module which enables a network-aware attack detection mechanism for precise attack detection. The proposed solution exhibits a stable performance of around 98% success rate regarding attack detection metrics against two data sets. Specifically, our solution reduces system latency by 12%, energy consumption by 15%, RAM usage by 20%, and improves packet delivery rates by 6.1%. These findings underscore the potential of our framework to enhance the robustness and responsiveness of 6G IoV systems, offering a significant contribution to vehicular network security and management.

Quantum Byzantine fault tolerance (BFT) consensus is a secure and reliable mechanism that enables network nodes to reach an agreement even in the presence of faulty nodes, by using distributed private correlated lists. It plays a crucial role in developing the blockchain-based Metaverse to ensure its integrity and security. In this paper, we propose a counterfactual quantum BFT (CQ-BFT) protocol for a multipartite network using counterfactual unitary telecomputation with the chained quantum Zeno gates. This consensus protocol achieves an agreement among the parties without the passage of any physical particles through the quantum channel. Due to the unique properties of counterfactual communication, we demonstrate that the CQ-BFT protocol can operate in the absence of a shared phase reference and provide a quantum layer of security and robustness against dephasing noise, fulfilling the stringent requirements of blockchain technology. In addition, we analyze the performance tradeoff of the CQ-BFT protocol in terms of the three pillars of blockchain—i.e., security, scalability, and decentralization. The human-centric Metaverse could leverage high degrees of security, noise resilience, and fault tolerance of the CQ-BFT protocol to enhance its underlying network infrastructure. This protocol leads to more robust and immersive virtual environments that prioritize the needs and experiences of Metaverse users.

QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.

The data heterogeneity across clients and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA). This approach aggregates the lower layers of neural networks, responsible for feature extraction, at the parameter server while keeping the upper layers, responsible for complex pattern recognition, at clients for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we derive a convergence bound of the framework under a non-convex loss function setting to reveal the role of unbalanced data size in the learning performance. On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the client selection, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the client scheduling policy. Compared with the benchmark schemes, the proposed PMA-FL improves 3.13% and 11.8% absolute accuracy on two typical datasets with heterogeneous data distribution settings, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic client selection and resource management approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.

We consider the problem of estimating time-varying quantum noise. Specifically, we focus on Pauli qubit noise with time-variations and attempt to construct the most accurate instantaneous channel description. To this end, we propose an adaptive framework of simultaneous communication and parameter estimation (SCAPE) that efficiently and accurately estimates the time-varying Pauli channel while communicating reliably over the channel being estimated. This adaptive framework gives the informed control of communication rate–parameter estimation tradeoff to communicating parties. Interestingly, this adaptive SCAPE requires post-processing entirely on the receiver’s end and minimal feedback to the sender to increase, decrease, or continue with the same code rate of employed error correcting code. This procedure can be particularly useful in time-varying quantum channels with natural periodic deviations in channel conditions, e.g., in satellite communication channels.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. We leverage quantum phenomena such as superposition and entanglement to work on large-scale multi-dimensional data represented by quantum states. Under stochastic behaviors and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantum-empowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed Qe-DRL algorithm and its superior computational learning speed. Our proposed Qe-DRL algorithm outperforms other benchmarks in terms of energy efficiency performance.

Most existing wireless federated learning (FL) studies focused on homogeneous model settings where devices train identical local models. In this setting, the devices with poor communication and computation capabilities may delay the global model update and degrade the performance of FL. Moreover, in the homogenous model settings, the scale of the global model is restricted by the device with the lowest capability. To tackle these challenges, this work proposes an adaptive model pruning-based FL (AMP-FL) framework, where the edge server dynamically generates sub-models by pruning the global model for devices’ local training to adapt their heterogeneous computation capabilities and time-varying channel conditions. Since the involvement of diverse structures of devices’ sub-models in the global model updating may negatively affect the training convergence, we propose compensating for the gradients of pruned model regions by devices’ historical gradients. We then introduce an age of information (AoI) metric to characterize the staleness of local gradients and theoretically analyze the convergence behaviour of AMP-FL. The convergence bound suggests scheduling devices with large AoI of gradients and pruning the model regions with small AoI for devices to improve the learning performance. Inspired by this, we define a new objective function, i.e., the average AoI of local gradients, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, model pruning, and resource block (RB) allocation design. Through detailed analysis, we derive the optimal model pruning strategy and transform the RB allocation problem into equivalent linear programming that can be effectively solved. Experimental results demonstrate the effectiveness and superiority of the proposed approaches. The proposed AMP-FL is capable of achieving 1.9x and 1.6x speed up for FL on MNIST and CIFAR-10 datasets in comparison with the FL schemes with homogeneous model settings.

Integrating the full-duplex capability with quantum communication potentially equips emerging wireless networks with a quantum layer of security for the stringent communication efficiency and security requirements. This paper proposes two new full-duplex quantum communication protocols to exchange classical or quantum information between two remote parties simultaneously without transferring a physical particle over the quantum channel. The first protocol, called quantum duplex coding, enables the exchange of a classical bit using a preshared maximally entangled pair of qubits by means of counterfactual disentanglement. The second protocol, called quantum telexchanging, enables the exchange of an arbitrary unknown qubit without using preshared entanglement by means of counterfactual entanglement and disentanglement. We demonstrate that quantum duplex coding and quantum telexchanging can be achieved by exploiting counterfactual electron-photon interaction gates. It is shown that these tasks can be viewed as full-duplex transmission of bits and qubits via binary erasure channels and quantum erasure channels, respectively.

Distributed learning and multi-tier computing are the key ingredients to ensure ultra-reliable and low-latency communication (URLLC) in 6G networks. The distinct transition from connected things in 5G URLLC networks to connected intelligence in 6G URLLC networks requires ultra-secure communication due to the massive amount of private data. However, it is a challenging task to ensure stringent 6G URLLC requirements along with user privacy and data security in distributed networks. In this paper, we devise a distributed quantum computation protocol to perform a nonlocal controlled unitary operation on a bipartite input state in concealed and counterfactual manner and integrate it with anonymous quantum communication networks. This distributed protocol allows Bob to apply an arbitrary singlequbit unitary operator on Alice’s qubit in a controlled and probabilistic fashion, without revealing the operator to her and without transmitting any physical particle over the quantum channel-called the counterfactual concealed telecomputation (CCT). It is shown that the CCT protocol neither requires the preshared entanglement nor depends on the bipartite input state and that the single-qubit unitary teleportation is a special case of CCT. The quantum circuit for CCT can be implemented using the (chained) quantum Zeno gates. The protocol becomes deterministic with simplified circuit implementation if the initial composite state of Alice and Bob is a Bell-type state. Furthermore, we provide numerical examples of quantum anonymous broadcast networks using the CCT protocol and show their degrees of anonymity in the presence of malicious users.

Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission. This paper proposes a practical channel estimation approach for the first time to an IRS-assisted multi-user ISAC system. The estimation problem in such a system is challenging since the sensing and communication (SAC) signals interfere with each other, and the passive IRS lacks signal processing ability. A two-stage approach is proposed to transfer the overall estimation problem into sub-ones, successively including the direct and reflected channels estimation. Based on this scheme, the ISAC base station (BS) estimates all the SAC channels associated with the target and uplink users, while each downlink user estimates the downlink communication channels individually. Considering a low-cost demand of the ISAC BS and downlink users, the proposed two-stage approach is realized by an efficient neural network (NN) framework that contains two different extreme learning machine (ELM) structures to estimate the above SAC channels. Moreover, two types of input-output pairs to train the ELMs are carefully devised, which impact the estimation accuracy and computational complexity under different system parameters. Simulation results reveal a substantial performance improvement achieved by the proposed ELM-based approach over the least-squares and NN-based benchmarks, with reduced training complexity and faster training speed.

This paper studies the trade-off between the comunication capacity of unmanned aerial vehicle (UAV) and the UAV's unintentional interference in a shared spectrum scenario. In fact, UAV will also cause unintentional interference to other ground users (GUs) who do not communicate with the UAV while transmitting data to a specific ground node (GN). And the location of these GUs is usually unknown. For this problem, we introduce the concept of unacceptable area in which the interference power received by GUs from UAV exceeds their anti-jamming tolerance, and study the trade-off between UAV's average communication capacity and average unacceptable area by joint UAV's 3D trajectory and transmit power optimization. Further, we propose an effective iterative algorithm to solve this non-convex problem by using successive convex approximation (SCA) and block coordinate descent (BCD) method. Numerical results show the proposed scheme can meet the communication requirements of UAV and reduce the UAV's unintentional interference at the same time.

We propose a scheme for simultaneous classical communication and parameter estimation of generalized Pauli channels. This framework relies on the facts that i) under certain conditions, Pauli channels act as classical symmetric channel, and ii) a recently proposed parameter estimation method of Pauli channels can be executed while satisfying the conditions of i). The performance of the parameter estimation part in this simultaneous scheme relies on the symbol error rate of the communication module. This reliance gives rise to a natural tradeoff between the communication rate and the performance of parameter estimation in this simultaneous scheme. We exemplify our protocol by utilizing repetition codes for communication and the aforementioned parameter estimation scheme for the characterization of the channel at hand. We explore the tradeoff behavior between the communication rate and the performance of parameter estimation in the numerical examples. Our proposal paves the way for practical methods in simultaneous communication and channel estimation over unknown quantum channels.

Quantum cybersecurity is the study of all facets regarding the security of communication and computation in a distributed network. Significant developments in quantum technologies have outclassed their classical counterparts, thus envisioning the realization of a quantum internet. However, such quantum resources, in the hands of an adversary, can jeopardize network security. In this paper, we study two secure network connectivity concerns, namely,privacyandanonymity, in quantum information retrieval systems. To this end, we propose a state-of-the-art single-server multi-user quantum anonymous private information retrieval (QAPIR) protocol. To actualize this, we utilize anonymous entanglement as a quantum resource. We show that the QAPIR protocol not only provides privacy but also introduces anonymity as an added layer of security in quantum networks. Furthermore, we also detail a comparative security analysis that establishes the desirable properties of our proposal.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

We discuss discrete Weyl channels (DWCs) with correlated noise in consecutive uses. Some special cases of DWCs have been shown to have the so-called transition behavior, where the optimal signal states, which maximize the Holevo information between the transmitter and the receiver, sharply transition from product states to maximally entangled states as the degree of memory between the consecutive channel uses increases from a threshold value. We first show the general existence of this transition behavior for a class of DWCs where the product states are optimal for the memoryless case. We also provide two methods to estimate the degree of memory exhibited by the correlated noise. These estimators rely on generating classical data by measuring the channel output which shows the same memory characteristics as the consecutive uses of the channel. We specifically consider the Markovian noise but one of our proposed estimators can be straightforwardly generalized to an arbitrary type of noise as it provides the direct access to the sequence in which noise affects the consecutive input states.

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users' collaboration, and thus, avoids many complicated issues such as users' privacy and security, selfishness, etc. In order to optimize users' quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived, making this method simple and flexible to the system context. The second one is based on the deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.

The random propagation of molecules in a fluid medium is characterized by the spontaneous diffusion law as well as the interaction between the environment and molecules. In this paper, we embody the anomalous diffusion theory for modeling and analysis in molecular communication. We employ H-diffusion to model a non-Fickian behavior of molecules in diffusive channels. H-diffusion enables us to model anomalous diffusion as the subordinate relationship between self-similar parent and directing processes and their corresponding probability density functions with two H-variates in a unified fashion. In addition, we introduce standard H-diffusion to make a bridge of normal diffusion across well-known anomalous diffusions such as space-time fractional diffusion, Erdélyi-Kober fractional diffusion, grey Brownian motion, fractional Brownian motion, and Brownian motion. We then characterize the statistical properties of uncertainty of the random propagation time of a molecule governed by H-diffusion laws by introducing a general class of molecular noise-called H-noise. Since H-noise can be an algebraic-tailed distribution, we provide a concept of H-noise power using finite logarithm moments based on zero-order statistics. Finally, we develop a unifying framework for error probability analysis in a timing-based molecular communication system with a concept of signal-to-noise power ratio.

By acquiring cloud-like capacities at the edge of a network, edge computing is expected to significantly improve user experience. In this paper, we formulate a hybrid edge-cloud computing system where an edge device with limited local resources can rent more from a cloud node and perform resource allocation to serve its users. The resource procurement and allocation decisions depend not only on the cloud's multiple rental options but also on the edge's local processing cost and capacity. We first propose an offline algorithm whose decisions are made with full information of future demand. Then, an online algorithm is proposed where the edge node makes irrevocable decisions in each timeslot without future information of demand. We show that both algorithms have constant performance bounds from the offline optimum. Numerical results acquired with Google cluster-usage traces indicate that the cost of the edge node can be substantially reduced by using the proposed algorithms, up to 80% in comparison with baseline algorithms. We also observe how the cloud's pricing structure and edge's local cost influence the procurement decisions.

Molecular communication in nature can incorporate a large number of nano-things in nanonetworks as well as demonstrate how nano-things communicate. This paper presents molecular communication where transmit nanomachines deliver information molecules to a receive nanomachine over an anomalous diffusion channel. By considering a random molecule concentration in a space-time fractional diffusion channel, an analytical expression is derived for the first passage time (FPT) of the molecules. Then, the bit error rate of the $\ell $ th nearest molecular communication with timing binary modulation is derived in terms of Fox's $H$ -function. In the presence of interfering molecules, the mean and variance of the number of the arrived interfering molecules in a given time interval are presented. Using these statistics, a simple mitigation scheme for timing modulation is provided. The results in this paper provide the network performance on the error probability by averaging over a set of random distances between the communicating links as well as a set of random FPTs caused by the anomalous diffusion of molecules. This result will help in designing and developing molecular communication systems for various design purposes.

We consider molecular communication in a nanonet-work in the presence of stochastic interfering molecules where transmit nanomachines deliver information messages to a receive nanomachine over an anomalous diffusion channel. Specifically, a set of interfering molecules is assumed to be scattered in a region according to a Cox process. We first characterize statistics (mean and variance) of the number of interfering molecules arrived in a given time interval using Campbell's theorem. Since the interfering molecules significantly degrade the bit error rate performance in timing modulation scheme, we provide a simple mitigation scheme using the statistics of the number of interfering molecules.

Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. In this paper, we study a multi-user multi-edge-node computation offloading problem. Since edge nodes' communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a non-cooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash equilibrium via a best response-based offloading mechanism. Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. Then, we propose a model-free reinforcement learning offloading mechanism which helps MUs learn their long-term offloading strategies to maximize their long-term utilities. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as local processing and random assignment, and achieves up to 87.87% average long-term payoffs compared to the perfect CSI case.

Proactive caching is considered a cost-effective method to address the backhaul bottleneck problem in cellular network. In this paper, we propose a novel popularity predicting- caching procedure that takes raw video data as an input to determine an optimal cache placement policy, which deals with both published and unpublished videos. To anticipate the popularity of unpublished videos of which the statistical information is not available, we apply the content-based approach by extracting and condensing video features into a high-dimensional vector. Subsequently, we form G clusters of features representing the potential video categories (VCs) and map the feature vector into a G-dimensional space, where each element indicates the percentage to which the video contains the features of the corresponding VC. Finally, we train a prediction model to foresee the popularity, where the set of published videos is used as training data. Last, the prediction with expert advice method is used to update the training set, and to gain insight into how the predictor output will deviate from the best expert prediction, we address the concept of expected cumulative loss and derive the analytical expression for its upper bound. Extensive simulation results are shown to gain insight into our proposed system subject to different factors, such as network size, cache capacity, and user's preference profile. In summary, we show that applying intelligence-based content-aware proactive caching is an efficient approach to significantly improving the operation of cellular networks in the future.

The benefit of having a complementary network such as device-to-device (D2D) communications underlaying cellular systems lies in alleviating the traffic overloading burdens at base stations (BSs). This is critical in modern-day scenarios, e.g., in offloading and caching of mobile data traffic, and delivery of popular Internet contents to users. In this paper, we are chiefly interested in the establishment of D2D networks and how such a network evolves dynamically over time as D2D links are continuously formed and broken. Another important yet challenging research opportunity, i.e., the leverage of human users' social ties for D2D communications, will also be addressed in this paper. To investigate the D2D network formation problem, we look at a game-theoretic framework where the utility function is designed to balance the physical and social domains-where we systematically extract social tie strengths from a real-world data set. To study the evolution of such D2D networks through time in a dynamic stochastic context, we propose a dynamic potential game and show that its equilibrium behaviors can be achieved through some strategy and payoff learning mechanisms. Simulation results verify our analysis. Using a content delivery application to assess the performance of our proposed scheme, it can be shown that major improvements can be obtained in terms of network delay, content accessibility, and BS load reduction.

Cochannel interference is one of the inevitable deleterious components in designing and analyzing of a wireless network. The use of multiple antennas at both transmitting and receiving nodes is a promising technique to suppress and/or alleviate the effect of cochannel interference on capacity. In this paper, we assess the effects of both antenna correlation and cochannel interference on the ergodic capacity of multiple-input multiple-output channels with covariance feedback. In particular, we consider a general family of spatial fading correlation model-called unitary-independent-unitary-which encompasses most of zero-mean channels with arbitrary fading profiles including the popular separable correlation channel models. We derive the average minimum mean-square error and signal-to-interference-plus-noise ratio of the parallel spatial streams using Berezin's supermathematics. We then put forth the structure of optimal input covariance matrix maximizing the mutual information connected with the necessary and sufficient conditions as a generalization of the noise-limited case, which is tested by a simple iterative algorithm. Together with the powerful supermathematical framework, the result in the paper enables us to quantify the multiuser MIMO interference effects on the capacity in terms of spatial correlation and interference power heterogeneity.

In this paper, we study channel identification for a wireless network with the aid of compressed sensing (CS) in both cases of known and unknown sparsity levels of clusters. For the unknown case, we propose using blind CS signal recovery algorithm to sequentially estimate both sparsity level and channel gains. The refined version of blind CS technique is also provided to improve the consistency of channel identification process. The convergence of two algorithms is guaranteed by ensuring that the cost functions decrease after each update. We then investigate the cluster sparsity of users in the case of known sparsity levels of all clusters. By exploiting the alternating direction method of multipliers algorithm through distributed optimization, we can identify channels in sparse clusters parallelly and efficiently compared with conventional convex techniques. In summary, this paper provides some insight into employing sparse and distributed algorithms to efficiently solve the problem of fast channel identification and estimation of users in future network.

We study zero-forcing (ZF) detection for multiple input/multiple output (MIMO) spatial multiplexing under transmit-correlated Rician fading for an NR× NTchannel matrix with rank-1 line-of-sight component. By using matrix transformations and multivariate statistics, our exact analysis yields the signal-to-noise ratio moment generating function (M.G.F.) as an infinite series of gamma distribution M.G.F.'s and analogous series for ZF performance measures, e.g., outage probability and ergodic capacity. However, their numerical convergence is inherently problematic with increasing Rician K-factor, NR, and NT. We circumvent this limitation as follows. First, we derive differential equations satisfied by the performance measures with a novel automated approach employing a computer-algebra tool that implements Gröbner basis computation and creative telescoping. These differential equations are then solved with the holonomic gradient method (HGM) from initial conditions computed with the infinite series. We demonstrate that HGM yields more reliable performance evaluation than by infinite series alone and more expeditious than by simulation, for realistic values of K, and even for NRand NTrelevant to large MIMO systems. We envision extending the proposed approaches for exact analysis and reliable evaluation to more general Rician fading and other transceiver methods.

Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.

The H-transforms are integral transforms that involve Fox's H-functions as kernels. A large variety of integral transforms can be put into particular forms of the H-transform since H-functions subsume most of the known special functions including Meijer's G-functions. In this paper, we embody the H-transform theory into a unifying framework for modeling and analysis in wireless communication. First, we systematize the use of elementary identities and properties of the H-transform by introducing operations on parameter sequences of H-functions. We then put forth H-fading and degree-2 irregular H-fading to model radio propagation under composite, specular, and/or inhomogeneous conditions. The H-fading describes composite effects of multipath fading and shadowing as a single H-variate, including most of typical models such as Rayleigh, Nakagami-m, Weibull, α-μ, N*Nakagami-m, (generalized) K-fading, and Weibull/gamma fading as its special cases. As a new class of H-variates (called the degree-ζ irregular H-variate), the degree-2 irregular H-fading characterizes specular and/or inhomogeneous radio propagation in which the multipath component consists of a strong specularly reflected or line-of-sight (LOS) wave as well as unequal-power or correlated in-phase and quadrature scattered waves. This fading includes a variety of typical models such as Rician, Nakagami-q, κ-μ, η-μ, Rician/LOS gamma, and κ-μ/LOS gamma fading as its special cases. Finally, we develop a unifying H-transform analysis for the amount of fading, error probability, channel capacity, and error exponent in wireless communication using the new systematic language of transcendental H-functions. By virtue of two essential operations-called Mellin and convolution operations-involved in the Mellin transform and Mellin convolution of two H-functions, the H-transforms for these performance measures culminate in H-functions. Using the algebraic asymptotic expansions of the H-transform, we further analyze the error probability and capacity at high and low signal-to-noise ratios in a unified fashion.

For multiple-input/multiple-output (MIMO) spatial multiplexing with zero-forcing detection (ZF), signal-to-noise ratio (SNR) analysis for Rician fading involves the cumbersome noncentral-Wishart distribution (NCWD) of the transmit sample-correlation (Gramian) matrix. Anapproximationwith avirtualCWD previously yielded for the ZF SNR an approximate (virtual) Gamma distribution. However, analytical conditions qualifying the accuracy of the SNR-distribution approximation were unknown. Therefore, we have been attempting to exactly characterize ZF SNR for Rician fading. Our previous attempts succeeded only for the sole Rician-fading stream under Rician–Rayleigh fading, by writing the ZF SNR as scalar Schur complement (SC) in the Gramian. Herein, we pursue a more general matrix-SC-based analysis to characterize SNRs when several streams may undergo Rician fading. On one hand, for full-Rician fading, the SC distribution is found to be exactly a CWD if and only if a channel-mean–correlationconditionholds. Interestingly, this CWD then coincides with thevirtualCWD ensuing from theapproximation. Thus, under thecondition, the actual and virtual SNR-distributions coincide. On the other hand, for Rician–Rayleigh fading, the matrix-SC distribution is characterized in terms of the determinant of a matrix with elementary-function entries, which also yields a new characterization of the ZF SNR. Average error probability results validate our analysis vs. simulation.

For multiple-input-multiple-output (MIMO) spatial-multiplexing transmission, zero-forcing (ZF) detection is appealing because of its low complexity. Our recent MIMO ZF performance analysis for Rician-Rayleigh fading, which is relevant in heterogeneous networks, has yielded for the ZF outage probability and ergodic capacity infinite-series expressions. Because they arose from expanding the confluent hypergeometric function1F1(·, ·, σ) around 0, they do not converge numerically at realistically high Rician K-factor values. Therefore, herein, we seek to take advantage of the fact that1F1(·, ·, σ) satisfies a differential equation, i.e., it is a holonomic function. Holonomic functions can be computed by the holonomic gradient method (HGM), i.e., by numerically solving the satisfied differential equation. Thus, we first reveal that the moment generating function (m.g.f.) and probability density function (p.d.f.) of the ZF signal-to-noise ratio (SNR) are holonomic. Then, from the differential equation for1F1(·, ·, σ), we deduce those satisfied by the SNR m.g.f. and p.d.f. and demonstrate that the HGM helps compute the p.d.f. accurately at practically relevant values of K. Finally, numerical integration of the SNR p.d.f. produced by HGM yields accurate ZF outage probability and ergodic capacity results.

Wireless localization systems have a great importance in a variety of fields such as positioning and tracking systems. Specifically, in hash conditions, e.g., indoor environments, it is difficult to localize an agent with high accuracy due to radio blockage or insufficient information of anchors. Therefore, identification and mitigation of non-line-of-sight (NLOS) radio propagation are highlighted as a solution for improving localization accuracy by overcoming these limitations. In this paper, we develop a robust and efficient localization algorithm using relevance vector machine (RVM). We first design a RVM classifier to identify NLOS signals using features extracted from the received waveform. We then design a RVM regressor to predict a probability density function of range estimates (pair-nodes distance) by exploiting its prediction ability. Numerical results show that the RVM localization algorithm provides high localization accuracy with low complexity.

Localization using the received signal strength (RSS) is a popular technique in the indoor location aware service because of the wide deployment of wireless local area networks (WLANs) and the spreading of mobile device with the measuring RSS function. In this paper, we investigate the RSS-based WLAN indoor positioning system using ℓ0-norm recovery support of sparse representation. Based on the fingerprinting method, the radio map (RM) constructed in offline phase is decomposed into a dictionary and a corresponding sparse representation matrix, using the K-SVD learning overcomplete dictionary algorithm. The learned dictionary guarantees the condition of stable recovery sparse representation. The position of each reference point (RP) in the RM is characterized by an unique support in each vector of sparse representation. We use the orthogonal matching pursuit algorithm to find the support of sparse representation of the real-time measured RSS vector over the learned dictionary and thereby determine which RP is closest to the user. This is an ℓ0-norm minimization problem. We also study the effect of the other RPs to the recovery solution of real-time measurement vector. We first derive the weighted vector that reflects the contribution of each RP in the localization formulation, then the user position is estimated by this vector and the positions of RPs.

In this study, the authors propose an equivocation scheme for wiretap channels, which is composed of bit‐extension mapping, coset coding and hybrid automatic repeat request (HARQ). The inner bit‐extension code and outer coset code are used for equivocation of a wiretapper channel, whereas the HARQ scheme is to mitigate noisy errors in a main legitimate channel. These concatenated codes and HARQ are effective and practical for various channel conditions. The average equivocation and the probability of causing imperfect secrecy are analysed for finite codeword lengths. As a function of channel conditions, they investigate the block error rate at the legitimate receiver and the information leakage to the wiretapper. From simulation results, they further determine the minimum requirements of code design for some target values of the ‘residual’ block error rate and information leakage at maximum retransmission.

The use of multiple‐antenna arrays has attracted much attention for the physical layer security of wireless systems, where the so‐called artificial‐noise solution can be applied to enhance the communication confidentiality. In the first part of this study, the authors consider secure communication over a multiple‐input single‐output Rayleigh‐fading channel in the presence of a multiple‐antenna eavesdropper – referred to as a multiple‐input single‐output multiple‐eavesdropper (MISOME) wiretap channel. Specifically, secure beamforming with artificial noise is treated when the transmitter has access to full channel state information (CSI) of a legitimate channel but only partial CSI of an eavesdropper channel. First, the optimal power allocation between the information‐bearing signal and artificial noise (or simulated interference) is derived to maximise the achievable secrecy rate in the presence of a weak or strong eavesdropper. Then, the first‐order optimal power allocation strategy is developed in a switched fashion by selecting the best of weak‐ and strong‐eavesdropper solutions for a general eavesdropping attack, and a closed‐form expression is derived for the ergodic secrecy rate achieved by secure beamforming with this switched power allocation in the MISOME wiretap channel. The numerical results show that the switched power allocation is a simple but effective approach that nearly achieves the optimal secrecy rate.

The authors consider secure beamforming with artificial noise in a multiple‐input single‐output multiple‐eavesdropper (MISOME) wiretap channel, where a transmitter has access to full channel state information (CSI) of a legitimate channel but only partial CSI of eavesdropper channels. In the second part of this study, the authors first put forth a new notion of symbol error probability (SEP) for confidential information – called the ‘ δ ‐secrecy SEP’ – to connect the reliability and confidentiality of the legitimate communication in MISOME wiretap channels. For single‐antenna colluding and non‐colluding eavesdroppers, the authors then quantify the diversity impact of secure beamforming with artificial noise on the δ ‐secrecy SEP and show that the artificial ‐noise strategy with n t transmit antennas preserves the secrecy diversity of order n t − n e for n e colluding eavesdroppers and n t − 1 for n e non‐colluding eavesdroppers, respectively. In addition, the authors determine the optimal power allocation between the information‐bearing signal and artificial noise to minimise the δ ‐secrecy SEP in the presence of weak or strong eavesdroppers, and further develop the switched power allocation for general eavesdropping attacks.

Physical-layer multicast transmission, which seeks to deliver information messages to all users simultaneously, is becoming more important in wireless systems with demand for various multimedia mobile applications such as Multimedia Broadcast Multicast Services. The spatial randomness of communicating nodes in a wireless network is one of inevitable uncertainties in the design and analysis of network information flow and connectivity. The use of multiple antennas at both transmitting and receiving nodes is the most promising strategy to increase spectral efficiency and communication reliability as well as to enhance physical-layer confidentiality of wireless systems. In this paper, we characterize multicasting in such a stochastic multiple-input multiple-output (MIMO) network where a probe transmitter broadcasts confidential data with sectorized transmission to legitimate receivers sitting in a region R. We first put forth a measure of the total amount of information flow, called the space-time capacity, into R in a spatial random field of legitimate receivers without accounting for intrinsic confidentiality at the physical layer. We then derive the space-time capacity into the sectoral region R and the nth nearest ergodic capacity in a Poisson field to characterize the spatial average and ordering of MIMO ergodic capacity achieved by legitimate receivers in R. Using the Mar\u{c}enko-Pastur law, we further assess the asymptotic space-time capacity and the nth nearest ergodic capacity per receive antenna as the antenna numbers tend to infinity. In the presence of eavesdropping, we determine a total amount of confidential information flow per receive antenna, called the space-time secrecy rate, into R in Poisson fields of receiving equivalents-with asymptotic arguments. Using an asymptotic secrecy graph on R, we also characterize local confidential connectivity such as the secrecy range, out-degree, and out-isolation probability of the probe transmitter. The framework developed in this work enables us to quantify the local information flow in random MIMO wireless networks by averaging first small-scale fading processes over time and then large-scale path losses over space.

We analyze the performance of multiple input/multiple output (MIMO) communications systems employing spatial multiplexing and zero-forcing detection (ZF). The distribution of the ZF signal-to-noise ratio (SNR) is characterized when either the intended stream or interfering streams experience Rician fading, and when the fading may be correlated on the transmit side. Previously, exact ZF analysis based on a well-known SNR expression has been hindered by the noncentrality of the Wishart distribution involved. In addition, approximation with a central-Wishart distribution has not proved consistently accurate. In contrast, the following exact ZF study proceeds from a lesser-known SNR expression that separates the intended and interfering channel-gain vectors. By first conditioning on, and then averaging over the interference, the ZF SNR distribution for Rician-Rayleigh fading is shown to be an infinite linear combination of gamma distributions. On the other hand, for Rayleigh-Rician fading, the ZF SNR is shown to be gamma-distributed. Based on the SNR distribution, we derive new series expressions for the ZF average error probability, outage probability, and ergodic capacity. Numerical results confirm the accuracy of our new expressions, and reveal effects of interference and channel statistics on performance.

The use of multiple antennas at both transmitting and receiving nodes is the most promising strategy to increase spectral efficiency and communication reliability as well as to leverage physical-layer confidentiality of wireless systems. In this paper, we characterize secure connectivity in a stochastic multiple-input multiple-output (MIMO) multicast network where a probe transmitter broadcasts common confidential data with sectorized transmission to legitimate receivers sitting in a region R in the presence of passive eavesdroppers. We first determine a total amount of common confidential information flow per receive antenna, called the space-time secrecy rate, into R in Poisson fields of receiving equivalents — with asymptotic arguments. Using an asymptotic secrecy graph on R, we then characterize local secure connectivity such as the secrecy range, out-degree, and out-isolation probability of the probe transmitter.

In this paper, we integrate key wireless propagation effects such as the path loss, shadowing, and multipath fading into a single Fox's H-variate using the H-preserving property under products, powers, quotients, and their combinations. We then establish a unifying framework to analyze the error probability and channel capacity for V2V communication in a Cox field of vehicles, using again the language of Fox's H-functions. This framework enables us to characterize intervehicle communication in the doubly stochastic vehicular ad-hoc network (VANET) by averaging both small- and large-scale fading processes in time and (random) distance-dependent path losses in space.

Safety message dissemination in a vehicular ad-hoc network (VANET) requires vehicle-to-vehicle (V2V) communication with low latency and high reliability. The dynamics of vehicle passing and queueing as well as high mobility create distinctive propagation characteristics of wireless medium and inevitable uncertainty in space-time patterns of the vehicle density on a road. It is therefore of great importance to account for random vehicle locations in V2V communication. In this paper, we characterize intervehicle communication in a random field of vehicles, where a beacon or head vehicle (transmitter) broadcasts safety or warning messages to neighboring client vehicles (receivers) randomly located in a cluster on the road. To account for a doubly stochastic property of the VANET, we first model vehicle's random locations as a stationary Cox process with Fox's H-distributed random intensity (vehicle concentration) and derive the distributional functions of the lth nearest client's distance from the beacon in such a Fox Cox field of vehicles. We then consolidate this spatial randomness of receiving vehicles into a path loss model and develop a triply-composite Fox channel model that combines key wireless propagation effects such as the distance-dependent path loss, large-scale fading (shadowing), and small-scale fading (multipath fading). In Fox channel modeling, each constituent propagation effect is described as Fox's H-variate, culminating again in Fox's H-variate for the received power or equivalently the instantaneous signal-to-noise ratio at the lth nearest client vehicle. Due to versatility of Fox's H-functions, this stochastic channel model can encompass a variety of well-established or generalized statistical propagation models used in wireless communication; be well-fitted to measurement data in diverse propagation environments by varying parameters; and facilitate a unifying analysis for fundamental physical-layer performances, such as error probability and channel capacity, using again the language of Fox's H-functions. This work serves to develop a unifying framework to characterize V2V communication in a doubly stochastic VANET by averaging both the small- and large-scale fading effects as well as the (random) distance-dependent path losses.

With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active/sleep (on/off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks.

The need for bandwidth and the incitation to reduce power consumption lead to the reduction of cell size in wireless networks. This allows reducing the distance between a user and the base station, thus increasing the capacity. A relatively inexpensive way of deploying small-cell networks is to use femtocells. However, the reduction in cell size causes problems for coordination and network deployment, especially due to the intra- and cross-tier interference. In this paper, we consider a two-tier multiple-input multiple-output (MIMO) network in the downlink, where a single macrocell base station with multiple transmit antennas coexists with multiple closed-access MIMO femtocells. With multiple receive antennas at both the macrocell and femtocell users, we propose an opportunistic interference alignment scheme to design the transmit and receive beamformers in order to mitigate intra- (or inter-) and cross-tier interference. Moreover, to reduce the number of macrocell and femtocell users coexisting in the same spectrum, we apply a random spectrum allocation on top of the opportunistic interference alignment. Using stochastic geometry, we analyze the proposed scheme in terms of the distribution of a received signal-to-interference-plus-noise ratio, spatial average capacity, network throughput, and energy efficiency. In the presence of imperfect channel state information, we further quantify the performance loss in spatial average capacity. Numerical results show the effectiveness of our proposed scheme in improving the performance of random MIMO femtocell networks.

Safety message dissemination in a vehicular ad-hoc network (VANET) is in desperate need of vehicle-to-vehicle (V2V) communication with low latency and high reliability. The dynamics of vehicle passing and queueing as well as high mobility create distinctive propagation characteristics of wireless medium and inevitable uncertainty in space-time patterns of the vehicle density on a road. It is therefore of great importance to integrate stochastic geometry of vehicle locations into V2V channel modeling. In this paper, we characterize intervehicle communication in a random field of vehicles, where a beacon or head vehicle (transmitter) geobroadcasts safety or warning messages to neighboring client vehicles (receivers) randomly located in a cluster on the road. To account for a doubly stochastic spatial property of the VANET, we first model vehicle's random locations as a stationary Cox process with Fox's H-distributed random intensity (vehicle concentration) and derive the distributional functions of the `th nearest client's distance from the beacon in such a Fox Cox field of vehicles. We then consolidate this spatial randomness of receiving vehicles into a path loss model and develop a triply-composite Fox channel model that combines key wireless propagation effects such as the distance-dependent path loss, large-scale fading (shadowing), and small-scale fading (multipath fading).

The physical-layer multicast transmission that seeks to deliver the common information messages to the all users simultaneously is becoming more important in wireless systems with demand for various multimedia mobile applications such as Multimedia Broadcast Multicast Services. The spatial randomness of communicating nodes in a wireless network is one of inevitable uncertainty in design and analysis of network information flow and connectivity. In this paper, we characterize local information flow in a stochastic multiple-input multiple-output (MIMO) multicast network where a probe transmitter geobroadcasts common data with sectorized transmission to receivers sitting in a region R. We first put forth a measure of the total amount of common information flow, called the space-time capacity, into R in a spatial random field of receivers. We then derive the space-time capacity into a sectoral region R and the nth nearest ergodic capacity in a Poisson field to characterize the spatial average and ordering of MIMO ergodic capacity achieved by receivers in R. Using the Marčenko-Pastur law, we further assess the asymptotic space-time capacity and the nth nearest ergodic capacity per receive antenna as the antenna numbers tend to infinity. The framework developed in this work enables us to quantify the local information flow in random MIMO wireless networks by averaging both small-scale fading processes on time and large-scale path losses on space.

The use of multiple-antenna arrays can leverage the physical-layer security of wireless systems. It is therefore important to characterize such systems in a realistic situation where we can apply the so-called artificial-noise solution in multiple-antenna systems to enhance the communication confidentiality by only exploiting a fading nature of wireless environments. In this paper, we consider secure communication over a multiple-input single-output Rayleigh-fading channel in the presence of a multiple-antenna eavesdropper-referred to as a multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, secure beamforming with artificial noise is treated when the transmitter has access to full channel state information (CSI) of a legitimate channel but only channel distribution information of an eavesdropper channel. We first put forth a new notion of the symbol error probability (SEP) of confidential information-called the δ-secrecy SEP-to connect the reliability and confidentiality of the legitimate communication. We then quantify the diversity impact of secure beamforming with artificial noise on the δ-secrecy SEP in the MISOME wiretap channel and show that the artificial-noise strategy preserves the secrecy diversity of order nt- nefor nttransmit and neeavesdropper antennas.

This paper presents a new framework to evaluate the performance of wireless networks with intrinsic secrecy. Specifically, we put forth the notion of secure node packing (SNP), defined as the number of legitimate users capable of transmitting data with secrecy using stochastic geometry. Transmission with secrecy means a legitimate receiver can decode transmitted data (reliability) while its corresponding eavesdropper cannot decode (secrecy). The SNP is derived for two cases of secure transmission: weakly secure transmission against the nearest eavesdropper and strongly secure transmission against all eavesdroppers. We quantify the effect of network parameters, including spatial densities of legitimate transmitter and eavesdropper, on the throughput of wireless networks with secrecy. In addition, we determine the SNP in the presence of a coexisting network and show that the additional interference from a coexisting network with an appropriate spatial density is beneficial for the secure network throughput.

In this paper, we consider secure communication over a multiple-input single-output Rayleigh-fading channel in the presence of a multiple-antenna eavesdropper - referred to as a multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, secure beamforming with artificial noise is treated when the transmitter has access to full channel state information (CSI) of a legitimate channel but only partial statistical CSI of an eavesdropper channel. We first derive the optimal power allocation between the information-bearing signal and artificial noise (or simulated interference) to maximize the achievable secrecy rate in the presence of a weak or strong eavesdropper. We then develop a near-optimal power allocation strategy in a switched fashion for a general case and derive a closed-form expression for the ergodic secrecy rate achieved by secure beamforming with this switched power allocation in the MISOME wiretap channel.

In this paper, we consider a wideband cognitive radio network with limited available frame energy and treat a fundamental energy allocation problem: how available energy should be optimally allocated for sensing, probing, and data transmission to maximize the achievable average opportunistic spectrum access (OSA) throughput. By casting this problem into the multi-armed bandit framework under probably approximately correct learning, we put forth a proactive strategy for determining the optimal sensing cardinality and probing cardinality that maximize the average throughput of the secondary user. Numerical results show that our framework gives the the optimal diversity-energy tradeoff for the average OSA throughput.

In this paper, we consider a two-tier MIMO network in the downlink, consisting of a single macrocell base station with multiple transmit antennas coexisting with several closed-access MIMO femtocells. With multiple receive antennas at both the macrocell and femtocell users, we propose an opportunistic interference alignment scheme to design the transmit and receive beamformers in order to mitigate intra and inter-tier interference. Moreover, to reduce the number of macrocell and femtocell users coexisting in the same spectrum, we apply a random spectrum allocation on top of the opportunistic interference alignment scheme. By applying stochastic geometry, we evaluate the performance of our proposed scheme in terms of distribution of received signal-to-interference plus noise ratio. Numerical results show the effectiveness of our proposed scheme in improving the performance of random MIMO femtocell networks.

To account for randomly distributed nodes in a wireless ad hoc network, the random access transport capacity is defined as the average maximum rate of successful end-to-end transmission over some distance. In this paper, we consider a random access transport capacity for a dual-hop relaying to find the end-to-end throughput of wireless ad hoc network, where each node relays using Amplify-and-Forward (AF) strategy. In particular, we also present the exact outage probability for dualhop AF relaying in the presence of both co-channel interference and thermal noise, where interferers are spatially distributed following a Poisson distribution. Intriguingly, even though transmitting nodes increase, numerical results demonstrate that the overall throughput of dual-hop AF relaying decreases due to interference. Moreover, it is noted that the dual-hop AF relaying is still beneficial in terms of the random access transport capacity in wireless ad hoc networks.

A femtocell technology-towards the deployment of small-cell networks-is a key enabler for improving indoor coverage and throughput per network area at a low cost in future wireless networks. However, these small-cell networking inevitably increases cochannel interference due to aggressive (even uncontrolled) reuse of spectral resources. One of the attractive approaches to alleviating the cochannel interference is a multiple-antenna technique for which accurately characterizing the effects of interference is crucial but challenging. To elucidate this important problem, we analyze the performance of interference rejection diversity combining, often called the optimum combining, in an uplink two-tier femtocell network. Specifically, we consider that a single-antenna femtocell user (transmitter) communicates with a closed femtocell access point (receiver) with multiple antennas in the presence of single-antenna cochannel interferers from co-tier (femtocells) and cross-tier (macrocell) networks. We introduce a new mathematical methodology to analyze the average symbol error probability of optimum combining diversity systems in Rayleigh fading, accounting for multiple unequal-power interferers, each is spatially correlated across receiving antennas. The analysis resorts to the so-called Berezin's supermathematics that treats both commuting and Grassmann anticommuting variables on an equal footing. This powerful supermathematical framework enables us to quantify the cross- and co-tier interference effects in terms of interference power heterogeneity and spatial correlation.

Multi-hop relaying over long distance is efficient to solve transmitter-power problem and mitigate wireless channel impairment. This paper provides a multi-hop relaying with Decode-and-Forward(DF) strategy in a wireless ad hoc network considering both noise and interference. Furthermore, we consider a realistic communication model where interferers are randomly scattered and uncoordinated with Poisson distribution. We analyze exact outage probability and spectral efficiency of a multi-hop DF relaying. From numerical results, multi-hop DF relaying has better performance than dual-hop relaying in terms of both outage probability and spectral efficiency.

Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we show how a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading can be used to assess the aggregate interference in specific environments. Specifically, we consider scenarios like power controlled primary network, secondary network with interference avoidance mechanism, and non-circular coverage region.

A femtocell technology-towards the deployment of small-cell networks-is a key enabling feature for future wireless networks to improve indoor coverage and throughput per network area at a low cost. However, these small-cell networking inevitably increases cochannel interference due to aggressive (even uncontrolled) reuse of spectral resources. One of the attractive approaches to alleviating the cochannel interference is a multiple-antenna technique, where accurately characterizing the effects of cross- and co-tier interference on the performance of multiple-antenna communications is crucial but challenging. We apply superanalysis framework to provide further numerical results on the optimum combining diversity systems in an uplink two-tier femtocell network. Specifically, we consider that a single-antenna femtocell user (transmitter) communicates with a closed femtocell access point (receiver) with multiple antennas in the presence of single-antenna cochannel interferers from co-tier (femtocells) and cross-tier (macrocell) networks. We then quantify the cross- and co-tier interference effects in terms of degrees of interference power heterogeneity and spatial correlation.

We consider a wideband cognitive radio network, where the channel is modeled by doubly block fading in time and frequency. With a frame energy constraint, we propose a joint sensing and probing strategy, called active sensing, to access the best channel for secondary data transmission. By employing the multi-armed bandit problem, we establish the fundamental tradeoff between sensing, probing, and transmitting energies for an average throughput of the secondary user and proactively design the optimal cardinalities of sensing and probing sets of channels. Our design methodology provides a framework to determine the optimal energy tradeoff between exploration and exploitation in wideband cognitive radio networks.

Opportunistic spectrum access creates the opening of under-utilized portions of the licensed spectrum for reuse, provided that the transmissions of secondary radios do not cause harmful interference to primary users. Such a system would require secondary users to be cognitive-they must accurately detect and rapidly react to varying spectrum usage. Therefore, it is important to characterize the effect of cognitive network interference due to such secondary spectrum reuse. In this paper, we propose a new statistical model for aggregate interference of a cognitive network, which accounts for the sensing procedure, secondary spatial reuse protocol, and environment-dependent conditions such as path loss, shadowing, and channel fading. We first derive the characteristic function and cumulants of the cognitive network interference at a primary user. Using the theory of truncated-stable distributions, we then develop the statistical model for the cognitive network interference. We further extend this model to include the effect of power control and demonstrate the use of our model in evaluating the system performance of cognitive networks. Numerical results show the effectiveness of our model for capturing the statistical behavior of the cognitive network interference. This work provides essential understanding of interference for successful deployment of future cognitive networks.

We analyze the performance of optimum combining in a general cochannel interference environment with thermal noise. Specifically, we consider multiple unequal-power interferers, each is spatially correlated across receiving antennas. We develop a new mathematical methodology to analyze the average symbol error probability (SEP) of optimum combining diversity systems in Rayleigh fading. The analysis resorts to the so-called Berezin's supermathematics that treats both commuting and Grassmann anticommuting variables on an equal footing. This superanalysis framework enables us to derive the exact SEP expression for an arbitrary number of interferers with spatial correlation and possibly different power levels. Our results therefore encompass all the previous analytical results, based on the theory of multivariate statistics relating to complex Wishart matrices, for equal-power and/or spatially-uncorrelated interferers. Connecting the powerful supermathematical framework to the analysis of wireless diversity systems with optimum combining, we quantify the interference effects in terms of the degree of power unbalance and the amount of spatial correlation.

Cognitive radio (CR) is an advanced enabling technology for improving the spectrum utilization in wireless systems. Specially, spectrum underlay systems assign licensed bandwidth to a secondary network while guaranteeing the quality of service (QoS) for a primary network. In this paper, we consider downlink CR underlay multiple-input single-output (MISO) networks comprising primary and secondary transmitters serving multiple user. In particular, we formulate the following beamforming optimization problems: 1) total transmit power minimization problem; 2) mean-square error balancing problem; and 3) network interference power minimization problem. In the presence of perfect channel state information (CSI), we formulate the optimization algorithms in a centralized manner and determine the optimal beamformers using standard convex optimization techniques. To account imperfect CSI, we also propose robust algorithms through the worst-case design to mitigate the effect of channel uncertainty. Finally, numerical results are provided to illustrate the validity of our proposed algorithms.

In a two-way relay network, two terminals exchange information over a shared wireless half-duplex channel with the help of a relay. Due to its fundamental and practical importance, there has been an increasing interest in this channel. However, there has been little work that characterizes the fundamental tradeoff between the communication reliability and transmission rate across all signal-to-noise ratios. In this paper, we consider amplify-and-forward (AF) two-way relaying due to its simplicity. We first derive the random coding error exponent for the link in each direction. From the exponent expression, the capacity and cutoff rate for each link are also deduced. We then put forth the notion of bottleneck error exponent, which is the worst exponent decay between the two links, to give us insight into the fundamental tradeoff between the rate pair and information-exchange reliability in the two-way relay network. As applications of the error exponent analysis to design a reliable AF two-way relay network, we present two optimization framework to maximize the bottleneck error exponent, namely: i) the optimal rate allocation under a sum-rate constraint and its closed-form quasi-optimal solution that requires only knowledge of the capacity and cutoff rate of each link; and ii) the optimal power allocation under a total power constraint and perfect global channel state information, which is shown equivalently to a quasi-convex optimization problem. Numerical results verify our analysis and the effectiveness of the optimal rate and power allocations in maximizing the bottleneck error exponent, i.e. the network information-exchange reliability.

In a wireless network, the use of cooperation among nodes can significantly improve capacity and robustness to fading. Node cooperation can take many forms, including relaying and coordinated beamforming. However, many cooperation techniques have been developed for operation in narrowband systems for high signal-to-noise ratio (SNR) applications. It is important to study how relay networks perform in a low-SNR regime, where the available degrees of freedom is large and the resulting SNR per degree of freedom is small. In this paper, taking into account either low-power narrowband transmissions (P ¿ 0) or wideband transmissions with fixed power (W ¿∞), we investigate the achievable rates and scaling laws of bursty amplify-and-forward relay networks in the low-SNR regime. Specifically, our results allow us to understand the effect of different system parameters on the achievable rates and scaling laws in the low-SNR regime, and highlight the role of bursty transmissions in this regime. These results entirely depend on the geographic locations of the nodes and are applicable for both fixed and random networks. We identify four scaling regimes that depend on the growth of the number of relay nodes and the increase of burstiness relative to the SNR. We characterize the achievable rates and the scaling laws in the joint asymptotic regime of the number of relay nodes, SNR, and duty-cycle parameter. These results can serve as design guidelines to indicate when bursty transmissions are most useful.

Multiple-input multiple-output (MIMO) systems are being considered as one of the key enabling technologies for future wireless networks. However, the decrease in capacity due to the presence of interferers in MIMO networks is not well understood. In this paper, we develop an analytical framework to characterize the capacity of MIMO communication systems in the presence of multiple MIMO co-channel interferers and noise. We consider the situation in which transmitters have no channel state information, and all links undergo Rayleigh fading. We first generalize the determinant representation of hypergeometric functions with matrix arguments to the case when the argument matrices have eigenvalues of arbitrary multiplicity. This enables the derivation of the distribution of the eigenvalues of Gaussian quadratic forms and Wishart matrices with arbitrary correlation, with application to both single-user and multiuser MIMO systems. In particular, we derive the ergodic mutual information for MIMO systems in the presence of multiple MIMO interferers. Our analysis is valid for any number of interferers, each with arbitrary number of antennas having possibly unequal power levels. This framework, therefore, accommodates the study of distributed MIMO systems and accounts for different spatial positions of the MIMO interferers.

Joint source-channel coding has been shown to yield optimal end-to-end performance in terms of overall expected distortion for quasi-static fading channels. Due to the inherent broadcast nature of the wireless medium, wireless communications are susceptible to eavesdropping. Thus, it is unclear how imposing additional secrecy constraint on the system will affect the end-to-end performance of the joint source-channel coding. In this paper, we consider the information theoretic secure source transmission in a classical three node wiretap channel, consisting of a source node, a destination node, and a wiretapper node. In the high signal-to-noise ratio regime, we quantify the cost of providing secure transmissions through secrecy outage probability and secrecy distortion exponent. Our results show that with the additional secrecy constraint, there exists a lower bound on the bandwidth expansion factor, below which perfect secrecy is impossible. Moreover, this lower bound on bandwidth expansion factor depends on the type of layered source transmission strategy used. In summary, this work indicates that source-channel coding strategies as well as the level of secrecy need to be carefully designed in order to maximize the secrecy distortion exponent.

Wireless networks are susceptible to eavesdropping due to the inherent broadcast nature of the wireless medium. However, it is also this broadcast nature of wireless communications that allows cooperation among multiple users or relay nodes. Thus, it is unclear how cooperation and secrecy interacts, particularly in the scenario when each node in the network is equipped with multiple antennas (MIMO). In this paper, we consider the information theoretic secure transmission in a classical four node wiretap relay network, consisting of a source node, a destination node, and a relay node, in the presence of a fourth adversarial wiretapper node that can observe all transmissions from the source as well as the relay node. For a MIMO quasi-static fading channel model between every pair of nodes, we explore the importance of degrees of freedom in providing secure transmissions, through secrecy outage analysis in the high signal-to-noise ratio regime. We characterize the achievable diversity versus secure multiplexing gain tradeoff for decode-and-forward as well as compress-and-forward relaying, under the full-duplex constraint.

In a two-way relay network, two terminals exchange information over a shared wireless half-duplex channel with the help of a relay. Due to its fundamental and practical importance, there has been an increasing interest in this channel. However, surprisingly, there has been little work that characterizes the fundamental tradeoff between the communication reliability and transmission rate across all signal-to-noise ratio (SNR) ratios. In this paper, we consider amplify-and-forward (AF) two-way relaying due to its simplicity. We first derive the random coding error exponent for the link in each direction. From the exponent expression, the capacity and cutoff rate for each link are also deduced. We then put forth the notion of the bottleneck error exponent, which is the worst exponent decay between the two links, to give us insight into the fundamental tradeoff between the rate pair and information-exchange reliability of the two terminals.

In wireless networks, the use of cooperation among nodes can significantly improve capacity and robustness to fading. However, many cooperation techniques have been developed for operation in narrowband systems for high signal-to-noise ratio (SNR) applications. It is important to study how relay networks perform in a wideband regime, where the available degrees of freedom is large and the resulting SNR per degree of freedom is small. In this paper, taking into account wideband transmissions with fixed power (Wrarr infin), we investigate the achievable rates and scaling laws of bursty amplify-and-forward relay networks in the wideband regime. Specifically, our results allow us to understand the effect of different system parameters on the achievable rates and scaling laws in the wideband regime, and highlight the role of bursty transmissions in this regime. We identify four scaling regimes that depend on the growth of the number of relay nodes and the increase of burstiness relative to the SNR. These results can serve as design guidelines to indicate when bursty transmissions are most useful.

In this paper, we derive Gallager's random coding error exponent for multiple-input multiple-output (MIMO) Rayleigh block-fading channels, assuming no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. This measure gives insight into a fundamental tradeoff between the communication reliability and information rate of MIMO channels, enabling to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We quantify the effects of the number of antennas, channel coherence time, and spatial fading correlation on the MIMO exponent. In addition, the general formulae for the ergodic capacity and the cutoff rate in the presence of spatial correlation are deduced from the exponent expressions. These formulae are applicable to arbitrary structures of transmit and receive correlation, encompassing all the previously known results as special cases of our expressions.

We analyze diversity performance of scalar fixed- gain amplify-and-forward (AF) cooperation in multiple-input multiple-output (MIMO) relay channels with 𝓃ssource antennas, 𝓃Rrelay antennas, and 𝓃Ddestination antennas. We first derive the exact symbol error probability (SEP) for maximum likelihood decoding of orthogonal space-time block codes with M-ary phase-shift keying modulation over such channels and then characterize the effect of MIMO cooperative diversity on SEP behavior in a high signal-to-noise ratio regime. We show that the simple scalar-gain AF cooperation can create the diversity order dAF≤nSnD+nSnRnD/max{nS,nR,nD} with the equality if 2max{nS,nR,nD}≥nS+nR+nD-1. This finding reveals that the number of relay antennas greater than or equal to nS+ nD- 1 is required to achieve the additional diversity order nSnDby scalar-gain AF relaying. We also present the asymptotic SEP as the number of relay antennas tends to infinity.

In this paper, we assess the combined effects of rank deficiency and spatial fading correlation on the diversity performance of multiple-input multiple-output (MIMO) systems in terms of the symbol error probability, the effective fading figure (EFF), and the capacity at low signal-to-noise ratio (SNR). In particular, we consider a general family of MIMO channels known as double-scattering channels - i.e., Rayleigh product MIMO channels - which encompasses a variety of propagation environments from independent and identically distributed Rayleigh to degenerate keyhole cases by embracing both rank-deficient and spatial correlation effects. We quantify the combined effect of the spatial correlation and the lack of scattering richness on the EFF and the low-SNR capacity in terms of the correlation figures of transmit, receive, and scatterer correlation matrices. We further show the monotonicity properties of these performance measures with respect to the strength of spatial correlation, characterized by the eigenvalue majorization relations of the correlation matrices.

We derive Gallager's random coding error exponent for multiple-input multiple-output (MIMO) channels, assuming no channel-state information (CSI) at the transmitter and perfect CSI at the receiver. This measure gives insight into a fundamental tradeoff between the communication reliability and information rate of MIMO channels, enabling to determine the required codeword length to achieve a prescribed error probability at a given rate below the channel capacity. We quantify the effects of the number of antennas, channel coherence time, and spatial fading correlation on the MIMO exponent. In addition, the general formulae for the ergodic capacity and the cutoff rate in the presence of spatial correlation are deduced from the exponent expressions. These formulae are applicable to arbitrary structures of transmit and receive correlation, encompassing all the previously known results as special cases of our expressions.

We derive the exact maximal-ratio combining (MRC) performance of dual-hop cooperative diversity systems with L-antenna destination reception in Nakagami-m fading channels, where a single-antenna relay operates in semi-blind (fixed-gain) amplify-and-forward (AF) mode. We also show that if m0, m1, and m2are the respective Nakagami parameters for the source-to-destination, source-to-relay, and relay-to-destination links, the semi-blind AF cooperation with MRC achieves the diversity order of m0L + min {m1, m2L}.

The potential benefits of multiple-antenna systems may be limited by two types of channel degradations-rank deficiency and spatial fading correlation of the channel. In this paper, we assess the effects of these degradations on the diversity performance of multiple-input multiple-output (MIMO) systems, with an emphasis on orthogonal space-time block codes (OSTBC), in terms of the symbol error probability (SEP), the effective fading figure (EFF), and the capacity at low signal-to-noise ratio (SNR). In particular, we consider a general family of MIMO channels known as double-scattering channels-i.e., Rayleigh product MIMO channels-which encompasses a variety of propagation environments from independent and identically distributed (i.i.d.) Rayleigh to degenerate keyhole or pinhole cases by embracing both rank-deficient and spatial correlation effects. It is shown that a MIMO system with transmit and receive antennas achieves the diversity of order in a double-scattering channel with effective scatterers. We also quantify the combined effect of the spatial correlation and the lack of scattering richness on the EFF and the low-SNR capacity in terms of the correlation figures of transmit, receive, and scatterer correlation matrices. We further show the monotonicity properties of these performance measures with respect to the strength of spatial correlation, characterized by the eigenvalue majorization relations of the correlation matrices.

We derive the exact maximal-ratio combining (MRC) performance of dual-hop cooperative diversity systems with L-antenna destination reception in Nakagami-m fading channels, where a single-antenna relay operates in fixed-gain (semi-blind) amplify-and-forward (AF) mode. We also show that if m0, m1, and m2are the respective Nakagami parameters for the source-to-destination, source-to-relay, and relay-to-destination links, the fixed-gain AF cooperation with MRC achieves the diversity order of m0L + min {m1, m2L}.

In this paper, we derive the asymptotic statistics of mutual information for multiple-input multiple-output (MIMO) Rayleigh-fading channels in the presence of spatial fading correlation at both the transmitter and the receiver. We first introduce a class of asymptotic linear spectral statistics, calledcorrelants, for a structured correlation matrix. The mean and variance of MIMO mutual information are then expressed in terms of the correlants of spatial correlation matrices in the asymptotic regime where the number of transmit and receive antennas tends to infinity. In particular, using Szego's theorem on the asymptotic eigenvalue distribution of Toeplitz matrices, we give examples for special classes of correlation matrices with Toeplitz structure-exponential(orKac-Murdock-Szego),tridiagonal,andconstant(orintraclass) correlation matrices.

Relay power allocation has been shown to provide substantial performance gain in wireless relay networks when perfect global channel state information (CSI) is available. In this paper, we consider a more realistic scenario, where such global CSI is subject to uncertainty, and we aim to design robust power allocation protocols for both the coherent and noncoherent amplify-and-forward relay networks. The problem formulation is such that the output signal-to-noise ratio is maximized under both the aggregate and individual relay power constraints. Our previous results show that these optimization problems can be formulated as quasiconvex optimization problems, and are solved using the bisection method via a sequence of conic feasibility problems. We extend these results to the case of uncertain global CSI, and design robust relay power allocations using the robust optimization methodology. For simple ellipsoidal uncertainty sets, the robust counterparts of these optimization problems are semi-definite programs and can be solved efficiently via interior-point methods.

Relay power allocation has been shown to provide substantial performance gain in wireless relay channels when perfect global channel state information (CSI) is available. In this paper, we show that by using a class of conic optimization theory, we can solve the relay power allocation problem for amplify-and-forward (AF) relay networks in a straightforward manner. The problem formulation is such that the achievable rate with perfect global CSI is maximized under both the aggregate and individual relay power constraints for coherent and noncoherent AF relay networks. Numerical results quantify the performance gain using the optimal relay power allocation for both the coherent and noncoherent AF relay networks.

In this paper, we analyze the effect of the line-of-sight (LOS) on the symbol error probability (SEP) of nonregenerative cooperation in multiple-input multiple-output (MIMO) dual-hop relay channels with nSsource antennas, nRrelay antennas, and no destination antennas-referred to as a (nS,nR,nD)-MIMO dual-hop nonregenerative relay channel. In particular, we consider the channels of the source-to-relay (S-to-R) and the relay-to-destination (R-to-D) links are Rayleigh and Rician fading distribution, respectively. To be specific, we derive the exact SEP for maximum likelihood (ML) decoding of orthogonal space-time block codes (OSTBCs) over such channels and validate the analytical results by comparing with Monte-Carlo simulation. It is shown that for a fixed channel gain a strong LOS component degrades the error performance, e.g. SEP, of MIMO cooperative communications due to the lack of scattering.

In this paper, we present simple opportunistic relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) strategies under an aggregate power constraint. In particular, we consider distributed relay-selection algorithms requiring only local channel knowledge. We show that opportunistic DaF relaying is outage-optimal, that is, it is equivalent in outage behavior to the optimal DaF strategy that employs all potential relays. We further show that opportunistic AaF relaying is outage-optimal among single-relay selection methods and significantly outperforms an AaF strategy based on equal-power multiple-relay transmissions with local channel knowledge. These findings reveal that cooperation offers diversity benefits even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay. Numerical and simulation results are presented to verify our analysis.

In a multiuser scenario, we study the capacity of multiple-input/multiple-output (MIMO) communication systems in the presence of multiple MIMO co-channel interferers. We assume that transmitters have no information about the channel status and that all links undergo Rayleigh distributed fading; no restrictions are made to the transmission power levels or on the number of interferers and antennas, so that many possible cases can be studied, including distributed MIMO. In order to be able to cover all possible scenarios, we generalize the known determinant representation of hypergeometric functions with matrix arguments to the case when the argument matrices have eigenvalues with arbitrary multiplicity. Possible extensions and numerical results are then sketched.

The performance of optimum combining is studied in a Rayleigh fading environment with arbitrary-power cochannel interferers and thermal noise. Based on the joint eigenvalue distributions of quadratic forms in complex Gaussian matrices, closed form expressions for exact moment generating function (MGF) of the output signal-to-interference-plus-noise ratio are derived. From the exact MGF, the moments of the output SINR and the symbol error rate of various M-ary moulation schemes are obtained. We verify the accuracy of our analytical results by numerical examples. The new analytical framework provides a simple and accurate way to assess the effects of equal and unequal-power cochannel interferers and thermal noise on the performance of optimum combining.

In this paper, we present single-selection-opportunistic-relaying with decode-and-forward (DaF) and amplify-and-forward (AaF) protocols under an aggregate power constraint. We show that opportunistic DaF relaying is equivalent to the outage bound of the optimal DaF strategy using all potential relays. We further show that opportunistic AaF relaying is outage-optimal with single-relay selection and significantly outperforms an AaF strategy with multiple-relay (MR) transmissions, in the presence of limited channel knowledge. These findings reveal that cooperative diversity benefits (under an aggregate power constraint) are useful even when cooperative relays choose not to transmit but rather choose to cooperatively listen; they act as passive relays and give priority to the transmission of a single opportunistic relay

We put forth a saddlepoint approximation for the outage capacity of multiple-input multiple-output (MIMO) systems using the exact moment generating function of the capacity. We consider both uncorrelated and spatially correlated Rayleigh-fading channels. Our results show that the saddlepoint method gives a remarkably accurate approximation to the outage capacity even at extremely low outage probabilities

In this paper, we analyze the capacity of multiple-input multiple-output (MIMO) Rayleigh-fading channels in the presence of spatial fading correlation at both the transmitter and the receiver, assuming the channel is unknown at the transmitter and perfectly known at the receiver. We first derive the determinant representation for the exact characteristic function of the capacity, which is then used to determine the trace representations for the mean, variance, skewness, kurtosis, and other higher-order statistics (HOS). These results allow us to exactly evaluate two relevant information-theoretic capacity measures - ergodic capacity and outage capacity - and the HOS of the capacity for such a MIMO channel. The analytical framework presented in the paper is valid for arbitrary numbers of antennas, and generalizes the previously known results for independent and identically distributed or one-sided correlated MIMO channels to the case when fading correlation exists on both sides. We verify our analytical results by comparing them with Monte Carlo simulations for a correlation model based on realistic channel measurements as well as a classical exponential correlation model

In this letter, we present new closed-form formulas for the exact average symbol-error rate (SER) of binary and M-ary signals over Nakagami-m fading channels with arbitrary fading index m. Using the well-known moment generating function-based analysis approach, we express the average SER in terms of the higher transcendental functions such as the Gauss hypergeometric function, Appell hypergeometric function, or Lauricella function. The results are generally applicable to arbitrary real-valued m. Furthermore, with the aid of reduction formulas of hypergeometric functions, we show previously published results for Rayleigh fading (m=1) as special cases of our expressions.

We present a new closed-form formula for the ergodic capacity of multiple-input multiple-output (MIMO) wireless channels. Assuming independent and identically distributed (i.i.d.) Rayleigh flat-fading between antenna pairs and equal power allocation to each of the transmit antennas, the channel capacity is expressed in closed form as finite sums of the exponential integrals which are the special cases of the complementary incomplete gamma function. Using the well-known asymptotic behavior of the MIMO capacity, we also give a simple approximate expression for the channel capacity. Numerical results show that the approximation is quite accurate for the entire range of average signal-to-noise ratios.

In multiple-input multiple-output (MIMO) fading environments, degenerate channel phenomena, so-called keyholes or pinholes, may exist under realistic assumptions that the spatial fading is uncorrelated at the transmitter and the receiver but the channel has a rank-deficient transfer matrix. In this paper, we analyze the average symbol error rate (SER) of a space-time block coded MIMO link in the presence of the keyhole. We also study an effective diversity order, which quantifies the influence of the keyhole on the diversity gain offered by space-time block codes.

The capacity of multiple-input multiple-output (MIMO) wireless channels is limited by both the spatial fading correlation and rank deficiency of the channel. While spatial fading correlation reduces the diversity gains, rank deficiency due to double scattering or keyhole effects decreases the spatial multiplexing gains of multiple-antenna channels. In this paper, taking into account realistic propagation environments in the presence of spatial fading correlation, double scattering, and keyhole effects, we analyze the ergodic (or mean) MIMO capacity for an arbitrary finite number of transmit and receive antennas. We assume that the channel is unknown at the transmitter and perfectly known at the receiver so that equal power is allocated to each of the transmit antennas. Using some statistical properties of complex random matrices such as Gaussian matrices, Wishart (1928) matrices, and quadratic forms in the Gaussian matrix, we present a closed-form expression for the ergodic capacity of independent Rayleigh-fading MIMO channels and a tight upper bound for spatially correlated/double scattering MIMO channels. We also derive a closed-form capacity formula for keyhole MIMO channels. This analytic formula explicitly shows that the use of multiple antennas in keyhole channels only offers the diversity advantage, but provides no spatial multiplexing gains. Numerical results demonstrate the accuracy of our analytical expressions and the tightness of upper bounds.

Space-time block coding is a modulation scheme for the use of multiple transmit antennas providing a simple transmit diversity scheme with the same diversity order as maximal-ratio receiver combining. Using the equivalent single-input single-out (SISO) model, we present a closed-form expression for the exact symbol error rate (SER) of orthogonal space-time block codes (STBC) over flat Rayleigh fading channels.

We investigate an improved upper bound on the bit error probability of turbo codes for the maximum likelihood (ML) decoding with imperfect channel state information (CSI) in a fully interleaved Rayleigh fading channel. The upper bound is based on the Viterbi & Viterbi (see Proc. of the IEEE Inform. Theory Workshop '98, San Diego, CA, Feb. 1998, p.72) bound and is also compared with iterative decoding using the log-MAP algorithm. It is shown that the upper bound well approximates the simulation results of the iterative decoding below a BER of 10/sup -5/.