The deployment of dual-functional communication and sensing base stations (BSs) in cellular networks will transform these networks into extensive sensing systems, enabling various emerging applications such as autonomous driving and smart cities. However, to fully realize these benefits, optimizing transmission and managing interference among different BSs is crucial. In this paper, we consider a cell-free integrated sensing and communication (ISAC) system, where multiple BSs, each equipped with multiple antennas, collaboratively communicate with multiple single-antenna users while jointly estimating the target’s location through signals reflected by the target and received at all BSs. In this context, fully utilizing all links necessitates coordinated transmit beamforming design among BSs to effectively balance communication and sensing performance. To address this challenge, we first characterize the sensing performance by deriving the Cramér-Rao lower bound (CRLB) for target location estimation. We then formulate an optimization problem to design the transmit beamforming vectors at each BS, minimizing the sensing CRLB while meeting communication quality of service constraint for each user. Due to the highly non-convex nature of this problem, we apply a series of transformations to convert it into a more tractable form and develop an iterative algorithm to solve it. Numerical results validate the effectiveness of the proposed design, highlighting its advantages in balancing the trade-off between sensing and communication compared to three benchmark designs.

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.

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.

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.

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.

The concept of extremely large-scale reconfigurable intelligent surfaces (XL-RIS) holds great promise for enabling sixth-generation (6G) communications. However, the vast number of passive reflection coefficients and the transition from far-field to near-field electromagnetic radiation pose significant challenges for channel estimation, especially under tight pilot overhead constraints. To address these challenges, we propose a novel hybrid integrated sensing and communication architecture and a three-stage channel estimation scheme for XL-RIS-assisted millimeter wave communication systems. The proposed scheme leverages user position data, obtained through a sensing module, to accurately estimate near-field cascaded channels. First, we design an integrated base station architecture that combines a fully-digital sensing module with a hybrid communication module to achieve high-resolution distance and angle estimations using linear frequency modulation signals. Next, we introduce a distance-error-minimization based localization algorithm to effectively estimate user coordinates. To balance channel estimation performance and pilot overhead, we carefully select the appropriate number of position update iterations. Using these estimated coordinates, we calculate the channel fading coefficients for the near-field cascaded channels, facilitating accurate channel estimation. Simulation results validate the effectiveness of our proposed scheme, demonstrating reduced overhead while maintaining superior channel estimation performance.

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.

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.

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a backhaul-limited multiple access channel (MAC) for cooperative localization of the target. Different from schemes on only information domain or signal domain cooperation, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the backhaul resource allocation, together with a greedy strategy for node selection. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to outperform the baseline schemes significantly.

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.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is able to perform reflection and refraction of the incident signals simultaneously, making it a promising technology for omnidirectional localization. In this work, we study a STAR-RIS based millimeter-wave (mmWave) localization system in the near field. In particular, we exploit the multipath components (MPCs) as signals emitted from a virtual STAR-RIS and propose a multi-stage localization algorithm. Specifically, in the initialization stage, we present a practical two-step localization method to obtain coarse estimates of UE positions, based on the second-order Fresnel approximation of the near-field channels. In the optimization stage, to effectively leverage the MPCs for localization performance improvement, we propose to add signal weights to the received signals. Then, the signal weights, STAR-RIS energy splitting (ES) coefficients and phase shifts are jointly optimized to minimize the Cramér-Rao lower bound (CRLB). Finally, in the refinement stage, the localization accuracy is further improved based on the information obtained during the first two stages. Simulation results demonstrate the effectiveness of the proposed multipath assisted localization algorithm and show that STAR-RIS surpasses conventional RIS in the omnidirectional localization scenario.

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.

Traditional millimeter-wave beam selection or prediction algorithms typically rely on beam scanning measurements at the transceivers, incurring substantial training overhead and exhibiting limited adaptability in diverse environments. Recent efforts have aimed to mitigate these challenges by incorporating sensing information, thereby reducing or eliminating the need for extensive beam training. However, existing works predominantly concentrate on exploiting a single sensing modality and often overlook the potential benefits of utilizing historical sensing information. In this paper, we introduce an intelligent beam prediction framework that leverages a deep integration of multimodal sensing data, encompassing GPS, camera, radar, and LiDAR data. The design proposed involves the application of customized deep neural networks to extract features from camera, radar, and LiDAR data. These extracted features, combined with user position and selected beam index, are concatenated to form an aggregated feature vector at each time instance. Subsequently, a time series of these concatenated feature vectors is utilized to exploit temporal correlation for beam prediction through a dedicated long short-term memory network module. Numerical simulations confirm the effectiveness of the proposed design and its superiority over several considered state-of-the-art baselines.

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.

Optimizing beamforming is crucial in mitigating pronounced propagation loss and ensuring reliable communication at millimeter-wave (mmWave) frequencies. Traditional beam optimization methods rely on either precise channel estimation or extensive beam training in the mm Wave band, both of which entail substantial pilot overhead. To alleviate this overhead, we leverage the spatial congruence between sub-6 GHz (sub-6G) and mm Wave channels and propose a sub-6G information and few pilots aided beam prediction network (SPBPNet) through deep learning. Specifically, the proposed SPBPNet comprises two cascaded modules: i) the angular information extraction module, which extracts angular features from the available sub-6G channel estimate and maps them to a minimal set of narrow beam directions to be measured in the mm Wave band; and ii) the beam prediction module, which takes limited beam training along the selected directions and then fuses measurements in the mm Wave band with the sub-6G channel information to generate mmWave beam predictions. Numerical results demonstrate that SPBPNet efficiently maps sub-6G channel estimates to mmWave beams and achieves a superior balance between performance and pilot overhead compared to state-of-the-art benchmarks. Moreover, SPBPNet exhibits resilience to varying sub-6G channel estimates at different signal-to-noise ratio levels.

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.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

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.

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.

In this paper, we consider a cooperative radar sensing system, where a radar transmitter sends a sensing signal to track a target, several radar receivers receive the echo signal from the target and send local processing results to a fusion center (FC) for cooperative localization. Different from existing researches on information domain or signal domain cooperation, we propose a novel hybrid information-signal domain cooperative radar sensing (HISCRS) framework. In the proposed HISCRS framework, the FC not only collects the extracted location information such as the time delays from the radar receivers, but also obtains a fused version of the echo signals from different radar receivers via a deep over-the-air computation (AirComp) scheme, which can fully exploit the correlations among the echo signals to significantly reduce the communication cost. Specifically, information domain features, i.e., the time delays, are used to obtain a coarse location of the target and signal domain features, i.e., the fused echo signals, are used to generate a location offset to further improve the localization accuracy. Simulations results show that the proposed HISCRS framework is able to achieve a notable gain over the existing cooperative sensing schemes with reduced communication cost.

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.

Federated learning (FL) is an edge learning framework that has received significant attention recently. However, the cost of communication has become a major challenge for FL as the number of edge devices grows and the complexity of training models increases. Besides, data samples across all edge devices are usually not independent and identically distributed (non-IID), posing additional challenges to the convergence and model accuracy of FL. Therefore, we propose a novel personalized FL framework based on deep coded over-the-air computation, named DipFL. In this framework, we design a deep AirComp aggregation (DACA) module for n-to-1 information aggregation. Besides, a joint source-channel coding (JSCC) module is designed based on the variational auto-encoder (VAE) model, which not only encodes the transmitted data, but also reduces the bias of local samples by introducing certain regularisation terms. In addition, we propose a personalized mix module that allows local models to be more personalized by mixing the global model and the local models. Simulation results confirm that the proposed DipFL framework is able to significantly reduce the amount of transmitted data, while improving FL performance especially at low signal-to-noise regimes.

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 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.

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.

This paper investigates a new concept of employing intelligent reflecting surface (IRS) to enhance the monitoring performance of wireless information surveillance system, where a full-duplex legitimate monitor is employed to eavesdrop the suspicious transmission from a transmitter to a receiver with the help of an IRS. Under this setup, we consider three IRS deployment strategies, where the IRS is placed near the suspicious transmitter, the suspicious receiver and the legitimate monitor, respectively. First, the monitoring rate achievable by the IRS-aided surveillance system under each deployment strategy is analyzed, which reveals that deploying the IRS near the suspicions transmitter achieves the maximum rate with an asymptotically large number of IRS reflecting elements. Next, efficient algorithms are proposed to maximize the monitoring rate by jointly optimizing the receive and jamming beamforming vectors at the legitimate monitor and the reflection coefficients at the IRS. In particular, a performance upper bound is obtained via properly characterizing the upper and lower bounds of the jamming signal power and using semidefinite relaxation (SDR), while low-complexity algorithms based on the penalty dual decomposition (PDD) framework are also presented to achieve near-optimal performance. Finally, numerical results are presented to validate our analysis as well as the effectiveness of the proposed algorithms, and useful insights are drawn.

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopttwo-timescaletransmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing theshort-termtransmit beamforming vectors at the BS (including information and energy beams) as well as thelong-termphase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is non-convex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated.

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.

This paper investigates the passive beamforming and deployment design for an intelligent reflecting surface (IRS) aided full-duplex (FD) wireless system, where an FD access point (AP) communicates with an uplink (UL) user and a downlink (DL) user simultaneously over the same time-frequency dimension with the help of IRS. Under this setup, we consider three deployment cases: 1) two distributed IRSs placed near the UL user and DL user, respectively; 2) one centralized IRS placed near the DL user; 3) one centralized IRS placed near the UL user. In each case, we aim to minimize the weighted sum transmit power consumption of the AP and UL user by jointly optimizing their transmit power and the passive reflection coefficients at the IRS (or IRSs), subject to the UL and DL users’ rate constraints and the uni-modulus constraints on the IRS reflection coefficients. First, we analyze the minimum transmit power required in the IRS-aided FD system under each deployment scheme, and compare it with that of the corresponding half-duplex (HD) system. We show that the FD system outperforms its HD counterpart for all IRS deployment schemes, while the distributed deployment further outperforms the other two centralized deployment schemes. Next, we transform the challenging power minimization problem into an equivalent but more tractable form and propose an efficient algorithm to solve it based on the block coordinate descent (BCD) method. Finally, numerical results are presented to validate our analysis as well as the efficacy of the proposed passive beamforming design.

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.

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.

An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve). To guarantee the security, the UAV employs a power splitting approach, where its transmit power can be divided into two parts for transmitting confidential messages and artificial noise (AN), respectively. We aim to maximize the average secrecy rate by jointly optimizing the UAV's trajectory, the transmit power levels and the corresponding power splitting ratios allocated to different time slots during the whole flight time, subject to both the maximum UAV speed constraint, the total mobility energy constraint, the total transmit power constraint, and other related constraints. To efficiently tackle this non-convex optimization problem, we propose an iterative algorithm by blending the benefits of the block coordinate descent (BCD) method, the concave-convex procedure (CCCP) and the alternating direction method of multipliers (ADMM). Specially, we show that the proposed algorithm exhibits very low computational complexity and each of its updating steps can be formulated in a nearly closed form. Besides, it can be easily extended to the case of three-dimensional (3D) trajectory design. Our simulation results validate the efficiency of the proposed algorithm.

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.

Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral/energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming (i.e., IRS phase shifts) is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming (i.e., transmit precoding vectors at the access point (AP)) is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the AP, subject to the users' individual quality of service (QoS) constraints on the achievable long-term average rate. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term passive beamforming problem and a family of short-term active beamforming problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. We show that both the long-term and short-term problems can be efficiently solved and the proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark 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.

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.

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.

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 work, the authors propose a transceiver design strategy based on switched preprocessing (SP) for interference management in K ‐pair MIMO interference channels. Each transmitter performs SP by using a small number of permutation matrices to allocate the entries of its precoder output vector on different transmit antennas. Each arrangement of permutation matrices among the K transmitters gives rise to a set of K parallel point‐to‐point transceivers, referred to as MIMO latent transceiver set (MLTS). Based on the given channel state information (CSI), the optimum MLTS among the available ones is chosen by minimising the squared Euclidean distance between the pre‐estimated noiseless received vector and the true transmit symbol vector. In addition, they consider two CSI error models, i.e. the stochastic error model and the norm‐bounded error model, and for each type they propose robust algorithms for the design of the MLTS associated to the different choices of permutation matrices, which are based on minimising various types of mean square error criteria. A detailed study of computational complexity for the proposed SP‐based MIMO transceiver design algorithms is carried out. Simulation results verify the effectiveness of the new SP‐based designs for MIMO interference channels.

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.

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.

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.

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.

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.

In this paper we consider nonconvex optimization and learning over a network of distributed nodes. We develop a Proximal Primal-Dual Algorithm (Prox-PDA), which enables the network nodes to distributedly and collectively compute the set of first-order stationary solutions in a global sublinear manner [with a rate of $O(1/r)$, where $r$ is the iteration counter]. To the best of our knowledge, this is the first algorithm that enables distributed nonconvex optimization with global rate guarantees. Our numerical experiments also demonstrate the effectiveness of the proposed algorithm.

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.

In this paper, we propose joint transceiver design algorithms for the full-duplex K -pair multiple-input multiple-output interference channel with simultaneous wireless information and power transfer. To mitigate and exploit the complex interference, we consider two important utility optimization problems, i.e., the sum power minimization problem and the sum-rate maximization problem. In the first problem, our aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization (AO) and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, where the concave-convex procedure (CCCP), the minimum mean-square error (MMSE) criterion, and the semidefinite relaxation technique are, respectively, employed to compute the vectors of power splitting ratios, the receiving matrices, and the transmitting beamforming vectors. Two simplified algorithms based on fixed beamformers, namely, the maximum ratio transmission and the maximum signal-to-interference-leakage beamformers are also proposed. In the second problem, our aim is to maximize the sum-rate under additional power and EH constraints. Due to the highly non-convex nature of this problem, we first reformulate it into an equivalent-weighted MMSE problem by introducing suitable weight factors, such that the global optima of the two problems are identical. Then, by utilizing the concept of AO and CCCP, we show that the equivalent problem can be efficiently solved. Again, with the aid of the fixed beamformers, two simplified algorithms are provided to reduce the computational complexity. Simulation results are presented to validate the effectiveness of the proposed algorithms.

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.

In this paper, we propose a joint transceiver design algorithm for the full-duplex (FD) K-pair multiple- input multiple-output (MIMO) interference channel with simultaneous wireless information and power transfer (SWIPT). The aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, aimed at successively optimizing: 1) the power splitting (PS) vectors of the EH nodes; 2) the receive beamforming vectors; 3) the transmit beamforming vectors.The first step is carried out based on concave-convex procedure (CCCP), the second step is based on the minimum mean square error (MMSE) criterion and the third step resorts to using semidefinite relaxation (SDR). Simulation results are presented to validate the effectiveness of the proposed algorithm.

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, 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.

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.

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.