This work considers a spatially non-stationary channel tracking problem in broadband extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. In the case of spatial non-stationarity, each scatterer has a certain visibility region (VR) over antennas and power change may occur among visible antennas. Concentrating on the temporal correlation of XL-MIMO channels, we design a three-layer Markov prior model and hierarchical two-dimensional (2D) Markov model to exploit the dynamic sparsity of sparse channel vectors and VRs, respectively. Then, we formulate the channel tracking problem as a bilinear measurement process, and develop a novel dynamic alternating maximum a posteriori (DA-MAP) method to solve the problem. DA-MAP contains four core modules: channel estimation module, VR detection module, grid update module, and temporal processing module. Specifically, the first module is an inverse-free variational Bayesian inference (IF-VBI) estimator that avoids computationally intensive matrix inverse in each iteration; the second module is a turbo compressive sensing (Turbo-CS) algorithm that only needs small-scale matrix operations in a parallel fashion; the third module refines the polar-delay domain grid; and the fourth module can process the temporal prior information to ensure high-efficiency channel tracking. Simulation results demonstrate that the proposed method achieves significant improvements in channel tracking performance with low computational overhead.

In this paper, we propose an integrated sensing and communication (ISAC) system enabled by movable antennas (MAs), where the base station (BS) transmitter is equipped with MAs to enhance both sensing and communication performance. To characterize the benefits of MA-enabled ISAC systems, we focus on the line-of-sight (LoS) channel scenario and derive the Cramér-Rao bound (CRB) for angle estimation error, which is then minimized by jointly optimizing the antenna position vector (APV) and beamforming design, subject to a pre-defined signal-to-noise ratio (SNR) constraint to ensure the communication performance. Despite the non-convexity of the resulting problem, we develop a boundary traversal breadth-first search (BT-BFS) algorithm to obtain the global optimal solution, along with a lower-complexity boundary traversal depth-first search (BT-DFS) algorithm to find a local optimal solution efficiently. Extensive numerical results are presented to verify the effectiveness of the proposed algorithms, and demonstrate the superiority of the considered MA-enabled ISAC system over conventional ISAC systems with fixed-position antennas (FPAs).

Integrated sensing and communication (ISAC) in multi-hop wireless networks is a key technology for supporting a wide range of emerging Internet of Things (IoT) applications, addressing challenges such as spectrum scarcity and limited network coverage. To investigate the performance trade-off between end-to-end communication and sensing in these networks, this paper focuses on maximizing the end-to-end communication rate while ensuring the overall sensing performance in multiple-input multiple-output (MIMO) ISAC multi-hop wireless networks. In order to achieve this, we formulate a mixed-integer nonlinear programming (MINLP) problem that is highly non-convex and difficult to solve directly. To address this difficulty, we first transform the MINLP problem into a more tractable form through a series of equivalent transformations. We then propose an efficient algorithm based on generalized Benders decomposition (GBD) to solve the transformed problem optimally. Finally, numerical results demonstrate that the proposed algorithm achieves the optimal performance obtained by the exhaustive search method but with much lower complexity.

Positioning and sensing, as critical enablers for emerging applications, can be further empowered by multi-band fusion technology. In this paper, a two-stage framework utilizing position-domain joint stochastic particle variational Bayesian inference (PD-JSPVBI) is proposed to address the key challenges in time-of-arrival (TOA)-based direct position determination. Unlike existing works focusing on multi-band delay estimation or single-band direct positioning, our unified framework enables joint target localization directly from multi-station, multiband signals. The algorithm integrates prior information and resolves spatial coordinate coupling via a novel two-dimensional particle-based variational approximation, significantly improving estimation efficiency. Additionally, a joint estimation mechanism is introduced to synchronously optimize positioning parameters (e.g., target coordinates) and non-ideal factors (e.g., timing synchronization errors, random initial phases) within a unified variational framework. Simulation results validate the proposed algorithm's superiority, outperforming conventional cascaded architectures by eliminating error propagation and leveraging multi-band coherence. The proposed solution offers a promising pathway for high-precision direct positioning systems.

We investigate a joint scattering environment sensing, channel estimation, and data recovery problem in an uplink integrated sensing and communication (ISAC) system. Based on a three-dimensional (3D) location-domain sparse channel model, the joint problem is formulated as a bilinear sparse recovery problem with a dynamic position grid and imperfect parameters. We propose an expectation maximization based bilinear subspace variational Bayesian inference (EM-BiSVBI) algorithm to solve the problem effectively, where the E-step performs Bayesian estimation of the the location-domain sparse channel and transmitted data, and the M-step refines the dynamic position grid and learns the imperfect factors via gradient update. In particular, the BiSVBI algorithm in the E-step avoids the high-dimensional matrix inverse by a subspace constrained approach while ensuring convergence to a stationary solution of the Kullback-Leibler divergence minimization problem. Simulations verify the advantages of the proposed method over baselines.

This paper presents STS-T, a novel spatial-temporal-spectral transformer for end-to-end prediction of jamming spectrum situations. To address challenges such as node mobility, legitimate interference, and noisy or missing data, STS-T employs spatiotemporal positional encoding and axial attention mechanisms that effectively capture global features while reducing computational complexity. Specifically, the model integrates a Temporal Predictive Decoder with dilated convolution for improved temporal forecasting, and a Spatial Interpolation Decoder with cross-attention to seamlessly map between input and output spaces. A synthetic dataset generated via OMNeT++ simulations is also introduced to compensate for the lack of real-world UAV jamming data. Experimental results confirm that STS-T outperforms existing baselines, providing a robust solution for predicting jamming spectrum situations in complex UAV swarm environments.

Affine Frequency Division Multiplexing (AFDM) is an emerging multicarrier modulation technique well-suited for high-mobility scenarios in next-generation wireless systems. By achieving delay-Doppler orthogonality in a twisted time-frequency domain, AFDM offers robust and efficient communication while enabling integrated sensing and communication through precise environmental parameter estimation. However, a major drawback of AFDM is its high peak-to-average power ratio (PAPR), which can cause power amplifier saturation and degrade system reliability. To address this issue, we propose an autoencoder-based PAPR reduction scheme leveraging convolutional neural networks, which effectively lowers the PAPR while maintaining satisfactory bit error rate (BER) performance. Simulation results under various modulation schemes and linear time-varying channels demonstrate that the proposed approach outperforms conventional methods in both PAPR reduction and BER performance.

This work investigates the potential of exploiting movable antennas (MAs) to enhance the performance of a multiuser downlink integrated sensing and communication (ISAC) system. Specifically, we formulate an optimization problem to maximize the transmit beampattern gain for sensing while simultaneously meeting each user's communication requirement by jointly optimizing antenna positions and beamforming design. The problem formulated is highly non-convex and involves multivariate-coupled constraints. To address these challenges, we introduce a series of auxiliary random variables and transform the original problem into an augmented Lagrangian problem. A double-loop algorithm based on a penalty dual decomposition framework is then developed to solve the problem. Numerical results validate the effectiveness of the proposed design, demonstrating its superiority over MA designs based on successive convex approximation optimization and other baseline approaches in ISAC systems. The results also highlight the advantages of MAs in achieving better sensing performance and improved beam control, especially for sparse arrays with large apertures.

The Barrage Relay Network (BRN) is a mobile ad hoc network architecture designed for tactical edge communication, emphasizing robustness and low-latency data delivery. In BRNs, unicast transmission is confined to a Controlled Barrage Region (CBR) established via cooperative communication. However, dynamic channel conditions can impair the successful formation of CBRs, thereby reducing transmission reliability. Increasing the excess width parameter can improve connectivity but at the cost of significant node redundancy. To address this tradeoff, we propose a deep Q-network (DQN)-based relay selection algorithm utilizing a multi-step temporal difference (TD) target. The proposed scheme aims to minimize relay node usage while preserving CBR reliability. To enable effective decision-making, an extended routing state is introduced, along with two specialized routing packets that facilitate efficient information dissemination. The action space, state space, and reward function are carefully defined to incorporate both local and global network state information. Simulation results show that the proposed method reduces the number of active relay nodes by 66.67%, while maintaining a 100% packet delivery ratio. Furthermore, the algorithm demonstrates strong adaptability across varying network topologies.

Barrage Relay Networks (BRNs), a subclass of mobile ad hoc networks (MANETs), are tailored for tactical edge environments where broadcast-based communication is pre-dominant. By establishing Controlled Barrage Regions (CBRs), BRNs enable spatial reuse and facilitate pipeline-based packet forwarding. This paper investigates unicast transmission within a formed CBR, where a source node generates status updates at fixed intervals and relays them to a destination node via a multihop network. The timeliness of update delivery, quantified by the Age of Information (AoI), is analyzed using a stochastic hybrid system (SHS) framework. Additionally, a version age analysis is conducted for a parallel relay transmission topology, highlighting the tradeoff between information freshness and node utilization.

This paper analyzes the impact of beam alignment errors (BAE) on directional ad hoc networks and proposes a new angular error model that derives angular errors from positional errors, overcoming limitations of existing models that assume fixed angular error variance. We derive the probability density function (PDF) of antenna gain for both flat-top and Gaussian antenna models and analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability in Poisson networks. Numerical results show that the new model more accurately captures the effects of BAE on SINR coverage and main lobe alignment probability. The proposed model provides a more realistic understanding of beam misalignment effects in practical network scenarios.

Integrated Sensing and Communication (ISAC) technology facilitates simultaneous reliable communication and high-precision sensing performance in vehicular networks. Many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Aiming to minimize the Cramér-Rao bound (CRB) for estimating vehicle's angle, distance and velocity while meeting communication rate requirements, we propose a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the superiority of the proposed algorithm and scheme.

Due to network delays and scalability limitations, clustered ad hoc networks widely adopt Reinforcement Learning (RL) for on-demand resource allocation. Albeit its demonstrated agility, traditional Model-Free RL (MFRL) solutions struggle to tackle the huge action space, which generally explodes exponentially along with the number of resource allocation units, enduring low sampling efficiency and high computational complexity. To mitigate these limitations, Model-Based RL (MBRL) offers a solution by generating simulated samples through an environment model, which boosts sample efficiency and stabilizes the training by avoiding extensive real-world interactions. However, establishing an accurate dynamic model for complex and noisy environments necessitates a careful balance between model accuracy and computational complexity & stability. To address these issues, we propose a conditional Diffusion Model (DM) as high-dimensional offline resource allocation planner in multifrequency time division multiple access (MF-TDMA) wireless ad hoc networks. By leveraging the astonishing generative capability of DMs, our approach takes advantage of generated high-quality samples to guide exploration and learn optimal policy. Extensive experiments show that our model outperforms MFRL in average reward and Quality of Service (QoS) while demonstrating comparable performance to other MBRL algorithms.

We investigate a joint visibility region (VR) detection and channel estimation problem in extremely large-scale multiple-input-multiple-output (XL-MIMO) systems, where nearfield propagation and spatial non-stationary effects exist. In this case, each scatterer can only see a subset of antennas, i.e., it has a certain VR over the antennas. A novel alternating maximum a posteriori (MAP) framework is developed for high-accuracy VR detection and channel estimation, which consists of three basic modules: a channel estimation module, a VR detection module, and a grid update module. Specifically, the first module is a low-complexity inverse-free variational Bayesian inference (IF-VBI) algorithm that avoids the matrix inverse via minimizing a relaxed Kullback-Leibler (KL) divergence. The second module is an expectation propagation (EP) algorithm that can recover binary VRs. And the third module refines polar-domain grid parameters via gradient ascent. Simulations demonstrate the superiority of the proposed algorithm in both VR detection and channel estimation.

In this article, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input-multiple-output (MIMO) radar system and investigate its sensing resolution. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width in the angular domain is characterized. We discover that this minimum width is related to the antenna size, the antenna number, and the target angle. Meanwhile, we present lower bounds of the ambiguity function in the Doppler and delay domains, and show that the impact of the antenna size on the radar performance in these two domains is very different from that in the angular domain. Moreover, the performance enhancement brought by MAs exhibits a certain tradeoff between the main lobe width and the side lobe peak levels. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method, and show that its performance is very close to the baseline. Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate that MAs can reduce the main lobe width and suppress the side lobe levels of the ambiguity function, thereby enhancing radar performance.

In millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable beam alignment is essential for maximizing beamforming gain during data transmission. As XL-MIMO systems integrate increasingly larger antenna arrays, the electromagnetic propagation field transitions from the far-field to the near-field regime. This shift requires beam training methods that account for both angular and distance dimensions. Although recent advancements have been made in near-field beam training, existing approaches still face challenges, including high training overhead and limited robustness. The advent of advanced wireless systems has enabled the acquisition of user position information through various techniques, presenting opportunities to reduce training overhead in near-field scenarios. In this paper, we propose an adaptive Position-Aware Beam Training (PABT) algorithm designed for near-field XL-MIMO systems. The PABT algorithm explicitly incorporates position errors into its design to enhance robustness. Specifically, it begins with an initialization phase that uses noisy position estimates to narrow the beam search space. Following this, a multi-round iterative measurement approach is employed, where Bayes’ rule is used to update the posterior distribution of the polar-domain codebook based on the measurements obtained. This approach dynamically refines the candidate beam until a predefined probability threshold or a maximum number of iterations is reached. For the PABT proposed, we also derive a theoretical lower bound on the training overhead required to achieve a given probability threshold. This analysis provides valuable insights for selecting the key algorithm parameters in subsequent simulations. Numerical evaluations validate the proposed PABT algorithm, showing significant improvements in normalized beamforming gain and achievable spectral efficiency compared to baseline methods.

Recent advancements in integrated sensing and communication (ISAC) technology offer significant potential for high-resolution localization and high-throughput communication in vehicular networks. However, many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. Additionally, the limited transmit power of roadside units (RSUs) and the small radar cross section (RCS) of vehicles can result in weak echo signals, hindering effective vehicle detection and tracking. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Our approach optimizes both RSU beamforming and IOS configuration (including refraction and reflection amplitudes and phase shifts) to minimize the Cramér-Rao bound (CRB) while meeting communication rate requirements. Solving this optimization problem is challenging due to the complex variable-coupling and the implicit CRB expression. To overcome these difficulties, we present a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the effectiveness of the proposed ZO-IPDD algorithm and the superior performance of the tracking scheme compared to existing methods.

Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduced hardware complexity. However, effective beamforming in multi-user scenarios relies heavily on accurate channel state information, the acquisition of which often requires significant pilot overhead, degrading system performance. To address this and inspired by the spatial congruence between sub-6GHz (sub-6G) and mmWave channels, we propose a Sub-6G information Aided Multi-User Hybrid Beamforming (SA-MUHBF) framework, avoiding excessive use of pilots at mmWave. SA-MUHBF employs a convolutional neural network to predict mmWave beamspace from sub-6G channel estimate, followed by a novel multi-layer graph neural network for analog beam selection and a linear minimum mean-square error algorithm for digital beamforming. Numerical results demonstrate that SA-MUHBF efficiently predicts the mmWave beamspace representation and achieves superior spectrum efficiency over state-of-the-art benchmarks. Moreover, SA-MUHBF demonstrates robust performance across varied sub-6G system configurations and exhibits strong generalization to unseen scenarios.

Due to the ability to provide superior error-correction performance, the successive cancellation list (SCL) algorithm is widely regarded as one of the most promising decoding algorithms for polar codes with short-to-moderate code lengths. However, the application of SCL decoding in low-latency communication scenarios is limited due to its sequential nature. To reduce the decoding latency, developing tailored fast and efficient list decoding algorithms of specific polar constituent codes (special nodes) is a promising solution. Recently, fast list decoding algorithms are proposed by considering special nodes with low code rates. Aiming to further speedup the SCL decoding, this paper presents fast list decoding algorithms for two types of high-rate special nodes, namely single-parity-check (SPC) nodes and sequence rate one or single-parity-check (SR1/SPC) nodes. In particular, we develop two classes of fast list decoding algorithms for these nodes, where the first class uses a sequential decoding procedure to yield decoding latency that is linear with the list size, and the second further parallelizes the decoding process by pre-determining the redundant candidate paths offline. Simulation results show that the proposed list decoding algorithms are able to achieve up to 70.7% lower decoding latency than state-of-the-art fast SCL decoders, while exhibiting the same error-correction performance.

For many practical applications in wireless communications, we need to recover a structured sparse signal from a linear observation model with dynamic grid parameters in the sensing matrix. Conventional expectation maximization (EM)-based compressed sensing (CS) methods, such as turbo compressed sensing (Turbo-CS) and turbo variational Bayesian inference (Turbo-VBI), have double-loop iterations, where the inner loop (E-step) obtains a Bayesian estimation of sparse signals and the outer loop (M-step) obtains a point estimation of dynamic grid parameters. This leads to a slow convergence rate. Furthermore, each iteration of the E-step involves a complicated matrix inverse in general. To overcome these drawbacks, we first propose a successive linear approximation VBI (SLA-VBI) algorithm that can provide Bayesian estimation of both sparse signals and dynamic grid parameters. Besides, we simplify the matrix inverse operation based on the majorization-minimization (MM) algorithmic framework. In addition, we extend our proposed algorithm from an independent sparse prior to more complicated structured sparse priors, which can exploit structured sparsity in specific applications to further enhance the performance. Finally, we apply our proposed algorithm to solve two practical application problems in wireless communications and verify that the proposed algorithm can achieve faster convergence, lower complexity, and better performance compared to the state-of-the-art EM-based methods.

Due to the capability of reshaping wireless transmission environments, intelligent reflecting surface (IRS) has emerged as a promising solution to address the blockage issue in millimeter wave (mmWave) communication systems. However, to reduce the channel estimation overhead and in the meantime harvest the beamforming gain brought by the large-scale antennas and reflecting elements, efficient beam training methods are indispensable. In this paper, we develop a deep learning (DL) aided two-stage beam training scheme for an IRS-assisted mmWave system. In the first stage, we recognize the effective channel paths for both direct link and cascaded link via multibeam scanning, where the different sparse properties of these two links are exploited. In the second stage, a deep neural network (DNN)-based beam synthesizer is developed to generate an optimized reflecting vector and hybrid precoder, based on the recognized multiple channel paths obtained in the first stage. Simulation results are presented to demonstrate the superiority of the proposed scheme over the state-of-the-arts.

Being able to provide excellent error-correction performance for polar codes with short-to-moderate code length, successive-cancellation list (SCL) is regarded as one of the most promising decoding algorithms. However, the application of SCL decoding in low-latency communication scenarios is limited due to its sequential nature. Recently, fast list decoding algorithms are proposed by considering special nodes with low code rates. Aiming at achieving further speedup for SCL decoding, this paper presents fast list decoding algorithms for two types of high-rate special nodes, namely single-parity-check (SPC) and sequence rate-1(SRI) nodes, based on the minimum-combinations set (MCS) which is able to significantly narrow the search space of candidate paths. Typically, SPC nodes can be directly decoded within one round of path splitting procedure, whereas SR1 nodes, as a group of parallel SPC nodes, can also be decode efficiently. Simulation results show that the proposed fast SCL decoder is able to reduce the decoding latency by 68.4% as compared to the state of the art, without any error-correction performance degradation.

The orthogonal time frequency space (OTFS), a recently introduced two-dimensional modulation in the delayDoppler domain, holds great promise for high-mobility communications. OTFS-based millimeter-wave systems are gaining attraction due to their ability to offer a larger bandwidth and increased robustness against Doppler spread. However, the presence of phase noise (PHN) from high-frequency oscillators can lead to severe inter-carrier interference in OTFS. In this paper, we present a new approach that addresses the challenge of PHN in millimeter-wave OTFS systems by introducing a joint design of data detection and PHN estimation, which are performed iteratively to facilitate the exchange of the posterior information between the two components. Specifically, a lowcomplexity expectation propagation algorithm is developed for data detection, where the posterior information is updated in an inverse-free way based on the minorization-maximization method. As for the PHN estimation, a belief propagation algorithm is applied for message passing on a factor graph. Simulation results demonstrate the superior performance of our proposed design, showcasing faster convergence and substantial gains compared to existing OTFS detectors. Moreover, our approach also exhibits robustness against PHN, highlighting its effectiveness in practical scenarios.

In this paper, we propose a movable antenna (MA)-enabled frequency-hopping (FH) multiple-input multiple-output (MIMO) radar system and analyze the properties of its radar ambiguity function. Specifically, we derive the expression of the ambiguity function and analyze the relationship between its main lobe width and the transmit antenna positions. In particular, the optimal antenna distribution to achieve the minimum main lobe width is revealed and we discover that this minimum width is related to the ratio of antenna dimension to wavelength, the number of antennas, and the target angle. However, to achieve this minimum width, there is inevitable performance loss in the side lobes. Therefore, we propose to balance between minimizing the side lobe levels and narrowing the main lobe of the ambiguity function by optimizing the antenna positions. To achieve this goal, we propose a low-complexity algorithm based on the Rosen’s gradient projection method (RGPM), and we show that its performance is very close to that of the genetic algorithm (GA). Simulation results are presented to validate the theoretical analysis on the properties of the ambiguity function, and demonstrate the advantages of MAs in improving the radar performance.

In Millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable alignment of transceiver beams is essential for optimizing beamforming gain during data transmission. As XL-MIMO systems feature increasingly large antenna arrays, the electromagnetic propagation field shifts from the far field to the near field. In addition to angular domain beam training, near-field scenarios require distance domain beam training. However, many existing near-field beam training methods are essentially adaptations of far-field techniques, leading to issues such as excessive training overhead and suboptimal alignment. This paper introduces a position-aware beam training algorithm. In the initialization phase, position information is leveraged to reduce the beam search space. Subsequently, a multi-round iterative measurement strategy is employed. This approach updates the polar-domain codebook's posterior distribution using Bayes' rule based on measurements and dynamically adjusts the candidate beam set until the iteration termination condition is met. Numerical evaluations confirm the superiority of our proposed position-aware beam training algorithm, demonstrating significant enhancements in both normalized beamforming gain and achievable spectrum efficiency.

Semantic communication is a promising framework for the next generation communication systems, which generally adopts deep learning based joint source and channel coding and has been verified to offer superior efficacy. A key ingredient in augmenting the reliability of this framework is the incorporation of hybrid automatic repeat request (HARQ) techniques. However, existing semantic HARQ architectures, such as fixed-length HARQ or chase combining HARQ (CC-HARQ), utilize predefined retransmission code lengths, lacking the flexibility to adjust to different channel signal-to-noise ratio (SNR) conditions. To address this issue, this paper develops an adaptive HARQ scheme by leveraging the double deep Q-network (DDQN) to determine the retransmission code lengths. Specifically, we first propose a basic model which consists of an image reconstruction module and a performance estimation module. The performance estimation module replaces the conventional error detection method like cyclic redundancy check (CRC) to estimate the structural similarity index measure (SSIM) of the reconstructed image at the receiver. Building on this basic model, our proposed HARQ scheme works by feeding back an NACK signal and an appropriate code length determined by the proposed DDQN algorithm to the semantic transmitter for the next transmission, if the estimated SSIM performance of the previous transmission does not exceed a predefined threshold. Experimental results demonstrate that our HARQ scheme is able to achieve the same SSIM performance as the existing semantic HARQ schemes, but with significantly reduced communication cost.

For reliable communication in high mobility scenarios, we need to estimate the channel in orthogonal time frequency space (OTFS) systems, which can be considered as a sparse signal recovery problem with an uncertain sensing matrix and solved by compressed sensing (CS) algorithms. However, conventional expectation maximization (EM)-based CS algorithms only output the point estimation of grid parameters to approximate the sensing matrix, which leads to an unavoidable approximation error. To address this problem, we present a turbo inverse-free successive linear approximation variational Bayesian inference (Turbo-IFSLA-VBI) algorithm, which provides the Bayesian estimation of both channel and grid parameters, thus the approximation error can be eliminated by iteratively approximating the sensing matrix with updated grid parameters. Besides, the proposed method employs a majorization-minimization (MM) framework to simplify the matrix inverse operations, achieving a lower computational complexity. Finally, simulation results are presented to verify the superiority of the proposed scheme over the state-of-the-art schemes.

Faced with the abundance of mobile ad-hoc network (MANET) applications, there emerges a strong incentive to provision MANET in millimeter wave. However, the deafness of directional antennas and the decentralized structure of MANET make it difficult to achieve consistent medium access control (MAC) among nodes through random competition. Therefore, graph coloring-based MAC (GC-MAC) scheme is proposed to implement time division multiplexed scheduling, but it allocates equal slots to links, disregarding the unbalanced and piecewise stationary traffic distribution in task-driven MANETs. Here, we propose a continual multi-agent reinforcement learning (RL)-assisted traffic-adaptive scheme to enhance the agility of slot allocation. Specifically, we add a contention period to frames of GC-MAC, during which nodes analyze stochastic characteristics of traffic to derive link traffic distribution for each task, and adjust slot assignment to reach cooperation through decentralized multi-agent deep Q-network (MA-DQN). Besides, considering the traffic variation due to task switch, continual RL is incorporated to accommodate changes of the environment more sensitively. Finally, simulation results prove the proposed scheme achieves faster convergence speed, lower delay and higher throughput.

Intelligent reflecting surface (IRS) is a promising technology to reconfigure the wireless channel cost-effectively, thereby improving transmission reliability in mobile ad hoc networks (MANETs). Prior works related to IRS primarily rely on channel estimation for configuring IRS, which, however, will introduce additional overhead and impact the efficiency of IRS-assisted MANETs, leading to increased delay and energy consumption during data transmission. To overcome this difficulty, we propose a multi-IRS-assisted double deep Q-Network (MIRS-DDQN) routing algorithm to find paths with higher end-to-end data rate and lower energy consumption. Routing packets are designed to collect experience tuples for DDQN to optimize the joint routing and transmit power selection policy. Moreover, these packets are also used to execute a blind beamforming strategy to configure IRS without incurring additional communication overhead. In particular, the IRSs can effectively enhance the links related to the IRS-assisted nodes and thus provide better solutions for DDQN to find an energy-efficient path with higher end-to-end data rate. Simulation results are presented to demonstrate the advantages of the proposed algorithm as compared to benchmark schemes in terms of end-to-end delay, energy consumption and end-to-end data rate.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly improve the channel estimation (CE) and target/scatterer/user sensing performance in the considered system, we propose a two-phase transmission scheme, where the coarse and refined CE/sensing results are respectively obtained in the first and second phases. Particularly, in each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is devised to solve the considered joint sensing and CE problem. The proposed algorithm incorporates the partial overlapping structured (POS) sparsity between the sensing and communication channels to improve the performance. Simulation results are provided to verify the superiority of the proposed algorithm.

Multiband fusion enhances delay estimation by jointly utilizing signals from multiple noncontiguous frequency bands. However, in the multiband signal model, there are many local optimums in the associated likelihood function due to the existence of high-frequency component and phase distortion factors, posing challenges for high-accuracy parameter estimation. To address this, we propose a two-stage scheme equipped with different signal models derived from the original model, where the first-stage coarse estimation is performed using a weighted root MUSIC algorithm to narrow down the search range for the subsequent stage, and the second-stage refined estimation utilizes a Bayesian approach to avoid convergence to bad suboptimal solutions. Specifically, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm in the refined stage. Unlike conventional particle-based VBI (PVBI) that optimizes only particle probability and incurs exponential per-iteration complexity with particle count, our more flexible SPVBI algorithm optimizes both the position and probability of each particle. Additionally, it utilizes block SSCA to significantly improve sampling efficiency by averaging over iterations, making it suitable for high-dimensional problems. Extensive simulations demonstrate the superiority of our proposed algorithm over various baseline methods.

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

Intelligent reflecting surface (IRS)-aided wireless relaying technology has aroused great interest recently as a promising new solution to enhance the system performance. However, most existing works only consider the decode-and-forward (DF) relay and ignore the base station (BS) to user direct link. In this paper, we focus on an IRS-aided full-duplex (FD) amplify-and-forward (AF) relay system and study the deployment design and beamforming optimization problem. Specifically, we first analyze the asymptotic rates achieved by three IRS deployment strategies (i.e., deploying the IRS near the BS, relay and user) when the number of reflecting elements becomes sufficiently large to obtain useful insights. Then, for the practical case with finite number of reflecting elements, we aim to maximize the transmission rate under different IRS deployment strategies by jointly optimizing the IRS reflection coefficients and transmit powers at the BS and relay. For the case of deploying the IRS near the user, a block coordinate decent (BCD)-based algorithm is proposed. For the cases of deploying the IRS near the relay and BS, we propose a virtual stochastic successive convex approximation (VSSCA) algorithm to solve our considered deterministic optimization problems efficiently. Finally, numerical results are provided to demonstrate the asymptotic performance analysis as well as the effectiveness of our proposed algorithms as compared to various benchmark schemes.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

Recently, massive multiple input multiple output (MIMO) channel tracking in the fifth generation (5G) new radio (NR) systems has attracted intensive interest. By exploiting the dynamic sparsity of massive MIMO channels, it is possible to design a high-accuracy channel tracking scheme. However, the existing channel estimation/tracking algorithms often ignore the practical imperfections in real systems, such as the channel aging effect due to the hopping Sounding Reference Signal (SRS) pattern, the time offset, phase noise, and multi-user SRS interference. In this paper, we propose a robust multi-user uplink channel tracking scheme, which is compatible with 5G NR systems and robust against various system imperfections. Specifically, we propose a sparse Markov channel model to capture the dynamic sparsity of massive MIMO-OFDM channels under the consideration of imperfect factors. Then, we propose a robust multi-user channel tracking scheme, which iterates between two components until convergence. Particularly, in thechannel estimation component, we employ the Turbo-CS method to exploit the channel dynamic sparsity to perform efficient multi-user channel estimation under non-orthogonal SRSs, where a multi-stage successive interference cancellation (MSIC) scheme is proposed to mitigate the multi-user SRS interference. Then, in theimperfection parameter estimation component, we further estimate the unknown imperfection parameters based on the updated Bayesian channel estimates from thechannel estimation component. Simulation results verify the superior channel tracking performance of our proposed scheme over the baselines.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.

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

Due to the sequential nature of the successive-cancellation (SC) algorithm, the decoding of polar codes suffers from significant decoding latencies. Fast SC decoding is able to speed up the SC decoding process, by implementing parallel decoders at the intermediate levels of the SC decoding tree for some special nodes with specific information and frozen bit patterns. To further improve the parallelism of SC decoding, this paper present a new class of special nodes composed of a sequence of rate one or single-parity-check (SR1/SPC) nodes, which can be typically found in high-rate polar codes and is able to envelop a wide variety of existing special node types. Then, we analyse the parity constraints caused by the frozen bits in each descendant node, such that the decoding performance of the SR1/SPC node can be preserved once the parity constraints are satisfied. Finally, a generalized fast decoding algorithm is proposed to decode SR1/SPC nodes efficiently, where the corresponding parity constraints are taken into consideration. Simulation results show that the proposed decoding algorithm of the SR1/SPC node can nearly achieve maximum-likelihood (ML) performance, and the overall SC decoding latency can be reduced by 43.8% as compared to the state-of-the-art fast SC decoder.

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a partially common sparsity. By exploiting this, we propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously. Specifically, the base station (BS) first transmits downlink pilots to sense the targets in the scattering environment. Then the user transmits uplink pilots to estimate the communication channel. Finally, joint scattering environment sensing and channel estimation are performed at the BS based on the reflected downlink pilot signal and received uplink pilot signal. A message passing based algorithm is designed by combining the turbo approach and the expectation maximization method. The advantages of our proposed scheme are verified in the simulations.

Mobile ad-hoc networks (MANETs) have garnered significant interest in various specific scenarios owing to their capability to provide flexible and decentralized communication. However, in MANETs, link failures caused by obstacles, traffic surges and inefficient routing algorithms, are commonly en-countered. To address these issues, we propose an intelligent reflecting surface assisted anti-blocking routing (IRS-ABR) algorithm that incorporates the deep Q-network (DQN) for dynamic obstacles avoidance and traffic control. Moreover, by employing IRSs as intermediate nodes in the network, the proposed algorithm can achieve enhanced path routing. The simulation results validate the effectiveness of the proposed algorithm, as it achieves a 50% higher packet delivery rate compared to the traditional algorithm, while also reducing the transmission delay and energy consumption by 34% and 12%, respectively, through the utilization of IRS.

In this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

In recent years, unmanned aerial vehicle (UAV) ad-hoc networks have achieved rapid development due to their autonomy and high reliability. Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks. However, due to the limited channel resources and complex communication environment, it becomes challenging to obtain complete information needed for clustering from remote nodes. In addition, the high dimension and non-linear relationships of data also make large-scale clustering diffcult. Therefore, in this paper, we propose a noval distributed clustering scheme. First, a matrix factorization (MF) based time series algorithm is proposed to predict and complement the incomplete information. Second, we adopt deep autoencoder to wisely incorporate the non-linear relationship of information needed for clustering. Finally we extract features as input to k-means to obtain clustering results. Simulation results demonstrate the effectiveness of the proposed clustering scheme.

In this work, we propose a neural adjusted min-sum (NAMS) decoder for low-density parity-check (LDPC) codes. In particular, we improve the traditional normalized min-sum (NMS) decoder by introducing a selection mechanism to adjust the check-node update step, where either the min-sum rule or the belief propagation (BP) rule is selected. Besides, we unfold the modified decoder into a model-driven neural network, where layer-dependent trainable parameters are introduced as weights in the Tanner graph and optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed NAMS decoder is able to provide superior error-correction performance as compared to the neural NMS decoder, with only slightly increased computational complexity. Moreover, in certain circumstances, the proposed NAMS decoder even outperforms the neural BP decoder, with much lower computational complexity.

Accurately predicting object depth is a key challenge in monocular 3D detection task. The perspective projection principle used by most state-of-the-art approaches demands a complex balance between the ratio-form depth estimation and 2D-3D geometric regularizations, and thus can lead to sub-optimal solutions. In this paper, we propose a novel synergistic scheme that can achieve better trade-off among these competing objectives. Our main proposal is a progressive depth regularization (PDR) architecture that splits the overall training process into three sequential depth estimation steps to gradually remove the unwanted deviations induced by the over-regularization. Specifically, our model first learns the coarse depth with the conventional perspective projection and combines the coarse-to-fine generation to reduce the search space of 2D projection height prediction. We then deactivate individual supervision on 2D projection height prediction and introduces a new auxiliary 3D physical height prediction to relax the 2D and 3D regularizations, respectively. Consequently, our PDR leads to more precise depth estimation by mitigating the inherent ambiguities in the geometric priors of perspective projection through progressive regularization relaxation. Extensive experiments on both KITTI and Rope3D benchmark show that our PDR delivers strong performance gains as compared to the previous methods.

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) communication systems have gained considerable attention recently. However, the benefits brought by IRS require the instantaneous channel state information (I-CSI) of the cascaded base station (BS)-IRS and IRS-user channel which is difficult to obtain in practice, especially for the multiuser scenario. To address this issue, in this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

Cooperative localization is a promising technique in wireless networks, and neighbor selection (NS) is essential to limit the degree of cooperation and reduce the amount of data to be exchanged. However, the existing NS algorithms may suffer from major performance loss when applied to networks with limited resources (e.g., bandwidth, time and energy). In this paper, we establish a general optimization framework for the NS problem to minimize the localization error under strict resource constraints. Based on the squared position error bound (SPEB) criterion, we formulate two distributed NS problems under implicit and explicit energy constraints, respectively, to balance the energy consumption of the network, where implicit energy constraints mean that specific energy profiles of the nodes’ neighbors are unavailable while explicit energy constraints mean the opposite. Moreover, we propose to jointly optimize the NS and power allocation in the explicit case to further improve the localization performance. The resulting problems are challenging to solve due to the nonlinear objective functions and discrete optimization variables. We first transform them into more tractable forms and then develop novel algorithms based on the penalty dual decomposition method to solve the transformed problems efficiently. Simulation results show that the proposed algorithms can significantly outperform benchmark algorithms. In particular, the proposed algorithm almost achieves the performance lower bound in the implicit case.

For intelligent reflecting surface (IRS)-aided wireless communications, channel estimation is essential and usually requires excessive channel training overhead when the number of IRS reflecting elements is large. The acquisition of accurate channel state information (CSI) becomes more challenging when the channel is not quasi-static due to the mobility of the transmitter and/or receiver. In this work, we study an IRS-aided wireless communication system with a practical channel model that characterizes the time-varying propagation property and propose an innovative two-stage transmission protocol. In the first stage, we send pilot symbols and track the direct/reflected channels based on the received signal, and then data signals are transmitted. In the second stage, instead of sending pilot symbols first, we directly predict the direct/reflected channels and all the time slots are used for data transmission. Based on the proposed transmission protocol, we propose a two-stage channel tracking and prediction (2SCTP) scheme to obtain the direct and reflected channels with low channel training overhead, which is achieved by exploiting the temporal correlation of the time-varying channels. Specifically, we first consider a special case where the IRS-access point (AP) channel is assumed to be static, for which a Kalman filter (KF)-based algorithm and a long short-term memory (LSTM)-based neural network are proposed for channel tracking and prediction, respectively. Then, for the more general case where the IRS-AP, user-IRS and user-AP channels are all assumed to be time-varying, we present a generalized KF (GKF)-based channel tracking algorithm, where proper approximations are employed to handle the underlying non-Gaussian random variables. Numerical simulations are provided to verify the effectiveness of our proposed transmission protocol and channel tracking/prediction algorithms as compared to existing ones.

Mobile ad-hoc network (MANET) has attracted ex-tensive attention in many applications with nodes operating over bandwidth-constrained wireless links in a self-organized manner. Typically, to avoid potential collisions, CSMA/CA method is widely adopted in MANETs and affects the provisioning quality of service (QoS). However, due to the potentially severe collisions in a large-scale MANET, current CSMA/CA methods fail to support QoS effectively, especially for scenarios with different priority services. Here, we propose a priority-aware CSMA/CA strategy to use the higher-priority packets to collect the queueing information from adjacent nodes in a piggyback manner and model the channel access process by a partial observation Markov decision process (POMDP). Furthermore mean-field multi-agent reinforcement learning (MARL) is adopted to allow each individual node in the MANET to dynamically select the appropriate contention window (CW) and gradually compute the global optimal policy. Finally, extensive simulation results verify significantly lower delay and packet loss rate for the higher- priority services and comparable provisioning quality for lower- priority services, which reflects the superiority over baselines.

Hardware impairments, such as power amplifier (PA) nonlinear distortion, present as the key source to system performance degradation in millimeter-wave (mmWave) communications. In this paper, we consider a mmWave massive MIMO downlink system with nonlinear PA at each transmit antenna and investigate the design of distortion-aware beamforming for efficient data transmission. In particular, we formulate a sumrate optimization problem that accounts for the characteristics of PA distortion. Rather than directly solving the original highly non-convex sum-rate optimization problem, we embrace the classic weighted-minimum-mean-square-error (WMMSE) framework and propose an algorithm to solve its equivalent WMMSE optimization problem for beamforming design. Numerical results confirm the effectiveness of our proposed WMMSE-based algorithm, which outperforms one existing distortion-aware beamforming algorithm and provides significant performance gain compared to conventional beamforming baselines that do not account for the knowledge of the PA distortion.

Recently, reconfigurable intelligent surface (RIS) have been introduced not only to overcome communication blockages due to obstacles but also for high-precision localization of users in GPS denied environments, e.g., indoors, woods, and underground tunnels, etc. This paper studies the cooperative localization problem in an RIS-aided milimeter wave (mmWave) system, where the RIS is deployed to assist the localization of two users and the two users further cooperate to improve their localization performance. We first build the system model based on the uniform planar array (UPA) response of RIS. Then, the Fisher information matrix (FIM) and the Cramér-Rao lower bound (CRLB) for estimating the absolute user equipment (UE) position are derived. An efficient block coordinate descent (BCD)-based reflect beamforming design algorithm is proposed to minimize the CRLB. Finally, numerical results are presented to show that user cooperation can provide additional localization performance gain as compared to the case without cooperation and centimeter-level positioning accuracy can be achieved by utilizing a large number of reflecting elements and exploiting user cooperation.

Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model. For the single-user case, we propose to incorporate the special structure of the reflected channels into the design of the generative model. While for the multiuser case, we extend the IRS-GAN and present a multiuser IRS-GAN (abbreviated as IRS-GAN-M), where the distributions of the reflected channels associated with different users are learned simultaneously with reduced number of network parameters (as compared to the naive scheme that assigns a dedicated IRS-GAN for each user). Moreover, theoretical analysis is presented to prove that the minimax game in the IRS-GAN-M framework has a global optimum if the generative and discriminative models are given with enough capacity. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework.

Millimeter-wave (mmWave) communication has emerged as a promising means for supporting high-rate covert communication. However, the use of antenna arrays with beamforming at mmWave requires precise beam alignment between legitimate parties, and this procedure may entail large beam training overhead and create additional signal leakage to eavesdroppers. In this work, we consider a multi-user mmWave communication system and address the problem of designing proper covert beam training and data transmission between legitimate parties Alice and Bobs, while keeping the underlying communication undetectable from warden Willie. We first propose a novel Covert Multi-user Beam Training Strategy (CMBTS) that adopts multi-finger beam codebook to reduce the probability of communication being detected and to enable simultaneous training for multiple users. With the proposed CMBTS, a joint optimization framework for covert beam training and data transmission with a friendly jammer is developed to maximize the effective covert throughput while ensuring the covertness constraint at warden is met. We further propose an algorithm that combines successive convex approximation and inexact block coordinate descent methods to solve the problem efficiently. Numerical results validate the effectiveness of the CMBTS proposed and confirm its superior performance as compared to several beam training baselines (including exhaustive and hierarchical search) tailored to the covert communication setup considered. Among them, CMBTS achieves the best successful alignment probability and the largest effective covert throughput yet with the least training overhead.

Intelligent reflecting surface (IRS) has emerged as a promising paradigm to improve the capacity and reliability of a wireless communication system by smartly reconfiguring the wireless propagation environment. To achieve the promising gains of IRS, the acquisition of the channel state information (CSI) is essential, which however is practically difficult since the IRS does not employ any transmit/receive radio frequency (RF) chains in general and it has limited signal processing capability. In this paper, we study the uplink channel estimation problem for an IRS-aided multiuser single-input multi-output (SIMO) system. The existing channel estimation approach for IRS-aided multiuser systems mainly consists of three phases, where the direct channels from the base station (BS) to all the users, the reflected channel from the BS to a typical user via the IRS, and the other reflected channels are estimated sequentially based on the estimation results of the previous phases. However, this approach will lead to a serious error propagation issue, i.e., the channel estimation errors in the first and second phases will deteriorate the estimation performance in the second and third phases. To resolve this difficulty, we propose a novel two-phase channel estimation (2PCE) strategy which is able to alleviate the negative effects caused by error propagation and enhance the channel estimation performance with the same amount of channel training overhead as in the existing approach. Specifically, in the first phase, the direct and reflected channels associated with a typical user are estimated simultaneously by varying the reflection patterns at the IRS, such that the estimation errors of the direct channel associated with this typical user will not affect the estimation of the corresponding reflected channel. In the second phase, we estimate the CSI associated with the other users and demonstrate that by properly designing the pilot symbols of the users and the reflection patterns at the IRS, the direct and reflected channels associated with each user can also be estimated simultaneously, which helps to reduce the error propagation. Moreover, the asymptotic mean squared error (MSE) of the proposed 2PCE strategy is analyzed when the least-square (LS) channel estimation method is employed, and we show that the 2PCE strategy can outperform the existing approach. Finally, extensive simulation results are presented to validate the effectiveness of our proposed channel estimation strategy.

Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements, yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model, where the special structure of the reflected channels is incorporated to improve the modeling accuracy. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework for IRS-related channel modeling.

Though 3D object detection from point clouds has achieved rapid progress in recent years, the lack of flexible and high-performance proposal refinement remains a great hurdle for existing state-of-the-art two-stage detectors. Previous works on refining 3D proposals have relied on human-designed components such as keypoints sampling, set abstraction and multi-scale feature fusion to produce powerful 3D object representations. Such methods, however, have limited ability to capture rich contextual dependencies among points. In this paper, we leverage the high-quality region proposal network and a Channel-wise Transformer architecture to constitute our two-stage 3D object detection framework (CT3D) with minimal hand-crafted design. The proposed CT3D simultaneously performs proposal-aware embedding and channel-wise context aggregation for the point features within each proposal. Specifically, CT3D uses proposal’s keypoints for spatial contextual modelling and learns attention propagation in the encoding module, mapping the proposal to point embeddings. Next, a new channel-wise decoding module enriches the query-key interaction via channel-wise re-weighting to effectively merge multi-level contexts, which contributes to more accurate object predictions. Extensive experiments demonstrate that our CT3D method has superior performance and excellent scalability. Remarkably, CT3D achieves the AP of 81.77% in the moderate car category on the KITTI test 3D detection benchmark, outperforms state-of-the-art 3D detectors.

Consider a multi-cluster cooperative relay network, where each relay cluster (consists of a certain number of amplify-and-forward (AF) relays) forwards the signal from its associated user equipment (UE) to the base station (BS). With the goal of providing fairness among the UEs and reducing the costs of full relay cooperation, we study the joint design of relay clustering and beamforming to maximize the minimum signal-to-interference-and-noise ratio (SINR) under per relay power constraints. This max-min SINR problem with relay clustering is formulated as a mixed-integer programming (MIP) problem, which is generally NP-hard. To tackle this problem, we propose a block coordinate descent (BCD) based algorithm based on the property that the constraints are separable among the optimization variables, i.e., the clustering matrix, the relay cooperative beamforming vector, and the receive beamforming vectors at the BS. Specifically, these variables are optimized iteratively in an alternating fashion, one at each time with others being fixed and we show that each subproblem can be efficiently and optimally solved. Simulation results demonstrate the effectiveness of the proposed algorithm as compared with the benchmark schemes.

Orthogonal time frequency space (OTFS) modulation shows significant advantages over orthogonal frequency division multiplexing (OFDM), specially in environments with high frequency dispersion. However, high peak-to-average power ratio (PAPR) has been one of the major drawbacks of OTFS systems, which impairs the efficiency of the power amplifier. To resolve the problem, we propose a PAPR reduction method based on the autoencoder (AE) architecture through deep learning (DL) techniques, where the encoder is trained to reduce the PAPR and the decoder is trained to reconstruct the original signal. By carefully designing the loss function, the bit error rate (BER) and the PAPR are simultaneously minimized, and a hyper-parameter is introduced to achieve a good compromise between BER and PAPR in the proposed scheme. Simulation results validate the advantages of the proposed scheme as compared to the other conventional schemes.

The flying ad-hoc network (FANET) is widely applied to unmanned aerial vehicles (UAV s) but it is vulnerable to the frequency jamming in reality. Therefore, this paper proposes a federated deep Q-network (DQN) based frequency hopping strategy to solve the problem of periodic frequency jamming. We developed a DQN mechanism with an exploration-exploitation epsilon-greedy policy, directed by a federated learning mechanism to obtain a frequency hopping strategy. The simulation results show that our proposed algorithm has better convergence and decision accuracy performance compared with the DQN based frequency hopping strategy. And the performance will improve when the number of UAVs increases.

In statistical priority-based multiple access protocol (SPMA), to ensure the performance of high-priority packets, the absolute prioritization mechanism is adopted and the threshold of each priority queue is usually set to a fixed value. However this limits the transmission of low-priority packets when there are few high-priority packets. Moreover, the existing backoff algorithms do not consider the factor of channel occupancy. In order to solve the above problems, in this paper, we propose a Deep Q-Network (DQN) based transmission and backoff (DQN-TB) approach that consists of two sub networks, where the first one aims to transmit more low-priority packets but not affect the performance of high-priority packets, while the second one determines the better backoff duration by utilizing the action of former sub-network and channel occupancy. Numerical experiments demonstrate that the proposed DQN-TB approach has better throughput and backoff delay performance.

With the increasing popularity of the Internet-of-Things (IoT) devices, the demand for fast and convenient battery charging services grows rapidly. Wireless charging is a promising technology for such a purpose and its usage has become ubiquitous. However, the close distance between the charger and the device being charged not only makes proximity-based and near-field communication attacks possible but also introduces a new type of vulnerabilities. In this article, we propose to create fingerprints for wireless chargers based on the intrinsic nonlinear distortion effects of the underlying charging circuit. Using such fingerprints, we design the WirelessID system to detect potential short-range malicious wireless charging attacks. WirelessID collects signals in the standby state of the charging process and sends them to a trusted server, which can extract the fingerprint and then identify the charger. We conduct experiments on eight commercial chargers over a period of five months and collect 8000 traces of signal. We use 10% of the traces as the training data set and the rest for testing. The results show that on the standard performance metrics, we have achieved 99.0% precision, 98.9% recall, and 98.9% F1 -score.

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the promising gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of the IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Finally, simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.

Covert communication prevents legitimate transmission from being detected by a warden while maintaining certain covert rate at the intended user. Prior works have considered the design of covert communication over conventional low-frequency bands, but few works so far have explored the higher-frequency millimeter-wave (mmWave) spectrum. The directional nature of mmWave communication makes it attractive for covert transmission. However, how to establish such directional link in a covert manner in the first place remains as a significant challenge. In this paper, we consider a covert mmWave communication system, where legitimate parties Alice and Bob adopt beam training approach for directional link establishment. Accounting for the training overhead, we develop a new design framework that jointly optimizes beam training duration, training power and data transmission power to maximize the effective throughput of Alice-Bob link while ensuring the covertness constraint at warden Willie is met. We further propose a dual-decomposition successive convex approximation algorithm to solve the problem efficiently. Numerical studies demonstrate interesting tradeoff among the key design parameters considered and also the necessity of joint design of beam training and data transmission for covert mmWave communication.

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this article a new cost-effective analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1%.

In this paper, we consider a mmWave massive multiple-input multiple-output (MIMO) communication system with one static base station (BS) serving a fast-moving user, both equipped with a very large array. The transmitted signal arrives at the user through multiple paths, each with a different angle-of-arrival (AoA) and hence Doppler frequency offset (DFO), thus resulting in a fast time-varying multipath fading MIMO channel. In order to mitigate the Doppler-induced channel aging for reduced pilot overhead, we propose a new angular-domain selective channel tracking and Doppler compensation scheme at the user side. Specifically, we formulate the joint estimation of partial angular-domain channel and DFO parameters as a dynamic compressive sensing (CS) problem. Then we propose a Doppler-aware-dynamic variational Bayesian inference (DD-VBI) algorithm to solve this problem efficiently. Finally, we propose a practical DFO compensation scheme which selects the dominant paths of the fast time-varying channel for DFO compensation and thereby converts it into a slow time-varying effective channel. Compared with the existing methods, the proposed scheme can enjoy the huge array gain provided by the massive MIMO and also balance the tradeoff between the CSI signaling overhead and spatial multiplexing gain. Simulation results verify the advantages of the proposed scheme over various baseline schemes.

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of reflecting elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI; while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective fading channels with the optimized IRS phase shifts, thus significantly reducing the channel training overhead and passive beamforming design complexity over the existing schemes based on the I-CSI of all channels. Besides, for ease of practical implementation, we consider discrete phase shifts at each reflecting element of the IRS. For the single-user case, an efficient penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS stochastic successive convex approximation (SSCA) algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the user's achievable rate is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoder, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors. Finally, simulation results are presented to validate the effectiveness of our proposed algorithm as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.

In this work, we consider the use of model-driven deep learning (DL) techniques for signal detection in massive multiple-input multiple-output (MIMO) system. Massive MIMO promises improved spectral efficiency, coverage and reliability, compared to conventional MIMO systems. Unfortunately, these benefits usually come at the cost of significantly increased computational complexity. To address this difficulty, a learned conjugate gradient descent network, referred to as LcgNet, is presented by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes for every problem instance, we explicitly learn their universal values. We show that the performance of the proposed network can be greatly improved by augmenting the dimensions of these step-sizes. Furthermore, due to the limited learnable parameters to be optimized, the proposed networks are easy and fast to train. Numerical results demonstrate that this approach can achieve superior performance over some state-of-the-art MIMO detectors such as the CG detector, the linear minimum mean squared error (LMMSE) detector etc., with much lower computational complexity.

With the development of the Internet of vehicles and 5G, there emerge more and more challenging application scenarios with fast time-varying channels and high mobility nodes, such as high speed trains environment and vehicle-to-infrastructure (V2I) communication in highway. To support the reliable vehicular communication and mobile edge computing (MEC), it is important to obtain the future channel state information (CSI), which can help optimize system transmission scheme. In this paper, we propose an efficient blind CSI prediction model, called BCPMN. We first reshape the sampled signal into a specific 2-dimensional matrix. Then we propose a learning framework contains of convolutional neural network (CNN), long short-term memory (LSTM) network and fully connected layers. To validate the proposed model, we conduct extensive experiment in three modulation modes. The results show that the BCPMN achieves highly accurate signal-to-noise ratio (SNR) prediction in the fast changing channel model with different modulation modes. In particular, the proposed model can obtain better performance than other methods, and can achieve better performance than other methods without the payload cost of pilot.

Mobile edge computing (MEC) and millimeter wave (mmWave) communications are capable of significantly reducing the network's delay and/or enhancing its capacity. Hence we investigate a mmWave device-to-device (D2D) MEC system, in which user A carries out some computational tasks and shares the results with user B with the aid of a base station (BS). In order to minimize the system's delay, the task can be partitioned into two portions: the first part is computed locally at user A, while the second part is transmitted to the BS and computed by the MEC server. The computational results are then sent to user B through a D2D link and via the link from the BS to user B, over orthogonal time slots. To support computation offloading, both the users and the BS are equipped with multiple antennas and employ A/D hybrid beamforming for their transmission. We develop a novel algorithm for jointly optimizing the offloading ratio and the hybrid beamformers. The simulation results show that the proposed algorithm significantly reduces the system's delay compared to the existing algorithms.

The wireless channel conditions in the future mobile communication systems will become more and more complex as we are developing higher frequency bands, thus it is necessary to adjust the transmission parameters frequently. To ensure the reliability of data transmission, hybrid automatic repeat request (HARQ) techniques are widely used to improve the data throughput of wireless communication systems. This paper develops a polar coded incremental redundancy HARQ (IR-HARQ) scheme based on deep reinforcement learning (DRL) to combat the unexpected channel fluctuations in practice. Specifically, the IR bits are generated by performing quasi-uniform puncturing and polarizing matrix extension on polar codes, and the number of IR bits are optimized by utilizing the deep deterministic policy gradient (DDPG) algorithm in the considered IR-HARQ scheme. Simulation results show that compared with the conventional chase combing scheme and the fixed-length IR-HARQ scheme, the proposed IR scheme can significantly improve the system throughput.

In mobile ad-hoc networks (MANETs), network performance depends strongly on routing, therefore intensive researches about routing protocol have been developed. However, most traditional routing protocols suffer from significant performance degradation as they attempt to find a shortest path without considering the impact of congestion and channel quality. In this work, we propose an intelligent routing algorithm based on prioritized replay double deep Q-network (PRD-DQN). We design two kinds of packet, fast routing (FR) packet and experience transfer (ET) packet, to explore network, and a reward function is defined in which congestion and channel quality are both considered for adaptive routing decision. The simulation results demonstrate that the proposed algorithm can effectively learn a routing strategy with low congestion and high signal to noise ratio (SNR) and outperform the Q-Learning algorithm and the optimized link state routing (OLSR) protocol in terms of convergence speed, end-to-end latency and network throughput.

Media-based modulation (MBM) is a novel method that embedding part or all of the information in the variations of the transmission media. The traditional detection algorithms for MBM system have to estimate the channel state informations (CSIs) to recover the transmitted symbols and they are affected by the number of receiving antennas (RAs). In this paper we propose a deep neural networks (DNN) detector with a novel loss function (LF) which does not require estimation of CSIs and is independent of the number of RA. Numerical results validate that the proposed DNN detector has better bit-error-ratio (BER) performance than that of the traditional detection algorithms and the performance advantage of the proposed LF is also confirmed.

Unmanned aerial vehicle (UAV) swarms (or UAV networks) are vulnerable to jamming attacks due to the shared wireless transmission medium. In order to address this difficulty, frequency hopping based anti-jamming schemes are commonly used in the literature, however, their performance is usually limited due to the unique mobility feature of UAV swarms. In this work, a practical local reaction anti-jamming (LRAJ) scheme is proposed to reduce the packet transmission delay when the jammed nodes are dynamically changing. In the proposed scheme, the jammed nodes and their one-hop neighbors determine their node types at each frequency (channel) by exchanging information about the states of their corresponding frequencies, and performing adaptive frequency hopping (AFH) accordingly. In the mean time, the unjammed nodes can still maintain their normal operations. Therefore, with the aid of the proposed LRAJ scheme, the considered UAV swarm is able to resist malicious jamming attacks in the local area. Simulation results validate the effectiveness of the proposed scheme.

Intelligent reflecting surface (IRS) has been proposed as a promising technology to smartly control the wireless signal propagation and enhance the spectral efficiency of wireless communication systems cost-effectively. The channel state information (CSI) is a crucial factor for the design of optimal passive beamforming in the IRS assisted communication systems. However, acquiring such CSI is very challenging for IRS due to its lack of radio frequency (RF) chains. In this paper, we consider an IRS aided multiple-in single-out (MISO) orthogonal frequency-division multiplexing (OFDM) system and propose a deep learning (DL) based channel estimation method to address the above challenges. In particular, a convolutional neural network is designed to estimate both the direct and cascaded channels of the system considered. Simulation results validate that the proposed DL approach achieves better performance than traditional channel estimation techniques.

The statistical priority-based multiple access protocol (SPMA) is a MAC (medium access control) protocol adopted for the Tactical Targeting Network Technology (TTNT), by virtue of high reliability and low latency. In SPMA, in order to keep the network in a favorable loading situation and ensure 99 percent first time success rate for packets of the highest priority, priority thresholds are normally set to fixed values. However, this fixed threshold strategy is not necessarily optimal when the transmission rate at each node varies due to the change of channel condition. To improve the performance, in this paper, we propose an adaptive priority-setting strategy for SPMA, accounting for the change of transmission rates at network nodes. The performance enhancement from the proposed strategy is validated through numerical simulations. In particular, it is shown that higher throughput and lower latency are achieved when the number of nodes supporting high transmission rate increases, while 99 percent first time success rate for the packets of the highest priority is guaranteed when it decreases.

Orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation technique designed in the delay-Doppler domain, specially suitable for doubly-dispersive fading channels. In general, the conventional message passing (MP) algorithm is capable of eliminating the negative impacts of inter-symbol interferences for data detection in OTFS at the expense of high computational complexity. To reduce the receiver complexity in OTFS systems, we propose a damped generalized approximate message passing (GAMP) algorithm, where the damping factors are optimized based on deep learning (DL) techniques. Specifically, each iteration of the GAMP algorithm is unfolded into a layer-wise structure analogous to a neural network and the damping factors are learned to improve the detection performance. The optimized damping factors can be directly employed in the original GAMP algorithm without increasing its computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and show that it can outperform the classical GAMP algorithm and the MP algorithm.

In this article, we propose a sparse hybrid combining (SHC) scheme for the uplink transmission of beamspace massive multiple-input multiple-output (MIMO) system with low-resolution analog to digital converters (LADCs), to alleviate the performance bottleneck caused by the multi-user interference and quantization noise, with reduced hardware cost and power consumption. To this end, we formulate the optimization of the proposed SHC scheme as a system energy efficiency maximization problem under some practical constraints. The resulting problem contains the highly coupled nonconvex objective function, as well as the discrete binary constraints. By exploiting some fractional programming (FP) techniques and introducing auxiliary variables, we first recast the original challenging problem into a more tractable yet equivalent form. We then develop an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) method to find its local stationary solutions. Finally, simulation results verify the effectiveness of the proposed SHC scheme by numerical examples in terms of the achieved system energy efficiency.

Symbiotic radio (SR) is emerging as a spectrum-and energy-efficient communication paradigm for future passive Internet of Things (IoT), where some single-antenna backscatter devices, referred to as Tags, are parasitic in an active primary transmission. The primary transceiver is designed to assist both direct-link (DL) and backscatter-link (BL) communication. In multi-Tags SR systems, the transceiver designs become much more complicated due to the presence of DL and inter-Tag interference, which further poses new challenges to the availability and reliability of DL and BL transmission. To overcome these challenges, we formulate the stochastic optimization of transceiver design as the general network utility maximization problem (GUMP). The resultant problem is a stochastic multiple-ratio fractional nonconvex problem, and consequently challenging to solve. By leveraging some fractional programming techniques, we tailor a surrogate function with the specific structure and subsequently develop a batch stochastic parallel decomposition (BSPD) algorithm, which is shown to converge to stationary solutions of the GNUMP. The simulation results verify the effectiveness of the proposed algorithm by numerical examples in terms of the achieved system throughput.

In this article, relay-assisted computation offloading (RACO) is investigated, where user A wishes to share the results of computational tasks with another user B with the assistance of a mobile-edge relay server (MERS). To enable this computation offloading, we propose a hybrid relaying (HR) approach employing a pair of orthogonal frequency bands, which are, respectively, used for the amplify-forward relaying of computational results and the decode-forward relaying of the unprocessed raw tasks. The motivation here is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Using this framework, we seek to minimize the weighted sum of the execution delays and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under some practical constraints. By adopting a series of transformations, we first recast this problem into a form amenable to optimization and then develop an efficient iterative algorithm for its solution based on the concave-convex procedure (CCCP). By virtue of the particular problem structure in our case, we propose furthermore a simplified algorithm based on the inexact block coordinate descent (IBCD) method, which leads us to much lower computational complexity. Finally, our numerical results demonstrate the advantages of the proposed algorithms over the state-of-the-art benchmark schemes.

Due to the severe pathloss experienced by electromagnetic waves in the millimeter wave (mmWave) band, a substantial challenge in their design is to have an adequate coverage area. With the objective of improving the coverage area and the sum rate attained, we conceive new full-duplex (FD) mmWave multiple-input multiple-output (MIMO) multiple-relay systems. Specifically, we propose a novel two-timescale analog-digital hybrid beamforming scheme for maximizing the sum rate, while reducing the system's complexity and the channel state information (CSI) signalling overhead, as well as mitigating both the effects of self-interference and that of outdated CSIs caused by the associated delays. In the proposed scheme, the long-timescale analog beamforming matrices are designed based on the available channel statistics and updated in a frame-based manner, where a frame contains a fixed number of time slots. By contrast, the short-timescale digital beamforming matrices are optimized more frequently - namely for each time slot - based on the low-dimensional effective CSI matrices available on a real-time basis. We develop both an efficient analog beamforming algorithm based on the cut-set bound as well as on stochastic successive convex approximation (SSCA) and an innovative digital beamforming algorithm that relies on the theory of penalty dual decomposition (PDD), where our design objective is to maximize the system's sum rate. Both the convergence properties and the computational complexity of the proposed algorithms are also examined. Our simulation results show that the proposed two-timescale hybrid beamforming design significantly outperforms the conventional beamformers both in terms of requiring a lower CSI-signalling overhead and a higher sum rate in the face of realistic outdated CSIs.

In this work, we investigate a full-duplex (FD) millimeter wave (mmWave) multicell system, where the BS of each cell receives signals from uplink (UL) users and transmits signals to downlink (DL) users at the same time, over the same frequency band. We maximize the sum rate lower bound of the FD multicell system by jointly optimizing the digital and analog beamforming matrices at the base station (BS) and the transmit power levels of the UL users under total transmit power constraints and unit-modulus constraints (due to the analog beamforming matrices), in the presence of imperfect channel state information (CSI). The problem under study is very challenging due to the highly non-convexity of the objective function and constraints. We transform this problem into an equivalent but more tractable form and propose a novel iterative algorithm based on the penalty dual decomposition (PDD) to solve it. The proposed algorithm is guaranteed to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of the original problem. Moreover, we also extend our proposed algorithm to the structure of subarray. Simulation results validate the effectiveness of the proposed algorithm as compared with conventional nonrobust and half-duplex (HD) algorithms.

In many applications in wireless communications, we need to recover a structured sparse signal from a linear measurement model with uncertain sensing matrix. There are two challenges of designing an algorithm framework for this problem. How to choose a flexible yet tractable sparse prior to capture different structured sparsities in specific applications? How to handle a sensing matrix with uncertain parameters and possibly correlated entries? As will be explained in the introduction, existing common methods in compressive sensing (CS), such as approximate message passing (AMP) and variational Bayesian inference (VBI), may not work well. To better address this problem, we propose a novel Turbo-VBI algorithm framework, in which a three-layer hierarchical structured (3LHS) sparse prior model is proposed to capture various structured sparsities that may occur in practice. By combining the message passing and VBI approaches via the turbo framework, the proposed Turbo-VBI algorithm is able to fully exploit the structured sparsity (as captured by the 3LHS sparse prior) for robust recovery of structured sparse signals under an uncertain sensing matrix. Finally, we apply the Turbo-VBI framework to solve two application problems in wireless communications and demonstrate its significant gain over the state-of-art CS algorithms.

We propose a novel randomized channel sparsifying hybrid precoding (RCSHP) design to reduce the signaling overhead of channel estimation and the hardware cost and power consumption at the base station (BS), in order to fully harvest benefits of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. RCSHP allows time-sharing among multiple analog precoders, each serving a compatible user group. The analog precoder is adapted to the channel statistics to properly sparsify the channel for the associated user group, such that the resulting effective channel (product of channel and analog precoder) not only has enough spatial degrees of freedom (DoF) to serve this group of users, but also can be accurately estimated under the limited pilot budget. The digital precoder is adapted to the effective channel based on the duality theory to facilitate the power allocation and exploit the spatial multiplexing gain. We formulate the joint optimization of the time-sharing factors and the associated sets of analog precoders and power allocations as a general utility optimization problem, which considers the impact of effective channel estimation error on the system performance. Then we propose an efficient stochastic successive convex approximation algorithm to provably obtain Karush-Kuhn-Tucker (KKT) points of this problem.

Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network's edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

This work studies the joint problem of content placement, remote radio head (RRH) clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, downlink users are cooperatively served by multiple RRHs, in turn connected to a centralized baseband unit (BBU) pool via fronthaul links. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without utilizing the fronthaul links. We aim to jointly optimize the aforementioned three aspects, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. Two joint design algorithms are presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables, namely: the penalty concave-convex procedure (P-CCCP) and penalty dual decomposition (PDD) based algorithms. Furthermore, since content placement is usually updated over a larger timescale, we propose a two-timescale joint design algorithm, where the P-CCCP and PDD-based algorithms can be employed for efficient initialization as well as for establishing performance limits. Simulation results validate the efficiency of the proposed algorithms.

This work studies the joint problem of power and trajectory optimization in a rotary-wing unmanned aerial vehicle (UAV)-enabled mobile relaying system. In the considered system, in order to provide convenient and sustainable energy supply to the UAV relay, we consider the deployment of a power beacon (PB) which can wirelessly charge the UAV and it is realized by a properly designed laser charging system. To this end, we propose an efficiency (the weighted sum of the energy efficiency during information transmission and wireless power transmission efficiency) maximization problem by optimizing the source/UAV/PB transmit powers along with the UAV's trajectory. This optimization problem is also subject to practical mobility constraints, as well as the information-causality constraint and energy-causality constraint at the UAV. Different from the commonly used alternating optimization (AO) algorithm, two joint design algorithms, namely: the concave-convex procedure (CCCP) and penalty dual decomposition (PDD)-based algorithms, are presented to address the resulting non-convex problem, which features complex objective function with multiple-ratio terms and coupling constraints. These two very different algorithms are both able to achieve a stationary solution of the original efficiency maximization problem. Simulation results validate the effectiveness of the proposed algorithms.

Massive connectivity is regarded as a key requirement for future networks to support new communication paradigms, where the human-type communications coexist with machine-type communications. Owing to the limited coherence time but the huge number of potential devices, it is impossible to allocate mutually orthogonal pilot sequence for all potential devices, which may impose severe interference on the device activity detection and channel estimation. Existing nonorthogonal pilot design methods for conventional cellular network are not suitable for the massive connectivity regime. To overcome this challenge, we first formulate the pilot sequences design as an optimization problem to minimize the average mean square error (MSE) of channel estimation under the individual power constraint. The proposed optimization problem is nonconvex and highly coupled. By exploiting some approximation techniques, we convert the problem into a more tractable form and subsequently develop a distributed algorithm based on the matrix fractional programming (FP) and the alternating direction method of multipliers (ADMM) methods. Simulations validates that the proposed scheme not only achieves significant gains in channel estimation over state-of-the-art baseline schemes, but also improves the device activity detection performance.

This work studies the joint problem of optimal content placement, RRH clustering and beamformer design, in a cache-enabled cloud-radio access network (C-RAN). In the considered system, multiple remote radio heads (RRHs) connected to a centralized baseband unit (BBU) pool via fronthaul links, cooperatively serve the downlink users by grouping them into potentially overlapping clusters. Each RRH is equipped with a local cache from which it can directly acquire the requested user contents, without the need to occupy the fronthaul links. We aim to jointly optimize the caching placement, user association and downlink beamforming vector at each RRH, in order to strike a balance between fronthaul traffic reduction and transmission power minimization. To this end, we propose to employ the ratio between these two important system utilities as the objective function, referred to as caching efficiency. A penalty dual decomposition (PDD) based algorithm is presented to address the resulting nonconvex optimization problem, which features coupling constraints and mixed-integer variables. Simulation results validate the efficiency of the proposed algorithm.

In this paper, we present a secure transmission scheme to improve the secrecy capacity of wiretap systems by blending the benefits of polar code and artificial noise (AN). In the considered system, a transmitter tries to communicate with a receiver without leaking any confidential information to an eavesdropper. We propose a new system utility function, referred to as message secrecy capacity (MSC), which is obtained by integrating the code rate of polar code into the conventional secrecy capacity. Then, in order to maximize the secrecy capacity of the information bits in polar code, we formulate a max-min optimization problem to optimize the powers allocated among useful signals and the AN. To address the highly non-convexity of the considered problem, we propose a concave-convex procedure (CCCP)-based algorithm by introducing some carefully designed auxiliary variables, and convergence to the set of KarushKuhn-Tucker (KKT) solutions is guaranteed. Numerical results demonstrate the effectiveness of the proposed MSC objective function and power allocation scheme.

In non-orthogonal multiple access (NOMA) systems, the outage is considered to happen when a user cannot correctly decode the messages for the users with higher decoding order and hence the successive interference cancellation (SIC) is failed in traditional definition. However, in this case, the user may still correctly decode its message by treating the uncancelled signal as interference and the outage is avoided. By considering this behavior, a more accurate alternative outage probability can be defined. In this paper, we investigate user scheduling and power allocation for a downlink NOMA system with imperfect SIC by employing the alternative outage probability as then performance metric. The coupling of user scheduling and power allocation makes the problem complicated. Therefore, we propose a two-phase algorithm, in which the user scheduling is first optimized through a matching theory based algorithm, and then power allocation is performed with the aid of the concave-convex procedure (CCCP) method. Simulation results show that the proposed low- complexity algorithm can achieve near-optimal performance and the algorithm based on the alternative outage probability outperforms the traditional one when the decoding is significantly affected by imperfect SIC.

The scheduling in a dense wireless network with interfering links is a very challenging problem, especially in the environments with additional jammers. In this work, we propose a joint jamming detection and link scheduling method based on deep neural networks (DNN). The proposed method admits a branched structure and mainly consists of two subnetworks, where the first subnetwork aims to detect and locate the jammer by utilizing the geographical information and received signal power, while the second one determines the link scheduling with the aid of the previously obtained jamming detection results. Furthermore, inspired by the multi-task learning method, we propose a hybrid-goal training approach to accelerate the training process. Numerical experiments have confirmed that the proposed DNN-based solution can achieve both superior jamming localization accuracy and highly competitive link scheduling performance.

Many important application scenarios in the future fifth generation (5G) systems, such as indoor navigation and autonomous driving, rely on accurate localization of users. In this paper, we propose a sparse-Bayesian-inference (SBI) based direct location algorithm for massive MIMO systems, which can exploit the sparse and high-resolution nature of angle of arrival (AoA) and any available statistical location information (SLI), to significantly improve the user localization accuracy. The existing common methods in SBI, such as approximate message passing (AMP) an variational Bayesian inference (VBI), may not work well for the massive MIMO localization problem due to their respective drawbacks. To overcome these drawbacks, we first propose a novel three-layer hierarchical structured (3LHS) sparse prior model to incorporate both the structured sparsity of the massive MIMO channel and the SLI into the SBI-based localization formulation. Then we propose a structured VBI algorithm called 3LHS-VBI to solve the resulting SBI-based localization problem. Finally, simulations verify the superior performance of the proposed location algorithm.

Mobile Ad-hoc Network (MANET) is a self-configuring network that is widely used but vulnerable to the malicious jammers in practice. In this paper, we consider a jamming channel problem in MANET where a jammer intermittently interrupts the communication channels and the transmitter needs to determine which time slot to send data in order to avoid the interruption. Learning from the historical experience, a Deep Q-Network (DQN) based approach is proposed to generate transmission decisions at the transmitter. In addition, a variant of DQN, termed adaptive DQN, is introduced to cope with the change of jamming conditions. The simulation results demonstrate that the proposed scheme can learn an optimal policy to guide the transmitter to avoid jamming more quickly and efficiently than a Q-learning baseline. Moreover, the effectiveness and robustness of the adaptive DQN is also numerically verified.

In a mobile edge computing (MEC) system with a large number of low power mobile terminals, proper computation offloading and resource allocation is crucial to achieving desirable system performance. In this paper, we consider the joint computation offloading and resource allocation problem for an uplink MEC system under the min-max fairness criterion. The proposed optimization problem is difficult to solve due mainly to the nonconvex nondifferentialbe objective and the nonlinear coupling of design variables in the constraints. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then develop a novel algorithm based on the concave-convex procedure (CCCP) technique to address the problem. Furthermore, by exploiting the problem structure, an efficient algorithm based on inexact block coordinate descent (IBCD) method is proposed to reduce the computational complexity. Numerical results validate the efficiency of the proposed algorithms.

Cooperative localization can compensate weaknesses of traditional localization techniques which do not operate well in harsh environment. However, cooperative localization signals increase the interference power for communication. In this work, we seek to joint localization and transmission power in order to maximize the transmission rate of secondary user under the power budget and primary users' outage constraints. At the same time, we consider the trade-off between localization error and localization interference when formulating the above problem in cooperative localization. The proposed optimization problem is nonconvex and highly coupled, which is challenging to solve. To simplify the problem, we introduce some auxiliary variables to the original optimal problem and apply a algorithm based on concave-convex procedure (CCCP). The simulation results demonstrate the advantages of location-aware network based on cooperative localization.

In this paper, we investigate the joint design of hybrid beamforming and demanding computation tasks offloading in mmWave-based mobile edge computing (MEC) systems, in order to minimize the maximum latency. The resulting optimization problem is challenging, mainly due to the highly nonlinear objective function and the unit modulus constraints on the analog beamformers. By seeking the special structure of the problem, we divide it into two separate problems. An iterative weighted mean-square error minimization (WMMSE) approach is adopted to address the first optimization problem, and the second problem is solved in closed-form. We also investigate a more practical scheme when only finite resolution phase shifters are implemented. Simulation results are provided to confirm that the proposed strategy achieves significant better performance than recent reported beamforming algorithm with fixed offloading ratio, and it is effective when low-resolution phase shifters are used.

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified l0-norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

In this paper, we investigate joint trajectory design and resource allocation algorithms to maximize the minimum average rate among ground users for unmanned aerial vehicle (UAV) communication systems, where both the orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) modes are considered. We first formulate the problems for UAV communications with the OMA and NOMA modes, respectively, which contain binary variables and highly coupled nonconvex objective functions and constraints. In order to handle the challenging problems, we transform the original problems into more tractable forms and then develop novel algorithms based on penalty dual-decomposition technique to solve them. Simulation results show that the proposed algorithms outperform the benchmarks.

The emerging cloud applications require group communications. For these applications, multicast is a better choice than unicast, because it can significantly improve the performance by eliminating the duplicated packets generated by servers. However, existing multicast schemes for datacenters are either based on IP multicast or centralized scheduling. IP multicast is inefficient for datacenters as it cannot take full advantage of the multipath property. And centralized schemes suffer from single-point failure and scalability problems. To solve these problems, we propose MiniForest, a distributed multicast framework for large-scale datacenter networks. It consists of new routing algorithms and a dynamic group management mechanism. A new address mapping solution is then designed for compatibility to existing upper-layer applications. Based on the mapping solution, we propose an efficient load balancing strategy, with which a minimal forest is constructed for all multicast trees. To study the performance of the new multicast scheme in theory, we further provide an analytical model for Clos-based datacenter networks and analyze the overloading behaviors from a new perspective. We show that the distributed scheme can be used in any size of datacenters. It has much lower complexity and better performance than centralized schemes.

In this article, we investigate the application of NOMA in mobile unmanned aerial vehicle (UAV) communication networks and propose the algorithm to jointly optimize the UAV trajectory and power allocation. Specifically, we formulate the optimization problem to maximize the minimum average rate among ground users for NOMA based UAV communication systems, which contains complicated and discrete binary constraints, as well as the highly coupled nonconvex objective function. Then, we transform the challenging original problem into a more tractable form with some equality constraints. Finally, we develop a double-loop algorithm to solve it with the aid of penalty dual-decomposition (PDD) technique. From the simulation results, the proposed algorithm outperforms the benchmarks.

In massive MIMO aided cloud radio access network (C-RAN), plenty of remote radio heads (RRHs), each equipped with a massive MIMO array, are distributed within a specific geographical area and are connected to a centralized baseband unit (BBU) pool through fronthaul links. One major performance bottleneck in the uplink of massive MIMO aided C-RAN is that, the RRHs need to transport a huge amount of data to the BBU for baseband processings. Existing fronthaul compression methods that rely on fully-digital processing are not suitable for the massive MIMO regime due to their high implementation cost. To overcome this challenge, we propose a two-timescale hybrid analog-and-digital spatial compression scheme at RRHs to reduce the fronthaul data, where the analog filter is updated at a slow timescale according to the channel statistics to achieve massive MIMO array gain, and the digital filter is updated at a fast timescale according to the instantaneous effective channel state information (CSI) to achieve spatial multiplexing gain. Such a design can alleviate the performance bottleneck of limited fronthaul with reduced hardware cost and power consumption, and is more robust to the CSI delay. We propose an online algorithm for the two-timescale non-convex optimization of analog and digital filters. Simulations verify the advantages of the proposed scheme over state-of-the-art baseline schemes.

This paper investigates a multiuser mobile edge computing system under interference channels, where mobile users can offload their latency-sensitive (computation-intensive) tasks to the mobile edge server via a base station (BS). In this work, we seek to jointly optimize the user selection indicators for offloading and the computation resources, as well as the transmit power level of the offloading users in order to minimize the system energy consumption under latency-sensitive, computation and transmit power budget, transmission quality, and user selection constraints. The proposed optimization problem is nonconvex and highly coupled, which is difficult to solve. By exploiting binary relaxation and introducing auxiliary variables, we first convert this problem into a more tractable form. We then propose a concave-convex procedure (CCCP) based algorithm to obtain the resulting problem. Furthermore, a simplified algorithm is proposed to reduce the computational complexity. Simulation results are proposed to verify the proposed algorithms.

This paper considers a computational results sharing (CRS) system where user A wants to share its computational results with user B with the aid of a relay equipped with an mobile edge computing (MEC) server. The performance of the CRS systems can be greatly impacted by the relay forward protocol and resources allocation. To realize cooperative computation and communication in a relay aided mobile edge computing system, we develop a hybrid relay forward protocol and properly allocate the system computational and communication resources, where we seek to balance the execution delay and network energy consumption. The problem is formulated as a nondifferentialbe optimization problem which is nonconvex with highly coupled constraints. By exploiting the problem structure, we propose a lightweight algorithm based on inexact block coordinate descent method. Our results show that the proposed algorithm exhibits much faster convergence as compared with the popular concave-convex procedure based algorithm, while achieving good performance.

Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation (FBMC-OQAM) has been intensively studied, and becomes a very potential candidate in future wireless communication system because of its numerous advantages. This paper presents a framework of Artifical Neural Network (ANN)-aided receiver design for the FBMC system. Specifically, two new joint channel estimation and equalization architectures are developed, which are based on two classical ANN algorithms, Multi-layer Perceptron (MLP) and Functinal Link Artificial Neural Network (FLANN). In addition, a powerful Loss Function (LF) is proposed by combining intrinsic characteristics of FBMC and is applied in the ANN-aided FBMC receiver. Numerical results validate the effectiveness of the proposed ANN-aided design and demonstrate its remarkable bit-error-ratio (BER) performance under multi-path channel environment. Furthermore, the performance advantage of the proposed LF is also confirmed by simulations.

The filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has attracted great interest in recent years for its low Adjacent Channel Leakage Ratio (ACLR). However, the problem of high peak-to-average power ratio (PAPR) has negative impact on the energy efficiency of the FBMC system. In this paper, we investigate PAPR reduction of FBMC-OQAM signals based on partial transmit sequence techniques (PTS). It has been shown that a trellis-based PTS scheme with multi-block joint optimization (MBJO) is superior to most schemes based on symbol-by-symbol PTS approach. However, the complexity of this scheme is undesirable in most cases. Several suboptimal solutions have been introduced in order to reduce the complexity at the expense of significant performance loss. To achieve a better complexity-performance tradeoff, we propose an improved scheme to approach the performance of trellis-based PTS with a lower complexity. The simulation results confirm that the proposed scheme effectively reduces the PAPR of FBMC-OQAM signals.

Sparse Code Multiple Access (SCMA) is a novel non- orthogonal multiple access technique for 5G systems. In this paper, we propose a suboptimal algorithm for SCMA codebook design over uplink Rayleigh fading channels. In uplink channels, the pairwise error probability (PEP) is derived firstly, based on which the diversity gain and the coding gain of SCMA are analyzed. According to the analysis, the multi-dimensional mother constellation design is formulated as a non-convex optimization problem, aiming to enlarge two factors to achieve a better coding gain. Furthermore, the optimization problem is simplified as a convex second-order cone programming problem to obtain a suboptimal solution. Simulation results show that the bit error ratio (BER) performance is improved significantly on account of the enlarged factors.

In this paper, we demonstrate a visible light communication (VLC) system using continuous phase modulation (CPM) and prove its advantage in energy efficiency over orthogonal frequency-division multiplexing (OFDM) scheme. Furthermore, to ensure the feasibility of broadband communications in indoor optical multipath environments, we propose algorithms including linear decomposition-based channel compensation to combat the inter-symbol-interference (ISI) and correlation matrix-based highly power-efficient CPM signal reconstruction to recover the original CPM signal without multipath effects. Additionally, in the signal reconstruction algorithm, a condition-enhanced method is proposed to solve the condition number problem of correlation matrix. The algorithms are verified and evaluated in terms of the normalized mean squared error (NMSE) simulation and bit error rate (BER) experiments. The power efficiency, the fading compensation character promote the proposed CPM transceiver to be a practical VLC candidate.

The main problem of the integrated waveform design for communication and navigation is timing. The timing accuracy requirement in navigation systems is much higher than that in communication systems. In order to solve this problem, a flexible design of waveform and its tracking algorithm is proposed in this paper. The proposed waveform is composed of PN sequences and subcarriers with different rates. The proposed tracking algorithm is based on the combination of tracking results of all the sequences and subcarriers. In addition, the performance of timing accuracy and multipath mitigation of proposed waveform is analyzed. Simulation results show that the proposed waveform has enhanced timing and multipath mitigation performance which are better than that of traditional spread spectrum communication waveform.

In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper considers the uplink of multiuser multiple-input multiple-output systems, where several mobile stations (MSs) cooperatively transmit hybrid messages, including common messages and private messages, to a single base station (BS). We aim to jointly design transmit precoding at the MSs' side and antenna selection at the BS side to maximize the achievable system throughput while reducing implementation complexity. The problem at hand is nonconvex and difficult to solve due to the antenna selection constraint. By exploiting the problem structure and linear relaxation, we propose using the Frank-Wolfe method and the well-known weighted mean-square error minimization approach to tackle the problem, leading to an efficient iterative algorithm. Moreover, due to the large number of antennas, the sparsity of antenna selection is also taken into account by introducing an l0-norm penalty function into the objective function. To tackle this nonconvex and discontinuous problem, we resort to quadratic approximation with smooth optimization and extend our proposed algorithm to the sparse optimization problem. The convergence of the proposed algorithms is analyzed and its effectiveness is verified by numerical examples in terms of the achieved system throughput.

This paper studies the multiple-input multiple- output (MIMO) millimeter wave (mmWave) systems with non-linear power consumption model for 5G network. A new non-linear power consumption model is investigated, consisting of the power cost generated by the circuit and the non-linear power amplifiers. In this work, we aim to optimize the hybrid transceiver to maximize the system capacity subject to the resultant non-linear power constraint. In order to address this problem, we first transform the original optimization problem to a more tractable problem based on the weighted minimum mean squared error (WMMSE) approach. Then, we propose a novel transceiver design algorithm based on the penalty dual decomposition (PDD) optimization framework to address this problem. Moreover, a simplified algorithm is also proposed by using linear approximation. The effectiveness of the proposed algorithm is verified by simulation results.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output (MU-MIMO) millimeter wave (mmWave) systems, where a number of mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation (5G) wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers (LNA). We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog) and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems which are solved via an alternating optimization (AO) method. Specifically, the antenna selection matrix is optimized via the concave-convex procedure (CCCP); the weighted mean-square error minimization (WMMSE) approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization (MO). The convergence of the proposed algorithm is analysed and its effectiveness is verified by simulation.

Wireless transmission with lens antenna arrays is becoming more and more attractive for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with limited radio frequency (RF) chains due to their energy-focusing capability. In this paper, we consider the joint design of beam selection and precoding to maximize the sum rate of a downlink single-sided lens MU-MIMO mmWave system under transmit power constraints. We first formulate the optimization problem into a tractable form using the popular weighted minimum mean squared error (WMMSE) approach. To solve this problem, we then propose an efficient joint beam selection and precoding algorithm based on the innovative penalty dual decomposition (PDD) method. Simulation results demonstrate that our proposed algorithm can achieve near-optimal performance when compared to the fully digital precoding scheme and thus outperform the competing methods.

Infrastructure-less and energy-limited multi-hop ad- hoc networks are vulnerable to the Denial-of-service (DoS) attacks launched by malicious jammers. In this paper, we propose a power allocation scheme with cooperative anti-jamming strategy to enhance communication reliability against jamming in ad-hoc networks under limited resource. In the proposed anti-jamming strategy, multiple relays are employed to assist the transmission in multiple hops. Under this strategy, we propose the optimal power allocation scheme with the objective of minimizing the outage probability at the destination. We decompose the outage minimization problem into two sub-problems and obtain the near-optimal solution of each sub-problem based on the upper bound of the outage probability. Simulation results demonstrate the effectiveness of the cooperative jamming strategy and the power allocation scheme proposed.

Millimeter wave (mmWave) systems with large number of antennas are considered as an enabling technology for the fifth generation (5G) cellular networks. For such systems, the traditional fully digital precoding is impractical due to the high cost of radio frequency (RF) chains. To overcome this difficulty, hybrid precoding (HP) is considered. However, in the conventional HP architecture, the number of analog phase shifters (APSs) and RF adders increases linearly with respect to the number of transmit antennas, leading to considerable energy consumption. In this paper, an energy-efficient weighted minimum mean square error (WMMSE) based hybrid precoding algorithm is proposed to maximize the achievable system sum-rate. Through a novel adaptive connection network, a partially-connected HP structure is employed to implement the design of the RF precoder, where each RF chain is connected to only a part of antennas. Given the RF precoder and combiner, we can optimize the baseband precoder and combiner by using WMMSE algorithm. Numerical results show that the proposed algorithm can achieve near optimal sum-rate performance and significantly improve the energy efficiency as compared to the conventional HP algorithm.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), which uses the subcarrier indices as a source of information, has attracted considerable interest recently. Motivated by the index modulation (IM) concept, we build a circular convolution filter bank multicarrier with index modulation (C-FBMC-IM) system in this paper. The advantages of the C-FBMC-IM system are investigated by comparing the interference power with the conventional C-FBMC system. As some subcarriers carry nothing but zeros, the minimum mean square error (MMSE) equalization bias power will be smaller comparing to the conventional C-FBMC system. As a result, our C-FBMC-IM system outperforms the conventional C-FBMC system. The simulation results demonstrate that both BER and spectral efficiency improvement can be achieved when we apply IM into the C-FBMC system.

This work studies the joint transceiver design for a full-duplex (FD) cloud radio access network (C- RAN) with simultaneous wireless information and power transfer (SWIPT). In the considered network, a number of FD remote radio heads (RRHs) receive information from uplink users (UUs), while transmitting both information and energy to a set of half-duplex (HD) downlink users (DUs) with power splitting receivers. Based on the particular problem structure, a block coordinate descent (BCD) method is proposed to minimize the total transmission power subject to both uplink-downlink quality of service (QoS) constraints and energy harvesting (EH) constraints. Although the problem has complicated constraints coupling a set of transceivers, uplink transmit power levels, and receive power splitting ratios, we prove that the proposed BCD algorithm converges to a Karush-Kuhn- Tucker (KKT) solution. Simulation results validate the effectiveness of the proposed algorithm as compared with the traditional HD scheme.

Sparse code multiple access (SCMA) is a novel non‐orthogonal multiple access scheme for 5G systems, in which the logarithm domain message passing algorithm (Log‐MPA) is applied at the receiver to achieve near‐optimum performance. However, the computational complexity of Log‐MPA detector is still a big challenge for practical implementation, especially for energy‐sensitive user equipments in the downlink scenario. A region‐restricted detector with an improved Log‐MPA (RRL detector) is proposed for downlink SCMA systems, in which the complexity is reduced from two perspectives. To avoid unnecessary calculations when searching the superposition constellation exhaustively, the proposed RRL detector updates the function nodes only within a restricted search region. While constellation points outside the search region are neglected, the performance is well maintained which is verified by simulations. Besides, the original Log‐MPA heavily relies on exponential operations, resulting in high computational complexity. To solve this problem, an improved Log‐MPA is also put forward in this study to make a better compromise between complexity and performance. Simulation results show that the complexity of the RRL detector is reduced considerably while the bit error rate performance degrades unnoticeably.

This study investigates the problem of physical layer security for amplify‐and‐forward (AF) multiple‐input multiple‐output (MIMO) relay systems operating in the presence of a passive eavesdropper. Specifically, the authors consider the robust design of an artificial noise (AN)‐assisted non‐linear transceiver employing Tomlinson–Harashima precoding (THP), with imperfect knowledge of the legitimate channel states. The design problem can be reformulated as a two‐level optimisation, where the outer problem aims to optimise the source precoder as a function of the relay precoder, while the inner problem at the relay aims to jointly optimise the relay precoder as well as the power allocation between the AN and the information‐bearing signals. To solve the inner problem, the authors adopt a bisection method which attempts to maximise the AN power level, to confuse the eavesdropper, while satisfying the mean‐squared‐error requirement for the intended user. Some relaxation for the objective function is applied to transform the problem into a standard convex optimisation one. Regarding the outer problem, closed‐form solutions for the precoders can be derived by an iterative method based on the Karush–Kuhn–Tucker conditions. Simulation results illustrate the superior secrecy performance provided by the proposed non‐linear transceiver design with AN and THP.

Orthogonal frequency division multiplexing with index modulation (OFDM‐IM) has attracted considerable interest recently. The technique uses the subcarrier indices as a source of information. In filter bank multicarrier (FBMC) system, double‐dispersive channels lead to inter‐carrier interference and/or inter‐symbol interference, which are caused by the neighbouring symbols in the frequency and/or time domain. When the authors introduce index modulation to the FBMC system, the interference power will be smaller comparing with that of the conventional FBMC system as some subcarriers carry nothing but zeros. In this study, the advantages of FBMC with index modulation (FBMC‐IM) are investigated by comparing the signal to interference ratio with that of the conventional FBMC system. However, the bit error rate (BER) performance is affected since there exists interference in the FBMC‐IM system. To improve the BER performance, the authors propose an optimal combination‐selection algorithm and an optimal combination‐mapping rule. By abandoning some combinations whose error probability are larger and by mapping the remaining combinations into specified bits, a better BER performance can be achieved compared with that without optimisation. The theoretical analysis and simulation results clearly show the FBMC‐IM system has a good BER performance under double‐dispersive channels.

The authors propose a novel turbo detection scheme based on the factor graph (FG) serial‐schedule belief propagation equalisation algorithm with low complexity for single‐carrier faster‐than‐Nyquist (SC‐FTN) and multi‐carrier FTN (MC‐FTN) signalling. In this work, the additive white Gaussian noise channel and multi‐path fading channels are both considered. The iterative FG‐based equalisation algorithm can deal with severe intersymbol interference and intercarrier interference introduced by the generation of SC and MC‐FTN signals, as well as the effect of multi‐path fading. With the application of Gaussian approximation, the complexity of the proposed equalisation algorithm is significantly reduced. In the turbo detection, low‐density parity check code is employed. The simulation results demonstrate that the FG‐based turbo detection method can achieve satisfactory performance with low complexity.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output millimeter wave systems, where several mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers. We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog), and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate under a set of constraints. The corresponding optimization problem is nonconvex and difficult to solve, mainly due to the receive antenna selection and constant modulus constraints on the analog receiving matrix. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems, which are solved via an alternating optimization method. The latter iteratively updates the antenna selection matrix, the transmit beamforming vectors, and the hybrid receiving matrices by sequentially addressing each subproblem while keeping the other variables fixed. Specifically, the antenna selection matrix is optimized via the concave-convex procedure; the weighted mean-square error minimization approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization. The convergence of the proposed algorithm is analyzed and its effectiveness is verified by simulation.

This paper investigates the design of robust nonlinear transceivers conceived for multiple-input multiple-output full-duplex wireless-powered relay networks in the face of realistic imperfect channel state information (CSI). A novel self-energy recycling aided relaying protocol is employed, whereby the relay node benefits from energy harvesting (EH) gleaned from the self-interfering link in addition to its primary energy. The proposed nonlinear transceiver relies on a Tomlinson-Harashima (TH) precoder along with an amplify-and-forward (AF) relaying matrix and a linear receiver, where the TH precoder is composed of a feedback matrix and a source precoding matrix. Two different criteria are considered for the robust design of the nonlinear transceiver in the presence of channel estimation errors modeled by the Gaussian distribution. The first one aims to minimize the mean-squared-error (MSE) at the destination subject to a transmit power constraint at the source and an EH constraint at the relay. The resultant optimization problem is converted to four subproblems and solved via an alternating optimization (AO) algorithm that iteratively updates the transceiver coefficients by sequentially addressing each subproblem, while keeping the other matrix variables fixed. The second design criterion aims to minimize the transmit power at the source under both MSE and EH constraints. Similarly, an AO-based iterative algorithm is proposed for solving this problem. Our simulation results show that the robust design advocated is capable of alleviating the effects of CSI errors, hence improving the robustness of the system over that of the corresponding linear designs.

This paper studies joint transceiver design for a full-duplex (FD) cloud radio access network with simultaneous wireless information and power transfer. In the considered network, a number of FD remote radio heads receive information from uplink users, while transmitting both information and energy to a set of half-duplex (HD) downlink users with power splitting receivers. We aim to minimize the total power consumption with both uplink-downlink quality of service constraints and energy harvesting constraints. The resulting problem is challenging, because various design parameters, such as the transceiver beamformers, the uplink transmit power, and the receive power splitting ratios, are tightly coupled in the constraints. Four different solution approaches are proposed for the joint transceiver design problem, each one leading to a different numerical algorithm. In particular, a block coordinate descent method is proposed, and by exploiting the problem structure, we prove that the algorithm converges to a Karush-Kuhn-Tucker solution, despite the coupling of various design variables in the constraints. Simulation results validate the effectiveness of the proposed algorithms as compared with the traditional HD scheme.

This paper addresses the transceiver design problem for secure downlink communications over a multiple-input multiple-output (MIMO) relay system in the presence of multiple eavesdroppers. A new algorithm based on alternating optimization (AO) is first proposed to maximize the signal-to-noise ratio (SNR) of a legitimate receiver under power constraints at the base station (BS) and the relay station (RS) and a set of secrecy constraints, by using the semidefinite relaxation (SDR) technique. To reduce complexity, a simplified design algorithm based on switched relaying (SR) is also proposed, in which both the BS and the RS are equipped with a codebook of permutation matrices. Based on this codebook, we construct a number of latent transceivers, each consisting of a BS beamforming vector and an optimally scaled RS permutation matrix. We use the bisection search and second-order cone programming (SOCP) techniques to design each latent transceiver and choose the optimal one with the largest SNR. We also develop an efficient approach to construct the codebook of permutation matrices. Our results show that the SR based algorithm significantly reduces the computational complexity while maintaining a similar performance to the AO based algorithm.

In the Low Density Signature (LDS) based multiple access systems, the constellation of chip symbols superposed by several users' symbols is named as superposition constellation. When LDS is applied into the downlink multiple access scenario, maximizing the minimum Euclidean distance of the superposition constellation will be a big challenge. In this paper, by optimizing the superposition constellation as square QAM, we propose a novel LDS matrix construction algorithm. Besides, the original Message Passing Algorithm (MPA) detector for LDS system leads to high complexity which is a big problem for energy-sensitive user equipment. We propose a low complexity region-restricted MPA detector by restricting the search region to a quadrant-like part of the superposition constellation. The complexity of the new detector is reduced to 25%~50% while performance in terms of bit error rate (BER) maintains almost the same. The theoretical analysis and simulation results further verify the effectiveness of our proposed algorithms.

Filter Bank Multicarrier (FBMC) modulation is thought to be important in 5th generation (5G) wireless communication systems for its higher spectral efficiency than conventional orthogonal frequency division multiplexing (OFDM). In this paper, we apply a kind of soft-input-soft-output (SISO) factor-graph-based maximum-a-posterior (MAP) detector in FBMC systems. The detector has better performance than simple minimum mean square error (MMSE) and zero forcing (ZF) equalizers in coded systems. Its computational complexity grows linearly with the number of interferences which means a good tradeoff between performance and complexity. The simulation results show the good performance of the detector. And the complexity is also analyzed in this paper.

In this paper, a novel joint secure resource allocation optimization is proposed for full-duplex (FD) relay networks with cooperative jamming (CJ) in the presence of multiple source-destination (SD) pairs and an eavesdropper. We first derive the expression of the secrecy capacity for a single FD relay link with CJ. Then the joint power allocation and relay subchannel assignment (JPARA) optimization is proposed to maximize the sum secrecy capacity of the network. The proposed optimization is evaluated by numerical results, which prove that significant performance gain can be achieved by full-duplex relays when the self-interference is well suppressed. Besides, the cooperative jamming scheme is shown to improve the throughput effectively in the FD mode, while higher gap tends to be achieved by CJ in the HD mode.

In this paper, we investigate the carrier frequency offsets (CFOs) effect on uplink universal filtered multi-carrier-interleave division multiple access (UFMC-IDMA), which has improved robustness against inter-carrier interference (ICI) compared with orthogonal frequency division multiplexing-IDMA (OFDM-IDMA). In particular, a multiuser detection algorithm for uplink UFMC-IDMA which is robust against CFOs is proposed. The proposed algorithm takes the CFOs impact into account in the detection process and iteratively mitigates the combined interference from other users and CFOs. In addition, a corresponding approximation method with reduced complexity is also developed, which omits the interference elements whose values are small. Simulation results validate the superior interference cancellation performance of the proposed algorithm and reveal that the performance loss of the low complexity approximation method is small compare with the proposed algorithm with full calculation.

In this paper, a low-complexity turbo detection scheme based on a weighted factor graph (FG) serial-schedule (SS) belief propagation (BP) equalization method is proposed for Faster-than-Nyquist (FTN) signaling. The iterative equalization method is applied to mitigate the severe intersymbol interference (ISI) introduced by FTN signaling. In order to reduce the complexity of the equalization method, Gaussian approximation (GA) is used to calculate the log-likelihood ratio (LLR). Thus, the computational complexity is merely linear with the number of ISI taps. Furthermore, LDPC, as an efficient coding technique, results in performance that approaches the Shannon limit in this turbo detection scheme. The simulation results show that the proposed weighted FG-SS-BP-based turbo detection method performs close to the optimal detector under ISI-free conditions with very low complexity.

This paper investigates the design of vector perturbation (VP) precoding using lattice reduction (LR) based on a multi-branch (MB) strategy for multi- user multiple-input multiple-output (MU-MIMO) systems. The MB strategy constructs a group of branches for transmitting data streams according to a pre-designed ordering scheme. For each branch, an LR-aided minimum mean square error (MMSE) VP precoder is proposed and three methods are devised for the perturbation vector design. We also develop an effective scheme to design the transmit ordering patterns with appropriate structures and a suitable selection mechanism to choose the best one. Simulation results show that the proposed MB-LR-MMSE-VP algorithm achieves a better bit error rate (BER) performance than existing VP precoding schemes.

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction consists of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.

In this paper, we investigate a multiuser multiple-input single-output relay system with simultaneous wireless information and power transfer, where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, we study the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix, and the power splitting (PS) ratios at the single-antenna receivers. The aim is to minimize the total transmission power of the BS and the RS under both signal-to-interference-plus-noise ratio and EH constraints. First, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. This AO-based approach is then extended into a robust transceiver design against norm bounded errors in channel state information (CSI), by using semidefinite relaxation and the S-procedure. Second, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO-based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed, which consists of BS beamforming vectors, optimally scaled RS permutation matrix, and receiver PS ratios. For the given CSI, the optimal latent transceiver with the lowest total power consumption is selected for transmission. We propose concave-convex procedure-based and subgradient-type iterative algorithms, respectively, to design the latent transceivers under perfect and imperfect CSI. Simulation results are presented to validate the effectiveness of all the proposed algorithms.

This paper investigates the resource optimization problem for the multiple-input multiple-output (MIMO) cognitive radio (CR) networks. Different from the conventional works, a novel optimization metric, namely the bandwidth-power product (BPP), is used to achieve the optimal spectrum sharing. We apply the direct-channel singular value decomposition (DC-SVD) method to design the optimal source precoding matrix. Then the unified power and channel allocation problem is derived and found to be a mixed-integer programming problem. Hence, a sub-optimal and tractable algorithm with low complexity called the joint iterative power and channel optimization (JIPCO) algorithm is proposed. Furthermore, the discrete particle swarm optimization (DPSO) algorithm is introduced to approximately provide an upper bound. Numerical results show that the JIPCO algorithm achieves the acceptable performance with great reduction of computation complexity and validate the superiority of the proposed scheme compared to conventional power optimization scheme using the water-filling method.

Cooperative communications and full-duplex (FD) relaying have been proposed to meet the ever increasing data traffic demands and fully exploit the scarce spectrum resources. In this paper, we consider FD relaying networks with multiple source-destination (S-D) pairs and multiple FD relays. We propose the joint optimal relay assignment and power allocation (ORAPA) scheme to maximize the sum rate of the network. The sum rate maximization problem is formulated as a mixed-integer nonlinear programming (MENLP) problem. We provide an equivalent maximum weighted bipartite matching (MWBM) problem to solve the MINLP issue and reduce computational complexity by adopting the Hungarian algorithm. Simulations results are provided to verify the effectiveness of our proposed scheme.

Multiple-input-multiple-output (MIMO) Device-to-Device (D2D) communication underlaying cellular networks can improve user throughout and extend battery life of user equipment. This paper studies MIMO precoding and power allocation schemes for the D2D and cellular uplink communications to improve the energy efficiency of both the D2D and the cellular user (CU). Due to the co-channel interference caused by the shared resources, %between the D2D link and the cellular uplink, the precoder design and power allocation of both the D2D transmitter (DTX) and the CU become highly inter- dependent. A distributed cooperative iterative optimization algorithm (DCIOA) is introduced, where either the DTX or the CU cooperatively exchanges the interference information and iteratively updates its optimum precoder and power level. Meanwhile, a centralized stochastic search optimization algorithm using the particle swarm optimization (PSO) method is applied to provide an upper bound. Simulation results show that the performance of the DCIOA is close to that of the centralized algorithm with significant reduction of computation and slight increment of exchange overhead.

This paper aims to investigate the resource allocation problem in a relay-assisted OFDMA cognitive radio (CR) system. Different from conventional CR resource allocation problems, a joint bandwidth and power optimization framework using the bandwidth-power product metric is proposed. Besides, rate requirement of the secondary system is satisfied and interference power at the primary receiver is limited. Meanwhile channel pairing at the relay terminal is operated so that signal received over a particular channel at the first hop can be forwarded over a different channel at the second hop. The problem is found to be nonconvex and intractable to seek for an optimal solution. Hence, using the Lagrangian-Dual and Gauss-Newton methods, a sub-optimal algorithm with low complexity is proposed. Numerical results are provided to validate the superiority of the proposed method compared to conventional power optimization methods using the waterfilling scheme.

In this paper, we consider the power splitting technique for multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH) respectively. Specifically, assuming norm-bounded errors (NBE) in the channel state information (CSI), we study the robust joint beamforming and power splitting (JBPS) design problem, where the total transmission power is minimized subject to both signal-to-interference- plus-noise ratio (SINR) and EH constraints. We first propose an efficient approximation method based on semidefinite relaxation (SDR) for solving the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. Then, a low complexity algorithm is proposed using EH relaxation and cutting-set philosophy, which partitions the original problem into an alternating sequence of optimization and worst-case analysis subproblems with guaranteed convergence. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

This study investigates resource allocation problems for a point‐to‐point multi‐carrier multiple‐input multiple‐output cognitive radio network. Different from conventional resource optimisation problems, a joint power and bandwidth resource optimisation framework using the novel optimisation metric, namely bandwidth‐power product, is developed. Besides, rate requirement of the secondary system is satisfied, and the interferences introduced to the primary users (PUs) are below threshold of tolerance. The optimal source precoding matrix is designed and two methods, namely the project‐channel singular value decomposition (SVD) and direct‐channel SVD methods, are applied to satisfy interference power constraints for PUs. Then the unified power and channel allocation problem is derived and found to be a mixed‐integer programming problem. Hence, a sub‐optimal and tractable algorithm with low complexity is proposed. The innovative idea is to determine the channel resource budget by selecting the best channels, where the criterion for evaluating the quality of channel is detailed discussed. Then the power optimisation subproblem and channel allocation subproblem can be performed independently using the Lagrange‐duality theory and Gauss–Newton method, respectively. The simulation results show significant improvement in spectral efficiency by using this framework compared to classical power optimisation framework using the waterfilling scheme.

In this work, the authors propose two low‐complexity variable forgetting factor (VFF) mechanisms for recursive least squares‐based adaptive beamforming algorithms. The proposed algorithms are designed according to the linearly constrained minimum variance (LCMV) criterion and operate in the generalised sidelobe canceller structure. To obtain a better performance of convergence and tracking, the proposed VFF mechanisms adjust the forgetting factor by employing updated components related to the time‐averaged LCMV cost function. They carry out the analyses of the proposed algorithms in terms of the computational complexity and the convergence properties and derive an analytical expression of the steady‐state mean‐square‐error. Simulation results in non‐stationary environments are presented, showing that the adaptive beamforming algorithms with the proposed VFF mechanisms outperform the existing methods at a significantly reduced complexity.

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

In this work, we propose an adaptive set-membership (SM) reduced-rank filtering algorithm using the constrained constant modulus (CCM) criterion for beamforming. We develop a stochastic gradient (SG) type algorithm based on the concept of SM technique for adaptive implementation. The filter weights are updated only if the bounded constraint cannot be satisfied. In addition, we also propose a scheme of time-varying bound and incorporate parameter dependence to characterize the environment for improving the tracking performance of the proposed algorithm. Simulation results show that the proposed adaptive SM reduced-rank beamforming algorithm with dynamic bounds achieves superior performance to previously reported methods at a reduced update rate.

In this paper, an improved full rank condition (named as λ-FRC) is proposed for the construction of nonbinary LDPC codes. Compared with the conventional FRC, the λ-FRC introduces a metric λ to evaluate the reliability of the messages passing through the circle. Codes with better performance can be obtained by selecting elements of the parity matrix according to the metric. Based on the λ-FRC, a greedy search algorithm is proposed to design the codes efficiently. In order to further improve the performance of the codes constructed, an iterative search algorithm is applied in the construction. Simulation results show that the performance of codes constructed by the λ-FRC methods outperforms those by the FRC methods, and the iterative search algorithm improves the performance of the codes as the iteration number increases1.

In this study, we propose a robust transceiver scheme with switched preprocessing (SP) for K-user multiple-input multiple-output (MIMO) interference channels. The channel state information (CSI) available is assumed to be imperfect under norm-bounded errors (NBE). Each transmitter is provided with a codebook of permutation matrices, so that each arrangement of permutation matrices among the K transmitters will generate a group of K parallel transceivers. The optimum transceiver group within the class of all possible such groups is chosen by a suitable selection mechanism for data transmission. To design each transceiver group, we adopt a worst-case design approach to minimize the maximum per user MSE. We show that the proposed transceiver design problem can be partitioned into an alternating sequence of optimization and worst-case analysis subproblems, which involves solving Second-Order Cone Programming (SOCP) problems. Simulation results show that the performance of the proposed SP-based transceiver is significantly better than existing methods in the presence of imperfect CSI.1.

In this work, we propose a transceiver strategy with switched preprocessing (SP) for interference suppression in K-pair multiple-input multiple-output (MIMO) interference channels. Each transmitter is equipped with a codebook of permutation matrices. For the given MIMO interference channel, all the combinations of permutation matrices among the transmitters can create a number of parallel transceivers. Based on the given channel state information (CSI) and a block of transmit symbols, the optimum transceiver branch is chosen by a suitable selection criterion for transmission. For each branch, we introduce a robust transceiver design algorithm based on minimizing the mean square error (MSE) criterion. The selection criterion is designed to minimize the Euclidean distance between the true transmit symbol vector and the pre-estimated noiseless received vector. Simulation results show that the performance of the proposed technique is significantly better than prior art in the case of imperfect CSI.

In this study, a two-hop wireless sensor network with multiple relay nodes is considered where the amplify-and-forward (AF) scheme is employed. Two algorithms are presented to jointly consider interference suppression and power allocation (PA) based on the minimization of the symbol error rate (SER) criterion. A stochastic gradient (SG) algorithm is developed on the basis of the minimum-SER (MSER) criterion to jointly update the parameter vectors that allocate the power levels among the relay sensors subject to a total power constraint and the linear receiver. In addition, a conjugate gradient (CG) algorithm is developed on the basis of the SER criterion. A centralized algorithm is designed at the fusion center. Destination nodes transmit the quantized information of the PA vector to the relay nodes through a limited-feedback channel. The complexity and convergence analysis of the proposed algorithms are carried out. Simulation results show that the proposed two adaptive algorithms significantly outperform the other previously reported algorithms.

This paper proposes the design of robust transceivers with Tomlinson-Harashima precoding (THP) for multiple-input-multiple-output relay systems with amplify-and-forward protocols based on a multibranch (MB) strategy. The MB strategy employs successive interference cancellation on several parallel branches, which are equipped with different ordering patterns so that each branch produces transmit signals by exploiting a certain ordering pattern. For each parallel branch, the proposed robust nonlinear transceiver design consists of THP at the source along with a linear precoder at the relay and a linear minimum-mean-square-error receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimized to minimize the mean square error. We then employ a diagonalization method along with some attributes of matrix-monotone functions to convert the optimization problem with matrix variables into an optimization problem with scalar variables. We resort to an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme is capable of alleviating the effects of channel state information errors and improving the robustness of the system.

Vector Orthogonal Frequency Division Multiplexing (V-OFDM) for single transmit antenna systems is a generalization of OFDM where Single-Carrier Frequency Domain Equalization (SC-FDE) and OFDM are two special cases. It has been shown to be able to collect multipath diversity and thus generally outperforms OFDM. The performance of OFDM under phase noise has been extensively studied in literature. These effects can be summarized as introducing a Common Phase Error (CPE) and Inter Carrier Interference (ICI) resulting in poor performance. The performance of V-OFDM under phase noise still remains uninvestigated and in this paper it is shown that phase noise in V-OFDM systems leads to a Common Vector Block Phase Error (CVBPE) as well as an Inter Vector Block Carrier Interference (IVBCI) effect. Both of these effects are detrimental to the performance of a V-OFDM system. An exact expression for the Signal to Interference Noise Ratio (SINR) for a V-OFDM system is derived and found to agree closely with simulation results.

In-phase and Quadrature Imbalance (IQI) exists in both analog IQ modulator and demodulator. When carrier frequency is ultra-high and/or bandwidth is ultra-wide, the impact of IQI on system performance is not negligible. While in most of the literatures, the combined effect of channel, TX, and RX IQI is discussed, in this paper, a method that can separate and simultaneously estimate the three is proposed. The complexity of the proposed method is low, and with estimated TX and RX IQI, simple symbol-by-symbol detection can be used to detect data symbols with negligible performance loss. Compared with the calibration methods, where auxiliary circuits are needed to separate the TX and RX IQI belonging to different communication links inside a transceiver, by exploiting the fact that the wireless channel changes much faster than the IQI, the method proposed here can separate not only the TX and RX IQI but also the channel belonging to the same communication link without any auxiliary circuits. Simulations are carried out to verify the performance of the proposed method.

Using large number of antennas at base station (BS) to formulate a multi-user multiple-input multiple-output (MU-MIMO) system, i.e., the massive MIMO, has attracted more and more attention recently, due to the high spectrum and energy efficiency it has. When there is correlation and mutual coupling, Rusek et al. showed that the channel may lose asymptotic orthogonality as the number of antennas increases. In this paper, we investigate the dual-polarized (DP) antenna array with exponential correlation and coupling model and prove that under the assumption that the correlation and coupling decreases exponentially with the distance between the antennas, the channels are still asymptotically orthogonal and we still have the same massive MIMO effect with the single-polarized (SP) antenna array. Through simulations, the asymptotic expressions are verified and the benefits brought by DP antenna array in massive MIMO system are quantitatively shown for both uniform linear array (ULA) and uniform planar array (UPA) configurations.

In order to achieve reliable communication, transmit diversity is commonly used in multiple antenna systems. Currently, to obtain transmit diversity, space-time code is usually used. In this paper, a new multiplicative transformation scheme is proposed for multiple antenna systems with non-binary low-density parity-check (LDPC) codes. It is shown that this scheme can provide transmit diversity. In the scheme, for the first antenna, the modulated LDPC codeword is directly applied, while for the second antenna, before modulation, a multiplicative transformation is used, where the multiplication is defined in the same Galois field as that used to construct the nonbinary LDPC code. The performance of the proposed system is analyzed, and it is shown that, compared with phase sweeping transmit diversity (PSTD) and Alamouti's space-time block code (STBC), the proposed system not only provides transmit diversity, but also achieves good gain. Simulation results further prove the increased performance in both block fading and slow fading channels.

In this work, we study a joint iterative power allocation and interference suppression technique based on the minimization of the bit error rate (BER) cost function for cooperative direct-sequence code division multiple-access (DS-CDMA)systems that employ multiple relays and the amplifyand-forward (AF) strategy. We develop stochastic gradient (SG) algorithms based on the minimum-BER (MBER) criterion to jointly update the parameter vectors that allocate the power levels among the relays subject to a power constraint and the linear receiver. Simulation results show that the proposed adaptive algorithms significantly outperform the two other comparison schemes.

This paper investigates a robust Tomlinson-Harashima precoding (THP) design for multiple-input multiple-output (MIMO) relay systems based on a multi-branch (MB) strategy. The proposed scheme employs a parallel MB structure at the source according to different pre-stored ordering patterns. For each parallel branch, the robust nonlinear transceiver design consists of a TH precoder at the source along with a linear precoder at the relay and a linear minimum-mean-squared-error (MMSE) receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimised to minimise the MSE. We can finally use an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker (KKT) conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme outperforms existing transceiver designs with perfect and imperfect channel state information (CSI).

An improved construction scheme of efficient extended irregular repeat-accumulate (eIRA) codes with easy check matrix encoding is proposed in this paper. At the dual diagonal parts of the right check matrices of the codes, the distances of the nodes are enlarged in their corresponding Tanner graphs by increasing the distances between the adjacent “1” of the matrices. Thus, the probabilities of small loops in the Tanner graphs of the codes are reduced. Therefore, it decreases the error self-feedback and obtains good decoding performance. Moreover, with an extrinsic information transfer (EXIT) chart method, the degree profile of the code can be optimally designed to meet the requirement of optimal decoding performance. Simulation results show that our proposed coding scheme slightly outperforms the contrast original eIRA code about 0.05–0.1 dB at a bit-error-ratio (BER) of 10−5with the same binary phase shift keying (BPSK) system in an additive white Gaussian noise (AWGN) channel. In addition, it obtains lower encoding and decoding complexity since it employs the sparse check matrices for encoding and applies the easy addressing operations both for encoding and decoding. Therefore, the proposed scheme of constructing eIRA codes can be efficiently applied in wireless digital communications.

In this work, we propose a minimum mean squared error (MMSE) robust base station (BS) precoding strategy based on switched relaying (SR) processing and limited transmission of side information for interference suppression in the downlink of multiuser multiple-input multiple-output (MIMO) relay systems. The BS and the MIMO relay station (RS) are both equipped with a codebook of interleaving matrices. For a given channel state information (CSI) the selection function at the BS chooses the optimum interleaving matrix from the codebook based on two optimization criteria to design the robust precoder. Prior to the payload transmission the BS sends the index corresponding to the selected interleaving matrix to the RS, where the best interleaving matrix is selected to build the optimum relay processing matrix. The entries of the codebook are randomly generated unitary matrices. Simulation results show that the performance of the proposed techniques is significantly better than prior art in the case of imperfect CSI.

This paper addresses the problem of robust Tomlinson-Harashima precoding (THP) for multiple-input multiple-output (MIMO) relay systems. The robust nonlinear transceiver design consists of a TH precoder at the source along with a linear precoder at the relay and an minimum-mean-squared-error (MMSE) receiver at the destination. The imperfect channel state information (CSI) is considered. By taking the channel uncertainties into account, the source and relay precoders are jointly optimised to minimise the mean-squared-error (MSE).We finally use an iterative method to obtain the solution for relay and source precoders via Karush-Kuhn-Tucker (KKT) conditions. Simulation results demonstrate that the proposed scheme outperforms existing transceiver designs with perfect and imperfect CSI.

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for blind adaptive constrained constant modulus (CCM) recursive least square (RLS) algorithms applied to linear interference suppression in direct-sequence code division multiple access (DS-CDMA) systems. The proposed VFF mechanism employs an updated component relating to the time average of the constant modulus (CM) cost function to automatically adjust the forgetting factor in order to ensure good tracking of the interference and the channel. Analytical expressions for predicting the mean-squared error of the proposed adaptation technique are obtained. Simulation results show that the proposed VFF mechanism achieves superior performance to existing methods at a reduced complexity.

Turbo codes are near Shannon limit channel codes widely used in space communication systems and so on at low signal-to-noise rate (SNR). Timing recovery is one of the key technologies for these systems to work effectively. In this paper, an efficiently iterative timing recovery with Turbo decoding is presented. By maximizing the sum of the square of soft decision metrics from Turbo decoding, it can obtain accurate timing acquisition. And a computation-efficient approximate gradient descent method is adopted to obtain rough estimate of timing offset. Another merit of it is that, by the proposed method, a rate-1/6 Turbo coded binary phase shift keying (BPSK) system can even work at very low SNR (Es/N0) about -7.44 dB without any pilot symbol. Finally, the whole timing recovery scheme is accomplished where the proposed method is combined with Mueller-Muller (M&M) timing recovery which performs the timing track. Simulation results indicate that the Turbo coded BPSK system with rather large timing errors by the proposed scheme can achieve performance within 0.1 dB of the ideal code with reasonable computations and storages.

A novel frequency offset tracking algorithm for Space-Time Block Coded (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems is proposed in this work. Tracking of a frequency offset between the transmitter and the receiver is often aided by transmitting pilots embedded in the data payload. The proposed algorithm mainly exploits the specific construction of the OFDM symbol in STBC-OFDM systems, which does not need any additional pilots or sequences in the data field, providing high efficiency in spectrum. The estimator is derived on the basis of the maximum likelihood (ML) theory. Simulation results show that in a 2 × 2 multiple input multiple output (MIMO) system, under the assumption that the antennas are uncorrelated to each other, this method can provide a significant performance improvement in terms of the estimation accuracy of the frequency offset.

A statistical-based density evolution algorithm is proposed for nonbinary low-density parity-check (LDPC) codes. It is applicable to both regular and irregular codes under study. The algorithm proposed serves as a tool to analyze the performance limit for iterative decoding of nonbinary LDPC codes, which in turn guides code design. Specifically, it provides approximated evaluations of convergence thresholds for codes given specific degree distributions. It is shown that, for a class of quasi-cyclic (QC) structured extended irregular repeat-accumulate (SeIRA) nonbinary LDPC codes, degree distributions can be optimized via the algorithm proposed. Simulation results exhibit that the nonbinary LDPC codes designed outperform the optimized binary LDPC codes on the AWGN channel with similar code length and rate in terms of bits. Performance gap between the binary and nonbinary LDPC codes is larger in the scenario of higher order modulation.

In this paper, we investigate the performance of iterative decoding for concatenated Turbo coded and Space-time block coded (STBC) system. In our proposed system the soft parity bit needs to be estimated, which differs from traditional Turbo decoding, as the receiver adopts iterative decoding not only to Turbo codes but the entire system. In this case a simplified and effective method of parity bit estimation is presented, which we focus on, resulting in a moderate increase of complexity. Simulation results demonstrate that performance improvement can be obtained using the iterative scheme. Meanwhile the increase of complexity caused by iteration and parity bit estimation in exchange for performance improvement is considered. Our proposed scheme makes an optimal trade-off between complexity and performance for transmitting in Rayleigh fading channel.

This paper proposes a media access control (MAC) protocol with several efficient modules for cognitive point to multi-point (PMP) networks with rapid changes of spectrum opportunities. By modeling the status of each channel as a Markov process in the spectrum allocation module, we shall sense only a small set of possible channels while making efficient use of the spectrum and maintaining the frequency synchronization of all the cognitive users. And the admission control module, the rate control module and the resource allocation module guarantee the quality of service (QoS) requirements of different kinds of admitted connections on all the downlinks and uplinks by greedy algorithm and convex optimization. And our protocol also minimizes the total energy cost in the network. The simulation results show that our algorithm can make efficient use of the spectrum and the QoS requirements of all the connections are guaranteed.

Making efficient use of network resources is of great importance in wireless networks. In this paper, we propose a cross-layer algorithm to minimize the energy consumption and provide QoS guarantee jointly for various connections with different QoS requirements in wireless cellular networks. We formulate an admission control problem and a multiple objective programming problem which allocates the rate and network resource for each connection in the network simultaneously. We derive efficient solutions to the two problems which attain an efficient usage of the transmission powers, and guarantee the rate and delay requirements of every admitted connection in the network. The simulation results show that our algorithm makes use of the network resource more effectively than layered approaches and our algorithm can be implemented in various wireless cellular networks with fast convergence.

The technology of space-time coded continuous phase modulation (STC-CPM) has aroused considerable attention recently in wireless communication systems for improving the capacity and data rate without bandwidth expansion. Symbol timing synchronization is an important issue in such systems. In this paper, a data aided symbol timing estimation algorithm was proposed for burst-mode STC-CPM systems over Rayleigh fading channels. A training sequence is embedded before transmission of each packet. The receiver first adds the signals from different receive antennas and then calculates the digital Fourier transformation (DFT) of the phase of the one-symbol differential signal of the sum. The initial symbol timing offset can be estimated from the phase of the tone component of the DFT outputs. MATLAB simulation results show that the variance of the timing estimation error is very small in slow Rayleigh flat fading channels and frequency selective fading channels and the degradation of frame error rate (FER) is significantly small under the condition of non-ideal synchronization. This algorithm is suitable for any numbers of transmit and receive antennas and can estimate the timing offset rapidly and accurately in STC-CPM systems.

The paper considers a blind (non-data aided) and open-loop algorithm to estimate frequency offset of a linearly modulated waveform transmitted through fading channels. This method is based on second-order cyclostationarity, and can be used not only in time-selective channels, but also in frequency-selective fading channels. It does not rely on the Gaussian assumption of the complex equivalent low-pass channel process, and does not require the knowledge of the channel. Performance analysis of the proposed algorithm using Monte Carlo simulations in (1) additive white Gaussian noise (AWGN) channel, (2) flat fading channel, and (3) frequency selective fading channel are also reported.

A noncoherent GMSK receiver suitable for software radio is proposed. It is an I/Q structured receiver with a joint symbol timing error and frequency offset estimator based on discrimination and FFT calculation through a specified preamble. A matched filter suitable for this type of noncoherent GMSK receiver is investigated and differential detection is used to recovery the original data. Finally, bit error rate (BER) performances are offered via computer simulation. Compared with the optimal coherent receiver, the proposed receiver is significant due to its smaller implementation complexity. It also has a better performance and is more suitable to implement on common software radio platforms compared with typical noncoherent receivers.