The performance analysis of maritime communication networks is frequently constrained by the widespread yet unrealistic assumption of a uniform spatial distribution of vessels. This paper presents a unified stochastic geometry framework that integrates high-fidelity physical channel models with nonhomogeneous vessel topologies to provide a more accurate analytical foundation. We first systematically demonstrate that the macroscopic geographical distribution of vessels is a dominant factor governing overall network performance, with significant performance variations observed across different deployment scenarios. Subsequently, to analyze the spatial origins of this performance, we introduce the coverage probability density function (Cpdf) as a novel metric. The Cpdf enables a finegrained spatial decomposition of the total coverage probability, thereby identifying key geographical areas that contribute most to network performance and quantifying the precise spatial impact of physical phenomena, such as multipath fading nulls. Our results affirm the primacy of realistic spatial modeling and provide a new analytical tool for the design and optimization of next-generation maritime networks.

Federated learning (FL) over wireless networks has become a key enabler for privacy-preserving distributed artificial intelligence (AI). However, high learning latency remains a critical bottleneck due to the presence of stragglers, limited wireless resources, and frequent model uploads. While model quantization can mitigate this issue by reducing communication overhead, its effectiveness is sensitive to device heterogeneity and time-varying channel conditions. To address this issue, we proposeFedDamQu, a communication-efficient FL framework with doubly-adaptive model quantization, which dynamically adjusts quantization bit-widths across devices and communication rounds to balance latency and accuracy. Our objective is to maximize the model performance under learning latency constraints. The main contributions are summarized as follows. 1) Convergence Analysis under Unreliable Channels: We derive a novel convergence error upper bound forFedDamQu, which explicitly quantifies the impact of device selection, unreliable transmission, and quantization error on the global model performance, under both fixed and dynamic quantization gain settings. 2) Joint Optimization Framework: Based on the knowledge from the proposed theoretical bound, we formulate a joint mixed integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Efficient Solution Design: The MINLP problem is decomposed into three subproblems, where closed-form solutions for quantization bit-width configuration and bandwidth allocation subproblems are derived, and a lightweight yet effective iterative algorithm is developed to obtain a suboptimal solution for the device selection subproblem. Extensive experiment results validate the theoretical analysis and demonstrate thatFedDamQuconsistently outperforms existing methods in terms of convergence rate and model accuracy, while significantly reducing the overall learning latency.

Future wireless networks will be artificial intelligence (AI) native and highly heterogeneous, where 5G/6G, next generation Wi-Fi, low-altitude drone networks, and satellite networks, have to coexist in the crowded gigahertz and tens of gigahertz frequency bands. This introduces grave challenges in radio resource sharing due to fundamental technical specification discrepancies and complex time-varying interference patterns. As a remedy, we propose a learn-to-share (L2S) framework—a distributed multi-agent paradigm for spectrum sharing among multiple radio access technologies (RATs) that will coexist in the 6G era. In this framework, user terminals (UTs) that are mutually interfering with each other, regardless of which RAT they use, can operate as intelligent agents capable of autonomously making decisions on spectrum access through local sensing and learning, without centralized coordination. We formulate this spectrum sharing problem as a decentralized partially observable Markov decision process (Dec-POMDP) and design a spectral-temporal attention and recurrence (STAR) algorithm as the cognitive engine. STAR decomposes individual agent decisions into three tightly coupled subtasks: channel selection via Transformer networks that capture spatial interference correlations, threshold adaptation through bidirectional long short-term memory (LSTM) networks that model temporal channel dynamics, and waiting time scheduling using predictive LSTM planners that intelligently avoid collisions. Through extensive OMNeT++-based simulations, we demonstrate that STAR significantly outperforms traditional approaches: achieving 73.5% improvement in access success rate, 30.6% enhancement in fairness (Jain’s index), and 88.1% increase in system throughput compared with the conventional listen-before-talk (LBT) mechanism, while requiring 50.6% fewer convergence episodes than the deep recurrent Q-network (DRQN) baseline. Moreover, compared with the mixing Q-network (QMIX), a representative centralized training with decentralized execution (CTDE) baseline, our algorithm reduces the computing power needed per UT by 21.9% while achieving comparable access success rate and system throughput as a fully decentralized multi-agent framework.

In this paper, we study a sampling-based detection strategy for massive multiple-input multiple-output (MIMO) systems, driven by a modified discrete diffusion model formulated as an analytical, non-learning sampling process. Built upon this framework, the proposed discrete diffusion-based sampling (DDS) algorithm improves decoding performance by leveraging residual-dependent sampling, compared to the independent randomized successive interference cancellation (SIC). Specifically, the modified diffusion model incorporates a shortcut perturbation toward the SIC solution, a forward diffusion step to enhance diversity, and step-wise alignment with the perturbed received signal. Within this framework, the DDS algorithm further adopts one-dimensional discrete Gaussian distribution, involving a reformulated discrete Gaussian noise and an explicitly characterized sampling range, but retains computational complexity amenable to practical deployment. Moreover, we theoretically demonstrate an improved expected decoding radius over randomized SIC. Finally, simulation results based on massive MIMO detection are presented to confirm performance gain of the proposed DDS algorithm.

Wireless federated learning (FL) is an emerging artificial intelligence (AI) technique capable of leveraging the data and computing capacity of networked wireless devices while ensuring their individual data privacy and security. However, in geographical areas with poor wireless signal coverage, implementing FL is challenging. Additionally, intensive computation and communication put significant strain on resource-limited wireless devices. To address these issues, firstly, we propose a user equipment (UE)-to-network relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes device scheduling, network topology construction, and multi-type resource allocation to achieve low latency and low energy consumption. The second contribution is threefold. 1) For solving the device scheduling problem, we propose a voting-based strategy to identify the most suitable wireless UEs as relays. 2) Regarding the network topology optimization problem, we derive the optimal solutions under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For solving the multi-type resource allocation problem, we analyze its mathematical structure and propose an iterative algorithm that has significantly lower computational complexity than the traditional method. This algorithm is capable of jointly optimizing the usage of transmission time resource, computing capacity, and transmit power. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

Belief propagation (BP) is a promising technique capable of providing reliable marginal probability distributions for the factor graph (FG) based wireless distributed cooperative positioning (DCP), which is of paramount importance in scenarios lacking global navigation satellite systems. However, BP may fail to converge on FG with short loops and thus only attains a poorly approximate distribution in practical applications. To solve the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network (GNN) enhanced parametric belief propagation (GNN-PBP) approach. We first approximate the nonlinear terms in the FG-based spatio-temporal messages by using Taylor polynomials, thus obtaining high-precision closed-form representations for each message flowing on the FG. The parametric representations of spatial messages are then refined by GNN-based learning. Finally, high-accuracy closed-form expressions for the a posteriori distributions of node positions are derived by inference on the FG. Numerical results demonstrate that our method is able to improve the positioning accuracy for wireless networks that have high density loops.

Massive multi-input multi-output (MIMO) signal processing algorithms heavily rely on high-dimension matrix operations, which impose excessively high computational complexity. Moreover, in the post-Moore era, the performance of the classical von Neumann computing architecture is facing severe limitations. The in-memory computing (IMC) technique holds the potential to break the memory wall and enhance the circuit’s energy efficiency. In this paper, we present an memristor crossbar based IMC circuit design for performing the classical least square (LS) channel estimation with high computation parallelism. Simulation results demonstrate that even when considering the writing and reading errors, the mean square error (MSE) of the proposed circuit with 7-bit memristor is almost the same as that achieved by the digital computer. Moreover, the proposed circuit achieves the same level of computing performance as the NVIDIA RTX 6000 Ada Generation, but with about 1/18 times as low computation time and about 25 times as high energy efficiency, as this benchmark commercial processor.

To address the latency bottleneck in wireless federated learning (FL) systems, we propose FedDamQu, a communication-efficient framework that adaptively adjusts model quantization bit-width across both devices and communication rounds to cope with device heterogeneity and dynamic wireless conditions. Our objective is to maximize global model performance under strict latency constraints. The key contributions are threefold. First, we derive a novel convergence error upper bound that explicitly characterizes the effects of device selection, unreliable transmission, and quantization error. Second, we formulate a joint mixed-integer nonlinear programming (MINLP) problem that integrates device selection, quantization bit-width configuration, and bandwidth allocation to minimize the convergence error under latency constraints. 3) Third, we decompose the MINLP into three tractable subproblems, obtain closed-form solutions for quantization bit-width configuration and bandwidth allocation, and develop a lightweight iterative algorithm for device selection. Extensive experiments demonstrate that FedDamQu consistently outperforms existing methods in terms of convergence speed and model accuracy, while significantly reducing the overall learning latency.

The integrated sensing and communication (ISAC) technology has emerged as an enabler that promises to transform the traditional mobile communication networks into the multifunctional perceptive mobile networks (PMNs), where precise positioning and motion state estimation of network nodes can be achieved relying on wireless communications within the network itself. However, in a practical PMN, multiple types of individually estimated parameters corresponding to multiple sensing targets are not naturally associated with each specific target, which may cause severe obstacles to subsequent signal processing tasks, such as positioning and motion state estimation. To address this challenge, a high-performance low-complexity sensing parameter association algorithm is proposed in this paper. Different from previous works, we first develop a novel spatial filter by exploiting the convolutional beamspace based beamformer to separate paths with different directions of arrival (DOA), and then leverage a low-complexity correlation-based algorithm to associate the DOA estimates with the corresponding paired range-velocity estimates. Extensive simulation results are provided to validate the superior performance of the proposed parameter association algorithm over state-of-the-art schemes.

We investigate the power allocation problem of a dynamic time division duplexing based heterogeneous network comprising downlink (DL) macro base stations (MBSs) and uplink (UL) small base stations (SBSs). In such networks, beyond intracell interference, the asynchronous DL transmission of MBSs and UL transmission of SBSs introduce additional interference known as cross-link interference. This interference occurs not only between MBSs and SBSs, but also between macro-cell user equipment and small-cell user equipment (SUE). To maximize the sum rate of UL and DL, we formulate a non-convex distributed optimization problem where power allocation variables of both DL and UL are to be optimized. We propose a power allocation algorithm relying on the Lagrange method with logarithmic barrier. Simulation results demonstrate that our proposed algorithm outperforms the representative benchmark schemes.

This paper addresses the difficulty of implementing federated learning (FL) in geographical areas with poor wireless signal coverage, and alleviates the high burden imposed by intensive computation and communication on resource-limited wireless devices. Firstly, we propose a user equipment (UE)-tonetwork relay aided FL (UNR-FL) architecture that facilitates a low-cost and flexible implementation of wireless FL, without densifying network equipment deployment. Secondly, we propose an adaptive network control scheme that jointly optimizes resource allocation, network topology construction, and device scheduling, to achieve low latency and low energy consumption. The second contribution is threefold. 1) For allocating resources, we propose a linear-complexity algorithm which is capable of jointly optimizing the transmission time resource and the computing power. 2) For constructing network topology, we derive the optimal closed-form solution under certain conditions, and propose a tabu search based meta-heuristic algorithm to find feasible solutions under the other conditions. 3) For scheduling devices, we propose a voting-based device scheduling algorithm that is near-optimal. Extensive experimental results demonstrate that the proposed UNR-FL architecture and the adaptive network control scheme are capable of substantially reducing the learning latency and the total energy consumption.

The growing demand for reliable and extensive connectivity has made low Earth orbit (LEO) satellites aided Internet of Things (IoT) systems a critical area of research. However, current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial IoT devices to the satellites (T-S) and from the satellites to the Earth station (S-ES), while also accounting for limited satellite coverage and Earth curvature effects. To realistically represent channel conditions, the Nakagami fading model is employed for the T-S links to characterize diverse small-scale fading environments, while the shadowed-Rician fading model is used for the S-ES links to capture the combined effects of shadowing and dominant line-of-sight paths. Furthermore, the analysis incorporates uplink and downlink interference, ensuring a comprehensive evaluation of system performance. The accuracy and effectiveness of our theoretical framework are validated through extensive Monte Carlo simulations. These results provide insights into key performance metrics, such as coverage probability and average ergodic rate, for both individual links and the overall system. Our study also offers an important analytical tool for optimizing the design and performance of IoT-over-LEO satellite systems with the operational constraints that are more realistic.

In harsh environments, such as mountainous terrain, dense vegetation and urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution or increased risk. Therefore, employing multiple types of UAVs to collaborate along with satellite assistance, becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical binomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP) which possesses both properties of exclusivity and clustering. Through simulations, MHCCP has been validated as a more suitable model for UAV groups composed of multiple clusters, compared with traditional point processes such as Poisson point process, binomial point process, and Poisson cluster process. This is because MHCCP ensures inter-cluster repulsion while effectively capturing the clustered distribution observed in practical scenarios. Then, taking into account the influence of terrain shadows on the aerial-satellite links in low-altitude harsh environments, we derive closed-form expressions of the outage probability and average ergodic rate for the aerial-to-satellite uplink of heterogeneous NTNs. Unlike existing studies, our analysis adopts an advanced system configuration that combines beamforming with frequency division multiple access and incorporates a shadowed-Rician fading model to accurately capture signal fading under complex environmental conditions. Furthermore, we investigate link performance in the presence of co-channel interference. Monte Carlo simulations validate that the derived closed-form solutions of the outage probability and the average ergodic rate provide a precise quantitative tool for evaluating the reliability and transmission efficiency of the aerial-satellite links, offering deeper insights into system performance in complex environments.

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical “window function” by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.

Future mobile wireless networks will catalyze substantial demand for precise distributed cooperative positioning (DCP), especially when the global navigation satellite systems are unavailable. However, conventional message passing based DCP methods may suffer considerable performance degradation due to message approximation and sparsity/mobility of nodes. In this paper, we first present a high-accuracy parametric message approximation method, which achieves closed-form representations of all types of messages involved and reduces the computational complexity of message passing procedures. Using these representations, we propose a model- and data-driven hybrid inference approach, dubbed graph neural network enhanced spatio-temporal message passing (GNN-STMP), which fine-tunes parametric messages passed on factor graph and obtains more accuratea posterioridistribution of nodes’ positions by exploiting GNN-generated messages. Furthermore, we develop a universal framework for the parametric message passing based DCP problem, by integrating GNN-STMP with the extend Kalman filter based node’s state prediction and refinement. This framework significantly reduces the positioning ambiguity caused by insufficient spatial ranging measurements from neighbor nodes. Simulation results and analyses demonstrate that, compared with state-of-the-art methods, our proposed approaches achieve the best and near-best positioning accuracy when insufficient and sufficient spatial ranging measurements are available, respectively, while incurring modest computational complexity.

The memristive crossbar array (MCA) has been successfully applied to accelerate matrix computations of signal detection in massive multiple-input multiple-output (MIMO) systems. However, the unique property of massive MIMO channel matrix makes the detection performance of existing MCA-based detectors sensitive to conductance deviations of memristive devices, and the conductance deviations are difficult to be avoided. In this paper, we propose an MCA-based detector circuit, which is robust to conductance deviations, to compute massive MIMO zero forcing and minimum mean-square error algorithms. The proposed detector circuit comprises an MCA-based matrix computing module, utilized for processing the small-scale fading coefficient matrix, and amplifier circuits based on operational amplifiers (OAs), utilized for processing the large-scale fading coefficient matrix. We investigate the impacts of the open-loop gain of OAs, conductance mapping scheme, and conductance deviation level on detection performance and demonstrate the performance superiority of the proposed detector circuit over the conventional MCA-based detector circuit. The energy efficiency of the proposed detector circuit surpasses that of a traditional digital processor by several tens to several hundreds of times.

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.

Parametric message passing (MP) is a promising technique that provides reliable marginal probability distributions for distributed cooperative positioning (DCP) based on factor graphs (FG), while maintaining minimal computational complexity. However, conventional parametric MP-based DCP methods may fail to converge in dense wireless networks due to numerous short loops on FG. Additionally, the use of inappropriate message approximation techniques can lead to increased sensitivity to initial values and significantly slower convergence rates. To address the challenging DCP problem modeled by a loopy FG, we propose an effective graph neural network enhanced fast convergent parametric MP (GNN-FCPMP) method. We first employ Chebyshev polynomials to approximate the nonlinear terms present in the FG-based spatio-temporal messages. This technique facilitates the derivation of globally precise, closed-form representations for each message transmitted across the FG, and reduces MP's sensitivity to initial positional values. Then, the parametric representations of spatial messages are meticulously refined through data-driven GNNs. Conclusively, by performing inference on the FG, we derive more accurate closed-form expressions for the a posteriori distributions of node positions. Numerical results substantiate the capability of GNN-FCPMP to significantly enhance positioning accuracy within wireless networks characterized by high-density loops and ensure rapid convergence.

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical nbinomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP). Our MHCCP possesses both the exclusivity and the clustering properties, thus it can better model the aircraft group composed of multiple clusters. Then, we derive closed-form expressions of the outage probability (OP) for the uplink (aerial-to-satellite) of heterogeneous NTNs. Unlike existing studies, our analysis relies on a more advanced system configuration, where the integration of beamforming and frequency division multiple access, and the shadowed-Rician (SR) fading model for interference power, are considered. The accuracy of our theoretical derivation is confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization of NTNs.

In this paper, we propose a stochastic geometry based innovative model to characterize the impact of the limited-size distribution region of terrestrial terminals in terrestrial-to-aerial networks by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the limited-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the terrestrial-aerial (T-A) links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and unmanned aerial vehicles (UAVs), and conduct a thorough analysis of the coverage probability of the T-A links under Nakagami fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic terrestrial-to-aerial networks involving nomadic aerial base-stations and terrestrial terminals confined in a limited-size region.

Asynchronous radio transceivers often lead to significant range and velocity ambiguity, posing challenges for precise positioning and velocity estimation in passive-sensing perceptive mobile networks (PMNs). To address this issue, carrier frequency offset (CFO) and time offset (TO) synchronization algorithms have been studied in the literature. However, their performance can be significantly affected by the specific choice of the utilized window functions. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We first derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then develop a practical window selection criterion and test a special window generated by the super-resolution algorithm. Numerical simulation has verified our analysis.

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.

Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.

To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts thepost-receptionbeamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O(log10M), while its computational complexity islinearlyscalable to the number of antennasM. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications.

We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for the channel state information of both the eavesdropping and interference channels. Based on the asynchronous distributed pricing model, the proposed scheme maximizes the total utility of all the users, where each user’s utility function is defined as the secrecy rate minus the interference cost imposed on other users. Using the weighted minimum mean square error, Schur complement and sign-definiteness techniques, the original non-concave optimization problem is approximated with high accuracy as a quasi-concave problem, which can be solved by the alternating convex search method. Simulation results consolidate our theoretical analysis and show that the proposed scheme outperforms the artificial noise-assisted interference alignment and minimum total mean-square error-based schemes.

In this paper, the projected-gradient-descent (PGD) -based detector for massive MIMO system, which consists of two basic operations — projection and gradient descent (GD), is studied to achieve the performance improvement. Since the projection and GD step have different loss functions, necessary compromise has to be made to balance them during iterations. For this reason, the generalized PGD (GPGD) method is proposed with flexible choices of projection and GD. Different from performing projection and GD alternatively, we show that implementing projection after every multiple GD steps is a better solution. Meanwhile, the step-size of GD is also investigated for convergence efficiency. After that, by unfolding this proposed GPGD method with deep neural networks (DNN), the self-corrected auto-detector (SAD) is established to achieve better decoding performance, where enhancement by attention mechanism and extension by another iterative method are also given for performance improvement and efficiency.

Perceptive mobile network (PMN) is an emerging concept for next-generation wireless networks capable of conducting integrated sensing and communication (ISAC). A major challenge for realizing high performance sensing in PMNs is how to deal with spatially separated asynchronous transceivers. Asynchronicity results in timing offsets (TOs) and carrier frequency offsets (CFOs), which further cause ambiguity in ranging and velocity sensing. Most existing algorithms mitigate TOs and CFOs based on the line-of-sight (LOS) propagation path between sensing transceivers. However, LOS paths may not exist in realistic scenarios. In this paper, we propose a cooperation based joint active and passive sensing scheme for the non-LOS (NLOS) scenarios having asynchronous transceivers. This scheme relies on the cross-correlation cooperative sensing (CCCS) algorithm, which regards active sensing as a reference and mitigates TOs and CFOs by correlating active and passive sensing information. Another major challenge for realizing high performance sensing in PMNs is how to realize high accuracy angle-of-arrival (AoA) estimation with low complexity. Correspondingly, we propose a low complexity AoA algorithm based on cooperative sensing, which comprises coarse AoA estimation and fine AoA estimation. Analytical and numerical simulation results verify the performance advantages of the proposed CCCS algorithm and the low complexity AoA estimation algorithm.

We propose a geographic and spatio-temporal in-formation based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and non- line-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.

The optimal design of the energy-efficient multiple-input multiple-output (MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is an important but unsolved problem. For such a system, we propose a novel absorbing-Markov-chain-based analysis framework to shed light on the puzzling relationship between the delay and reliability, as well as to quantify the system energy efficiency. We derive the transition probabilities of the absorbing Markov chain considering the Rayleigh fading, the channel estimation error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access, the ACK-enabled retransmissions within the delay bound and the interactions among these technical ingredients. Then, the delay-constrained reliability and the system energy efficiency are derived based on the absorbing Markov chain formulated. Finally, we study the optimal number of user equipments (UEs) and the optimal number of receiving antennas that maximize the system energy efficiency, while satisfying the reliability and latency requirements of URLLC simultaneously. Simulation results demonstrate the accuracy of our theoretical analysis and the effectiveness of massive MIMO in supporting large-scale URLLC systems.

Panoramic video is considered to be an attractive video format, since it provides the viewers with an immersive experience, such as virtual reality (VR) gaming. However, the viewers only focus on part of panoramic video, which is referred to as viewport. Hence, the resources consumed for distributing the remaining part of the panoramic video are wasted. It is intuitive to only deliver the video data within this viewport for reducing the distribution cost. Empirically, viewports within a time interval are highly correlated, hence the historical trajectory may be used for predicting the future viewports. On the other hand, a viewer tends to sustain attention on a specific object in a panoramic video. Motivated by these findings, we propose a deep learning-based viewport Prediction scheme, namely HOP, where the Historical viewport trajectory of viewers and Object tracking are jointly exploited by the long short-term memory (LSTM) networks. Additionally, our solution is capable of predicting multiple future viewports, while a single viewport prediction was supported by the state-of-the-art contributions. Simulation results show that our proposed HOP scheme outperforms the benchmarkers by up to 33.5% in terms of the prediction error.

In this paper, we propose a novel channel quality reporting approach for cellular communication systems. The proposed approach features a differential coding scheme in stationary propagation conditions and a detector of non-stationary propagation conditions, which further triggers a channel-quality predictor for the non-stationary environment based on a machine learning method. In particular, the machine learning engine learns about the specific large variations of the channel quality by collecting signaling information from mobile terminals in a given region. Our simulations in a controlled urban environment with vehicular users show that the proposed solution can effectively replace the 4-bit channel-quality reporting scheme of LTE and NR standards with a 2-bit one, providing correct channel-quality indication in non-stationary conditions with high probability.

In this paper, we investigate the physical layer security of a full-duplex base station (BS)-aided system in the worst case, where an uplink transmitter (UT) and a downlink receiver (DR) are equipped with a single antenna, while a powerful eavesdropper is equipped with multiple antennas. For securing the confidentiality of signals transmitted from the BS and UT, an artificial noise (AN)-aided secrecy beamforming scheme is proposed, which is robust to the realistic imperfect state information of both the eavesdropping channel and the residual self-interference channel. Our objective function is that of maximizing the worst-case sum secrecy rate achieved by the BS and UT, through jointly optimizing the beamforming vector of the confidential signals and the transmit covariance matrix of the AN. However, the resulting optimization problem is non-convex and non-linear. In order to efficiently obtain the solution, we transform the non-convex problem into a sequence of convex problems by adopting the block coordinate descent algorithm. We invoke a linear matrix inequality for finding its Karush-Kuhn-Tucker (KKT) solution. In order to evaluate the achievable performance, the worst-case secrecy rate is analytically derived. Furthermore, we construct another secrecy transmission scheme using the projection matrix theory for performance comparison. Our simulation results show that the proposed robust secrecy transmission scheme achieves substantial secrecy performance gains, which verifies the efficiency of the proposed method.

We study the detection of misbehavior in a Gaussian relay system, where the source transmits information to the destination with the assistance of an amplify-and-forward relay node subject to unreliable channel state information (CSI). The relay node may be potentially malicious and corrupt the network by forwarding garbled information. In this situation, misleading feedback may take place, since reliable CSI is unavailable at the source and/or the destination. By classifying the action of the relay as detectable or undetectable, we propose a novel approach that is capable of coping with any malicious attack detected and continuing to work effectively in the presence of unreliable CSI. We demonstrate that the detectable class of attacks can be successfully detected with a high probability. Meanwhile, the undetectable class of attacks does not affect the performance improvements that are achievable by cooperative diversity, even though such an attack may fool the proposed detection approach. We also extend the method to deal with the case in which there is no direct link between the source and the destination. The effectiveness of the proposed approach has been validated by numerical results.

In this paper, we consider a surveillance scenario where a team of sensing robots survey a sensitive area and transmit the monitored data to a remote base station through a mobile relay. In this scenario, it is challenging to autonomously adjust the position of the mobile relay for the sake of minimizing the total communication-motion energy consumption of the system, while maintaining the communication quality of the mobile sensing robots. We first derive the asymptotically optimal transmit powers of the mobile relay and of the sensing robots according to the predefined end-to-end packet error rate (PER) requirement. Then, we propose a joint communication-motion planning (JCMP) method for minimizing the total communication-motion energy consumption in both: single- and multi-sensing-robot scenarios, where the trajectories of the sensing robots are rigorously defined. We further consider the scenario where the sensing robots' trajectories are not fixed but can be optimized in restrained areas. The effectiveness of the proposed JCMP is verified by analysis and numerical results for different system configurations, showing that a substantial energy-efficiency improvement may be achieved in comparison with the benchmark that only optimizes the communication energy consumption.

We investigate power allocation optimization for global energy efficiency (GEE) maximization in the massive multiple-input multiple-output technique aided multi-pair one-way decode-and-forward relay systems. Assuming that the minimum mean-square error channel estimator and zero-forcing transceivers are employed at the relay, we first derive an accurate closed-form expression of the GEE of this complex system. Based on our analytical results, a non-convex power allocation optimization problem with the objective of GEE maximization is formulated under specific quality-of-service (QoS) and transmit power constraints. To solve this challenging problem, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by Dinkelbach's method and by the Charnes-Cooper transformation-based method. In addition, as a special case, the GEE maximization problem under the assumption of using the equal power allocation strategy at both the source users and the relay is also considered. Simulation results demonstrate the accuracy of our analytical results and the effectiveness of the proposed algorithms. Furthermore, the impact of several important system parameters (i.e., the QoS constraint, the transmit power constraints at both the source users and the relay, as well as the quality of channel estimation) on the maximum GEE achieved by the proposed algorithms is also illustrated.

A novel transmission protocol is conceived for a multi-user, multi-relay, multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-single-output (MISO) channels, which are spatially multiplexed for transmission. For the sake of improving the attainable capacity, these MISO channels are grouped using a pair of novel grouping algorithms, which are then evaluated in terms of their performance versus complexity trade-off1.

In this paper, a generalized low-complexity beamspace approach is proposed for two-dimensional localization of incoherently distributed sources with a uniform cylindrical array (UCyA) in large scale/massive multiple-input multiple-output (MIMO) systems. The received signal vectors in the antenna-element space are transformed into the beamspace by employing beamforming vectors. As a beneficial result, the total dimensions of the received signal vectors are significantly reduced. In addition, it is shown that the error introduced by the transformation decreases as the number of UCyA antennas increases. The UCyA is composed of multiple uniform circular arrays (UCAs), and the beamspace array response matrices of adjacent UCAs are linearly related. Then, the linear relation is exploited to estimate the nominal elevation direction-of-arrivals (DOAs) directly and the nominal azimuth DOAs based on a low-complexity search algorithm. In contrast, the linear relation in the traditional approach is based on approximations and the associated search algorithm is more complicated. Numerical results demonstrate that the proposed approach outperforms the existing approach in terms of both performance and complexity in the context of massive MIMO systems.

We consider an amplify-and-forward relay network composed of a source, two relays, and a destination. In this network, the two relays are untrusted in the sense that they may perform Byzantine attacks by forwarding altered symbols to the destination. Note that every symbol received by the destination may be altered, and hence, no clean reference observation is available to the destination. For this network, we identify a large family of Byzantine attacks that can be detected in the physical layer. We further investigate how the channel conditions impact the detection against this family of attacks. In particular, we prove that all Byzantine attacks in this family can be detected with asymptotically small miss detection and false alarm probabilities by using a sufficiently large number of channel observations if and only if the network satisfies a non-manipulability condition. No pre-shared secret or secret transmission is needed for the detection of these attacks, demonstrating the value of this physical-layer security technique for counteracting Byzantine attacks.

In this paper, we consider the problem of jointly designing transmit precoding (TPC) matrix and receive filter matrix subject to both secrecy and per-transmitter power constraints in the multiple-input multiple-output (MIMO) interference channel, where K legitimate transmitter-receiver pairs communicate in the presence of an external eavesdropper. Explicitly, we jointly design the TPC and receive filter matrices based on the minimum total mean-squared error (MSE) criterion under a given and feasible information-theoretic degrees of freedom. More specifically, we formulate this problem by minimizing the total MSEs of the signals communicated between the legitimate transmitter-receiver pairs, while ensuring that the MSE of the signals decoded by the eavesdropper remains higher than a certain threshold. We demonstrate that the joint design of the TPC and receive filter matrices subject to both secrecy and transmit power constraints can be accomplished by an efficient iterative distributed algorithm. The convergence of the proposed iterative algorithm is characterized as well. Furthermore, the performance of the proposed algorithm, including both its secrecy rate and MSE, is characterized with the aid of numerical results. We demonstrate that the proposed algorithm outperforms the traditional interference alignment algorithm in terms of both the achievable secrecy rate and the MSE. As a benefit, secure communications can be guaranteed by the proposed algorithm for the MIMO interference channel even in the presence of a sophisticated/strong eavesdropper, whose number of antennas is much higher than that of each legitimate transmitter and receiver.

Soft noncoherent detection, which relies on calculating the a posteriori probabilities (APPs) of the bits transmitted with no channel estimation, is imperative for achieving excellent detection performance in high-dimensional wireless communications. In this paper, a high-performance belief propagation (BP)-based soft multiple-symbol differential detection (MSDD) framework, dubbed BP-MSDD, is proposed with its illustrative application in differential space-time block-code(DSTBC)-aided ultra-wideband impulse radio (UWB-IR) systems. First, we revisit the signal sampling with the aid of a trellis structure and decompose the trellis into multiple subtrellises. Furthermore, we derive an APP calculation algorithm, in which the forward-and-backward message passing mechanism of BP operates on the subtrellises. The proposed BP-MSDD is capable of significantly outperforming the conventional hard-decision MSDDs. However, the computational complexity of the BP-MSDD increases exponentially with the number of MSDD trellis states. To circumvent this excessive complexity for practical implementations, we reformulate the BP-MSDD, and additionally propose a Viterbi algorithm-based hard-decision MSDD (VA-HMSDD) and a VA-based soft-decision MSDD (VA-SMSDD). Moreover, both the proposed BP-MSDD and VA-SMSDD can be exploited in conjunction with soft channel decoding to obtain powerful iterative detection and decoding-based receivers. Simulation results demonstrate the effectiveness of the proposed algorithms in DSTBC-aided UWB-IR systems.

There is a paucity of random access protocols designed for alleviating collisions in visible light communication (VLC) systems, where carrier sensing is hard to achieve due to the directionality of light. To resolve the problem of collisions, we adopt the successive interference cancellation (SIC) algorithm to enable the coordinator to simultaneously communicate with multiple devices, which is referred to as the multi-packet reception (MPR) capability. However, the MPR capability could be fully utilized only when random access algorithms are properly designed. Considering the characteristics of the SIC aided random access VLC system, we propose a novel effective capacity (EC)-based ALOHA-like distributed random access algorithm for MPR-aided uplink VLC systems having heterogeneous quality-of-service (QoS) guarantees. First, we model the VLC network as a conflict graph and derive the EC for each device. Then, we formulate the VLC QoS-guaranteed random access problem as a saturation throughput maximization problem subject to multiple statistical QoS constraints. Finally, the resultant non-concave optimization problem is solved by a memetic search algorithm relying on invasive weed optimization and differential evolution. We demonstrate that our derived EC expression matches the Monte Carlo simulation results accurately, and the performance of our proposed algorithms is competitive.

In this paper, we pioneer the study of physical-layer security in heterogeneous networks (HetNets). We investigate secure communications in a two-tier downlink HetNet, which comprises one macrocell and several femtocells. Each cell has multiple users and an eavesdropper attempts to wiretap the intended macrocell user. First, we consider an orthogonal spectrum allocation strategy to eliminate co-channel interference, and propose the secrecy transmit beamforming only operating in the macrocell (STB-OM) as a partial solution for secure communication in HetNet. Next, we consider a secrecy-oriented non-orthogonal spectrum allocation strategy and propose two cooperative STBs which rely on the collaboration amongst the macrocell base station (MBS) and the adjacent femtocell base stations (FBSs). Our first cooperative STB is the STB sequentially operating in the macrocell and femtocells (STB-SMF), where the cooperative FBSs individually design their STB matrices and then feed their performance metrics to the MBS for guiding the STB in the macrocell. Aiming to improve the performance of STB-SMF, we further propose the STB jointly designed in the macrocell and femtocells (STB-JMF), where all cooperative FBSs feed channel state information to the MBS for designing the joint STB. Unlike conventional STBs conceived for broadcasting or interference channels, the three proposed STB schemes all entail relatively sophisticated optimizations due to QoS constraints of the legitimate users. To efficiently use these STB schemes, the original optimization problems are reformulated and convex optimization techniques, such as second-order cone programming and semidefinite programming, are invoked to obtain the optimal solutions. Numerical results demonstrate that the proposed STB schemes are highly effective in improving the secrecy rate performance of HetNet.

We first conceive a novel transmission protocol for a multi-relay multiple-input-multiple-output orthogonal frequency-division multiple-access (MIMO-OFDMA) cellular network based on joint transmit and receive beamforming. We then address the associated network-wide spectral efficiency (SE) and energy spectral efficiency (ESE) optimization problems. More specifically, the network's MIMO channels are mathematically decomposed into several effective multiple-input-single-output (MISO) channels, which are essentially spatially multiplexed for transmission. Hence, these effective MISO channels are referred to as spatial multiplexing components (SMCs). For the sake of improving the SE/ESE performance attained, the SMCs are grouped using a pair of proposed grouping algorithms. The first is optimal in the sense that it exhaustively evaluates all the possible combinations of SMCs satisfying both the semi-orthogonality criterion and other relevant system constraints, whereas the second is a lower-complexity alternative. Corresponding to each of the two grouping algorithms, the pair of SE and ESE maximization problems are formulated, thus the optimal SMC groups and optimal power control variables can be obtained for each subcarrier block. These optimization problems are proven to be concave, and the dual decomposition approach is employed for obtaining their solutions. Relying on these optimization solutions, the impact of various system parameters on both the attainable SE and ESE is characterized. In particular, we demonstrate that under certain conditions the lower-complexity SMC grouping algorithm achieves 90% of the SE/ESE attained by the exhaustive-search based optimal grouping algorithm, while imposing as little as 3.5% of the latter scheme's computational complexity.

This contribution presents a method of obtaining the optimal power and subcarrier allocations that maximize the energy-efficiency (EE) of a multi-user, multi-relay, orthogonal frequency division multiple access (OFDMA) cellular network. Initially, the objective function (OF) is formulated as the ratio of the spectral-efficiency (SE) over the power consumption of the network. This OF is shown to be quasi-concave, thus Dinkelbach's method can be employed for solving it as a series of parameterized concave problems. We characterize the performance of the aforementioned method by comparing the optimal solutions obtained to those found using an exhaustive search. Additionally, we explore the relationship between the achievable SE and EE in the cellular network upon increasing the number of active users. In general, increasing the number of users supported by the system benefits both the SE and EE, and higher SE values may be obtained at the cost of EE, when an increased power may be allocated.

In our previous work, it was shown that the conventional approximate Bayes' theorem based probabilistic data association (PDA) algorithms output “nominal APPs”, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayes' theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using M-ary modulation. Furthermore, we demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we show that the EB-Log-PDA based IDD scheme operating without any inner PDA iterations has a similar performance to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.

In order to deal with uneven load distribution, mobility load balancing adjusts the handover region to shift edge users from a hot-spot cell to the less-loaded neighbouring cells. However, shifted users receive the reduced signal power from neighbouring cells, which may result in link quality degradation. This paper employs a user relaying model and proposes a user relay assisted traffic shifting (URTS) scheme to address this problem. In URTS scheme, a shifted user selects a suitable non-active user as relay user to forward signal, thus enhancing the link quality of the shifted user. Since the user relaying model consumes relay user's energy, a utility function is designed in relay selection to reach a trade-off between the shifted user's link quality improvement and the relay user's energy consumption. Simulation results show that the URTS scheme can improve SINR and capacity of shifted users. Also, URTS scheme keeps the cost of relay user's energy consumption at an acceptable level.

In this paper, the joint power and subcarrier allocation problem is solved in the context of maximizing the energy-efficiency (EE) of a multi-user, multi-relay orthogonal frequency division multiple access (OFDMA) cellular network, where the objective function is formulated as the ratio of the spectral-efficiency (SE) over the total power dissipation. It is proven that the fractional programming problem considered is quasi-concave so that Dinkelbach's method may be employed for finding the optimal solution at a low complexity. This method solves the above-mentioned master problem by solving a series of parameterized concave secondary problems. These secondary problems are solved using a dual decomposition approach, where each secondary problem is further decomposed into a number of similar subproblems. The impact of various system parameters on the attainable EE and SE of the system employing both EE maximization (EEM) and SE maximization (SEM) algorithms is characterized. In particular, it is observed that increasing the number of relays for a range of cell sizes, although marginally increases the attainable SE, reduces the EE significantly. It is noted that the highest SE and EE are achieved, when the relays are placed closer to the BS to take advantage of the resultant line-of-sight link. Furthermore, increasing both the number of available subcarriers and the number of active user equipment (UE) increases both the EE and the total SE of the system as a benefit of the increased frequency and multi-user diversity, respectively. Finally, it is demonstrated that as expected, increasing the available power tends to improve the SE, when using the SEM algorithm. By contrast, given a sufficiently high available power, the EEM algorithm attains the maximum achievable EE and a suboptimal SE.

It was conventionally regarded that the approximate Bayesian theorem based existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In our recent work, however, we demonstrated that these probabilities are not the true APPs in the rigorous mathematical sense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, in this paper we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input-multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the EB-Log-PDA algorithm's inner iteration on the design of EB-Log-PDA aided IDD receiver. We demonstrate that introducing inner iterations into EB-Log-PDA, which is common practice in conventional-PDA aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratios (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. Despite their difference in extrinsic LLRs, we also show that the IDD schemes employing the EB-Log-PDA and the AB-Log-PDA without incorporating any inner PDA iterations have a similar achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users' modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large.

In this paper, the design of iterative detection and decoding (IDD) schemes relying on a low-complexity probabilistic data association (PDA) aided method is conceived for turbo-coded multiple-input multiple-output (MIMO) systems communicating over Nakagami-m fading channels. The known PDA based MIMO detectors typically operate purely in the probability-domain. We show that the classic relationship where the extrinsic LLRs are given by subtracting the a priori LLRs from the a posteriori LLRs does not hold for the existing PDA based MIMO detectors. Therefore, the PDA method is not readily applicable to the IDD receiver. To overcome this predicament, we propose an approximate Bayesian theorem based log-domain PDA (AB-Log-PDA) detector, as well as a novel simple approach of calculating the bit-wise extrinsic LLRs for the AB-Log-PDA, which makes the AB-Log-PDA well-suited for employment in IDD receivers. It is shown that the proposed AB-Log-PDA based IDD scheme is capable of achieving a comparable performance to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a much lower computational complexity in the scenarios considered.

Combining of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) can significantly improve the performance of the wireless two-way relay network (TWRN). This paper proposes novel Max-Min optimization based relay antenna selection (RAS) schemes for zero-forcing (ZF) based MIMO-PNC transmission. RAS relaxes ZF's constraints on the number of antennas and extends the applications of ZF based MIMO-PNC to more practical scenarios, where the dedicated relay has more antennas than the end node. Moreover, RAS also brings diversity advantages to TWRN and the achievable diversity gains of the proposed schemes are theoretically analyzed. In particular, an equivalence relation is carefully built for the diversity gains obtained by 1) RAS for ZF based MIMO-PNC and 2) transmit antenna selection (TAS) for MIMO broadcasting (BC) with ZF receivers. This equivalence transforms the original problem to a more tractable form which eventually allows explicit analytical results. It is interesting to see that Max-Min RAS keeps the network diversity gain of ZF based MIMO-PNC to be the same as the diversity gain of the point-to-point link within the TWRN. This insight extends the understanding on the behaviors of ZF transceivers with antenna selection (AS) to relatively complicated MIMO-TWRN/BC scenarios.

An efficient semidefinite programming relaxation (SDPR) based virtually antipodal (VA) detection approach is proposed for Gray coded 16-QAM signalling over multiple-input--multiple-output (MIMO) channels. The existing index-bit-based VA-SDPR (IVA-SDPR) method is incapable of making direct binary decisions concerning the individual information bits without making symbol decisions first, except for the linear natural-mapping aided rectangular QAM constellations. By contrast, our new method is capable of directly deciding on the information bits of the ubiquitous Gray-mapping aided 16-QAM by employing a strikingly simple linear matrix representation (LMR) of 4-QAM. As an appealing benefit, the conventional "signal-to-symbol-to-bits'' decision process is substituted by a simpler "signal-to-bits'' decision process for the classic Gray-mapping aided rectangular 16-QAM. Furthermore, when combined with low-complexity bit-flipping based "hill climbing'', the proposed direct-bit-based VA-SDPR (DVA-SDPR) detector achieves the best bit-error-ratio (BER) performance among the known SDPR-based MIMO detectors in the context considered, while still maintaining a worst-case complexity order as low as O[(4NT+1)3.5].

Inter-cell co-channel interference (CCI) mitigation is investigated in the context of cellular systems relying on dense frequency reuse. A distributed Base Station (BS) cooperation aided soft reception scheme using the Probabilistic Data Association (PDA) algorithm and Soft Combining (SC) is proposed for the uplink of multi-user multi-cell MIMO systems. The realistic hexagonal cellular model relying on unity Frequency Reuse (FR) is considered, where both the BSs and the Mobile Stations (MSs) are equipped with multiple antennas. Local cooperation based message passing is used instead of a global message passing chain for the sake of reducing the backhaul traffic. The PDA algorithm is employed as a low complexity solution for producing soft information, which facilitates the employment of SC at the individual BSs in order to generate the final soft decision metric. Our simulations and analysis demonstrate that despite its low additional complexity and backhaul traffic, the proposed distributed PDA-aided reception scheme significantly outperforms the conventional non-cooperative benchmarkers.

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear Gray bit-to-symbol mapping schemes are considered. Our analytical and simulation results demonstrate that the linear natural mapping based B-PDA approach attains an improved detection performance, despite dramatically reducing the computational complexity in contrast to the conventional symbol-based PDA aided MIMO detector. Furthermore, it is shown that the linear natural mapping based B-PDA method is capable of approaching the lower bound performance provided by the nonlinear Gray mapping based B-PDA MIMO detector. Since the linear natural mapping based scheme is simpler and more applicable in practice than its nonlinear Gray mapping based counterpart, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping.

Directional antennas in ad hoc networks offer more benefits than the traditional antennas with omni-directional mode. With directional antennas, it can increase the spatial reuse of the wireless channel. A higher gain of directional antennas makes terminals a further transmission range and fewer hops to the destination. This paper presents the design, implementation and simulation results of a multi-channel Medium Access Control (MAC) protocols for dense Vehicular Ad hoc Networks using directional antennas with local beam tables. Numeric results show that our protocol performs better than the existing multichannel protocols in vehicular environment.

A new probabilistic data association (PDA) approach is proposed for symbol detection in spatial multiplexing multiple-input multiple-output (MIMO) systems. By designing a joint detection (JD) structure for consecutive symbol vectors in the same transmit burst, more a priori information is exploited when updating the estimated posterior marginal probabilities for each symbol per iteration. Therefore the proposed PDA detector (denoted as PDA-JD detector) outperforms the conventional PDA detectors in the context of correlated input bit streams. Moreover, the conventional PDA detectors are shown to be a special case of the PDA-JD detector. Simulations and analyses are given to demonstrate the effectiveness of the new method.