Accurate beam prediction is essential for maintaining reliable links and high spectral efficiency in dynamic low-altitude wireless networks. However, existing approaches often fail to capture the deep correlations across heterogeneous sensing modalities, limiting their adaptability in complex three-dimensional environments. To overcome these challenges, we propose a multi-modal predictive beamforming method based on a cross-attention fusion mechanism that jointly leverages visual and structured sensor data. The proposed model utilizes a Convolutional Neural Network (CNN) to learn multi-scale spatial feature hierarchies from visual images and a Transformer encoder to capture cross-dimensional dependencies within sensor data. Then, a cross-attention fusion module is introduced to integrate complementary information between the two modalities, generating a unified and discriminative representation for accurate beam prediction. Through experimental evaluations conducted on a real-world dataset, our method reaches 79.7% Top-1 accuracy and 99.3% Top-3 accuracy, surpassing the 3D ResNet-Transformer baseline by 4.4%-23.2% across Top-1 to Top-5 metrics. These results verify that multi-modal cross-attention fusion is effective for intelligent beam selection in dynamic low-altitude wireless networks.

Movable antenna (MA) has been recognized as a promising technology to enhance communication network performance by adjusting the antenna position within a confined region. In this paper, we consider an energy-efficient MA-enabled multiple-input multiple-output (MIMO) network with the hybrid analog-digital transceiver, where energy consumption induced by the MA movement is additionally considered to accurately evaluate the system energy efficiency (EE) performance. Under both fully-connected and partially-connected transceiver structures, we aim to maximize the system EE by jointly optimizing the hybrid beamformers and antenna positions, subject to the unit-modulus constraints and the minimum MA distance constraints. To tackle these two highly non-convex problems effectively, we propose an efficient two-layer successive convex approximation (SCA) based iterative algorithm, where we aim to iteratively update the achievable EE in the outer layer and alternately optimize the hybrid beamformers and antenna positions in the inner layer. Furthermore, considering the asymptotically low-SNR and high-SNR regimes, we respectively develop two low-complexity algorithms by leveraging the structural properties of their corresponding optimal fully-digital beamformers. Simulation results validate the superior EE performance and low-complexity advantage of our proposed algorithms over the existing benchmark schemes.

The evolution of communication systems shows a trend toward multifunctional integration, thus the joint design of training sequence and precoder for multifunctional purposes is of great significance. In this paper, we investigate the joint optimization of training sequence and precoder matrices for dual-functional multiple-input multiple-output (MIMO) systems under per-antenna power constraints, which considers the performance metrics of channel estimation, data transmission and target estimation simultaneously. A general fusion framework under per-antenna power constraints is established, where multiple linear constraints are transformed into a single weighted-sum constraint, and a modified subgradient algorithm is proposed to address it. Then, based on the fusion of positive semi-definite matrix-valued signal-to-noise ratios (SNRs), training sequence is optimized to strike a trade-off between channel estimation accuracy and sensing performance, and the optimal pilot based on the fusion structure is derived. The proposed algorithm solves mean square error minimization and mutual entropy maximization problems, achieving a balance between system performance and algorithm complexity. Based on the optimized training sequence, channel estimation error model is derived, and the corresponding precoder matrix is designed, which takes into account the performance of both data transmission and target estimation. Finally, numerical results are provided for demonstrating the performance of the proposed algorithms.

Integrated sensing and communication (ISAC) emerges as an advanced technology to improve the spectrum efficiency by sharing the same spectrum for both communication and sensing. However, the open nature and the shared spectrum make the privacy a critical issue. Fortunately, covert communication can tackle this issue and provide an additional privacy protection for ISAC. In this paper, we propose a novel multi-user covert ISAC scheme against collusive wardens. Specifically, a dual-functional transmitter senses the wardens while communicating with multiple legitimate users covertly, where the more practical Rician fading is considered. First, we analyze the global detection performance of collusive wardens, where we employ the moment matching to handle the intractable theoretical analysis and computation introduced by Rician fading. Then, we optimize each warden’s detection threshold to achieve the greatest detection, creating the worst scenario for legitimate communication. Under this threat, we maximize the average covert transmission rate through jointly optimizing the power allocation and beamforming. To solve this non-convex optimization problem, semidefinite relaxation and successive convex approximation are adopted to transform it into a convex problem, and a convergence-guaranteed iteration algorithm is developed to obtain the optimal solutions. Simulation results show the superiority of the proposed multi-user covert ISAC scheme while revealing the inherent trade-off among covertness, sensing, and communication.

In contrast to conventional fixed-position antennas (FPAs), movable antennas (MAs) are capable of actively exploiting the spatial channel variations to enhance the performance of wireless systems. In this paper, we investigate a movable antenna (MA) aided integrated sensing and covert communication (ISACC) system, where the MA movable regions are quantized into practical discrete positions. We aim to maximize the covert sum rate by jointly optimizing the BS transmit beamformers, the positions of both BS- and user-side MAs, and the radar receive equalizer, subject to constraints on radar echo signal-to-clutter-plus-noise ratio (SCNR) and covertness. To effectively tackle this problem, an efficient successive convex approximation (SCA) based alternating optimization (AO) algorithm is proposed, where the complicated log-fractional objective function is handled by fractional programming (FP) technique, and the discrete MA position variables are optimized by employing the penalty strategy. To obtain useful insights, we then focus on a simple single-user single-target (SUST) scenario, and demonstrate that the optimal Tx MA positions aim to de-correlate the BS-target and BS-Willie channels, whereas the optimal Tx MA positions can be flexibly chosen. Furthermore, we extend our work into the practical imperfect CSI scenario, in which a conservative approximation of the covertness constraint is derived, based on which the proposed AO algorithm is still applicable after some slight modifications. Numerical results demonstrate the superior performance of our proposed algorithms under both perfect CSI and imperfect CSI.

In this paper, the joint active terminal identification (ATI) and channel estimation (CE) problem is investigated for the asynchronous grant-free random access system in the context of low-earth orbit (LEO) satellite Internet of Things (IoT) network. An asynchronous grant-free model is established to characterize the implications of delay and Doppler caused by LEO satellites. Concurrently, a generalized approximate message passing-aided structured joint detection (SJD) scheme with Rician parameters learning (RPL) is proposed for joint ATI and CE. The prior mean and variance of the Rician channel are treated as hyperparameters and updated via the expectation-maximization algorithm. Furthermore, to alleviate the modeling mismatch, we develop an off-grid model following Taylor expansion, accompanied by a mismatch error parameter learning (MPL) framework to boost the accuracy of joint detection. Simulation results demonstrate that the proposed RPL-SJD scheme outperforms existing approaches in terms of normalized mean square error with 4dB and activity detection error rate with 7dB.

Unmanned aerial vehicles (UAVs) assisted communications have become one of the crucial approaches to enable the reliable and flexible data transmissions, particularly in ultra-reliable and low-latency scenarios, such as remote sensing, emergency response, and military long-range command transmission. In this paper, we investigate the secrecy performance of UAV-assisted short-packet transmission via non-orthogonal multiple access (NOMA), where a UAV serves as an aerial relay to forward mission-critical information from a base station to two remote users in the presence of a ground-based eavesdropper. Both the base station and UAV relay use beamforming for generating the artificial noise to disrupt the eavesdropping and enhance the security, and the UAV operates in half-duplex mode to meet resource constraints and avoid self-interference. The weighted effective secrecy rates of the two users are maximized by jointly optimizing the blocklength, transmission rate, power allocation coefficients, power-sharing factors and UAV position, which is shown to be non-convex and difficult to be solved directly. Accordingly, we decompose the problem into four sub-problems by applying the block coordinate descent (BCD) algorithm to maximize the weighted effective secrecy rate. Then, slack variables are introduced to further solve the sub-problems via successive convex approximation (SCA). Finally, simulation results are presented to demonstrate the effectiveness of the proposed scheme.

Cooperative cognitive radio (CR) networks can enable secondary users (SUs) to access the spectrum without disrupting the transmission of primary users (PUs), which brings a series of security challenges despite the significant increase in spectrum efficiency. In this paper, we propose a novel unmanned aerial vehicle (UAV) assisted covert transmission scheme for cooperative CR networks, where a UAV as the secondary transmitter can send its covert signal to a secondary receiver while ensuring the quality of service for the PU. To achieve the covert transmission of SU, the PU’s signal is used as a beneficial interference to disturb the detection of wardens. We first derive the minimum detection error probability and Kullback-Leibler divergence under the finite blocklength constraint. Then, the average effective throughput maximization problem under the probabilistic line-of-sight channel is established by jointly optimizing the UAV’s transmit power and trajectory. Finally, numerical results verifies that the UAV relay in the proposed scheme can not only assist in the information transmission of PU but also achieve the covert communication for the secondary network in the presence of multiple wardens.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for greatly enhancing communication performance of future wireless networks. To overcome the multiplicative fading effect of passive RIS and high energy consumption of active RIS, we propose a novel dynamic subarray-based hybrid active-passive RIS (HRIS) architecture by dividing all reflecting elements into multiple sub-RISs, each of which can flexibly switch between active and passive modes. Therefore, the proposed subarray-based HRIS is anticipated to achieve optimal system performance with minimal cost and energy consumption. In this paper, we aim to maximize the energy efficiency (EE) for the subarray-based HRIS assisted multi-user multiple-input single-output (MISO) system, where the transmit beamforming vectors at the base station (BS), the mode switching matrix, and the reflection matrices of active and passive sub-RISs are jointly optimized subject to individual user rate constraints. To tackle this intractable problem, we firstly explore the feasible region of the minimum rate threshold among all users, and then develop an efficient two-layer successive convex approximation (SCA) based iterative algorithm. Considering a simplified single-user scenario, we also derive some interesting insights into the optimal active-passive sub-RISs allocation for maximizing EE. It is revealed that for a small BS transmit power, deploying more active sub-RISs in the subarray-based HRIS is preferred to attain the maximum EE. Conversely, under a high BS transmit power and a small HRIS reflection power, more sub-RISs should be switched to the passive mode. Numerical simulation results verify the superior EE performance of the proposed dynamic subarray-based HRIS over the traditional active and passive RISs.

In this paper, we propose a general energy efficiency (EE) optimization framework for the hybrid analog-digital transceivers design in multi-hop communication systems. The analog and digital beamforming matrices are jointly optimized considering two kinds of practical power constraint models, i.e., sum power with box eigenvalue constraints (SPBECs) and multiple weighted power constraints (MWPCs), and unit-modulus constraints on analog beamforming matrices. For both the SPBECs and MWPCs cases, to tackle the challenging problem involving highly-coupled variables, an effective decoupling approach is first proposed. Specifically, a set of auxiliary variables are introduced to equivalently transform the original problem into a decoupled form with respect to the variables of each node. Then, for each node, we propose an efficient two-stage analog and digital beamforming optimization algorithm. To be specific, we optimize the analog beamforming matrices in the first stage by jointly exploiting the matrix-monotonic optimization framework and channel-alignment strategy. Then, we optimize the digital beamforming matrices in the second stage based on the multi-node water-filling methodology. Furthermore, in order to compute the parameters involved in the multi-node water-filling solutions for the SPBECs case, we propose two novel strategies, i.e., the Dinkelbach based strategy and the per-node penalty based strategy, which derive the parameters in closed-forms and offer clear physical interpretations. Moreover, the per-node penalty based strategy is effectively extended to the MWPCs case by additionally employing the Lagrangian duality theory. Simulation results demonstrate the superior performance and high efficiency of our proposed algorithms.

Movable antenna (MA) has emerged as a promising technology for enhancing communication performance over conventional fixed position antenna (FPA) by exploiting spatial channel variations. In this paper, we propose a general energy efficiency (EE) optimization framework for the MA-aided multi-user multiple-input multiple-output (MU-MIMO) downlink communications. We jointly optimize the precoding matrices and the positions of transmit and receive MAs considering two different types of power constraint models, i.e., the sum power constraint (SPC) and multiple weighted power constraints (MWPCs), and various physical constraints on MA positions. In both the SPC case and the MWPCs case, we optimize the MA positions by jointly employing the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA) methodologies. As for the precoding matrices optimization, by exploiting the uplink-downlink duality of MU-MIMO systems, we transform the downlink EE optimization into their virtual uplink EE optimization counterparts. Then, we derive the optimal structures of the precoding matrices, where the involved optimal power allocations take the multi-user water-filling solutions. To compute the parameters of the multi-user water-filling solutions, by taking advantage of the underlying algebraic monotonicity of the problem, we propose three novel design strategies, i.e., the direct Dinkelbach based design, the modified Dinkelbach based design, and the bound-ware penalty based design. In contrast to conventional fractional programming (FP) based EE optimization methods, the proposed algorithms offer significantly lower computational complexities and explicit physical insights. Moreover, the simulation results demonstrate the superior performance and high efficiency of our proposed EE optimization algorithms.

The combination of reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) can improve the resource utilization in non-line-of-sight scenarios. To sense the target with high accuracy, it is necessary to directionally reflect the sensing signal toward the sensing target via the RIS, improving the sensing performance. However, enhancing signal quality may increase the risk of information leakage when the sensing target is the warden. Against this background, we investigate a covert transmission problem in an RIS assisted ISAC system. Specifically, we obtain a tractable form of covertness constraint in terms of minimum detection error probability via the optimal detection threshold. Then, we maximize the sum covert transmission rate by jointly optimizing the beamforming of confidential signal and jamming signal as well as the RIS’s phase shift, while ensuring the reliability, covertness and sensing constraints. Owing to the effectiveness of the deep reinforcement learning algorithm in processing high-dimensional data and making intelligent decision, we propose a twins-deep deterministic policy gradient-based joint covert beamforming and the phase shift of RIS optimization (TD3-CBP) algorithm to solve the above non-convex problem. Finally, simulation results demonstrate the effectiveness of the proposed TD3-CBP algorithm in the covertness performance, achieving an average of 16.1 % higher sum covert transmission rate than the benchmark algorithms.

Integrated sensing and communication (ISAC) is expected to enable the fifth-generation (5G) networks to provide ubiquitous communication and sensing. However, some high-dynamic scenarios hinder applications of conventional ISAC schemes owing to the high overhead and poor real-time performance. In this paper, we design a novel ISAC architecture and propose a large language model (LLM) based two-stage beamforming prediction scheme. Specifically, in the first stage, we develop an LLM-based approach to predict the future channel state information (CSI) according to the history echoes. Via the data preprocessing and supervised fine-tuning, the LLM can achieve effective channel prediction task with unstructured data. In the second stage, according to the predicted/estimated CSI, we formulate a beamforming optimization problem to maximize the achievable sum rate while satisfying the quality of service (QoS). Then, we propose a Primary-dual network with the unsupervised adversarial learning to handle it, facilitating the on-line beamforming. Simulation results verify that, compared with the benchmarks, our proposed beamforming prediction scheme not only enjoys a higher channel prediction accuracy but also achieves a better balance between the performance and computational complexity.

In this paper, we studyrobustbeamforming design fornear-fieldphysical-layer-security (PLS) systems, where a base station (BS) equipped with an extremely large-scale array (XL-array) serves multiple near-field legitimate users (Bobs) in the presence of multiple near-field eavesdroppers (Eves). Unlike existing works that mostly assume perfect channel state information (CSI) or location information of Eves, we consider a more practical and challenging scenario in this paper, where the locations of Bobs are perfectly known, while onlyimperfect location informationof Eves is available at the BS. We first formulate a robust optimization problem to maximize the sum-rate of Bobs while guaranteeing a worst-case limit on the eavesdropping rate under location uncertainty. By transforming Cartesian position errors into the polar domain, we reveal an important near-fieldangular-error amplification effect, i.e., under the same location error, the closer the Eve, the larger the angle error, which severely degrades the performance of conventional robust beamforming methods based on imperfect channel state information. To address this issue, we first establish the conditions for which the first-order Taylor approximation of the near-field channel steering vector under location uncertainty is largely accurate. Then, we propose atwo-stagerobust beamforming method, which first partitions the uncertainty region into multiple fan-shaped sub-regions, followed by the second stage to formulate and solve a refined linear-matrix-inequality (LMI)-based robust beamforming optimization problem. In addition, the proposed method is further extended to scenarios with multiple Bobs and multiple Eves. Finally, numerical results validate that the proposed method achieves a superior trade-off between rate performance and secrecy robustness, hence significantly outperforming existing benchmarks under Eve location uncertainty.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. However, it also introduces a potential security threat due to the sensing behavior. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station continuously sense an aerial target while communicating with a ground receiver. First, we derive a closed-form expression of each warden's detection outage probability to obtain the global detection outage probability. Then, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate under the worst case that all wardens can collusively adjust their detection thresholds to achieve the best detection. To tackle this non-convex optimization problem, an iteration scheme is proposed. Numerical results demonstrate the validity of the proposed covert ISAC scheme.

In this paper, we design a novel unmanned aerial vehicle (UAV) aided covert cooperative cognitive radio (CR) scheme, where a UAV as the secondary transmitter can send its own covert signal to a secondary receiver while guaranteeing the quality of service for the primary user (PU). To accomplish the covert transmission of the secondary user, the PU’s signal is applied as a friendly interference to interrupt the detection of wardens. We first calculate the minimum detection error probability and Kullback-Leibler divergence under the finite blocklength constraint. Then, the average effective throughput maximization problem under the probabilistic line-of-sight channel is constructed by jointly optimizing the UAV’s transmit power and trajectory. Finally, simulation results demonstrate that the proposed UAV-assisted cooperative CR scheme is effective for covert air-ground transmissions against multiple wardens.

The combination of reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) can improve the resource utilization in non-line-of-sight scenarios. However, the private information in this situation raises security concerns when the transmission behavior is detected by wardens. Against this background, we investigate a covert transmission problem in an RIS assisted ISAC system. Specifically, we obtain a tractable form of covertness constraint in terms of minimum detection error probability via the optimal detection threshold, paving the way for optimization process. Then, the sum covert transmission rate is maximized by jointly optimizing the beamforming of confidential signal and jamming signal as well as the RIS’s phase shift. To solve the above non-convex problem, we propose a joint covert beamforming and the phase shift of RIS optimization-based twins-deep deterministic policy gradient (CBP-TD3) algorithm. Finally, simulation results demonstrate the effectiveness of the proposed CBP-TD3 algorithm in the covertness.

Abstract Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

Reconfigurable intelligent surface (RIS) assisted cell-free multiple-input multiple-output (MIMO) systems have emerged as a promising paradigm for future wireless communications. To overcome the multiplicative fading effect inherent in the passive RIS, a novel active RIS equipped with reflection-type power amplifiers has been proposed, which however is confronted with high hardware cost and power consumption under the fully-connected architecture. To address this issue, we consider a cost-and power-efficient sub-connected active RIS assisted cell-free system in this paper, where the whole active RIS is divided into multiple sub-RISs, each being connected to a dedicated power amplifier. We aim to maximize the system sum rate under imperfect channel state information (CSI) by jointly optimizing the AP transmit beamforming matrices and the RIS reflection coefficient matrix. Since the traditional centralized beamforming scheme may lead to a high computational burden at the central processing unit (CPU), we propose a two-stage distributed iterative algorithm to efficiently find high-quality suboptimal solutions. Specifically, in stage 1, users apply the classical weighted minimum mean-square error (WMMSE) method to optimize their local variables in parallel. Then in stage 2, APs optimize their respective transmit beamforming matrices, the RIS reflection phase shift vector and the RIS reflection amplification vector sequentially. The corresponding semi-closed-form optimal solutions are available by jointly leveraging the Lagrange duality theory, majorization minimization (MM) and symmetric alternating direction method of multiplier (S-ADMM) techniques. Moreover, we develop a simplified distributed algorithm to further reduce system communication overhead. Numerical results demonstrate that the two proposed distributed algorithms can achieve comparable sum rate performance to the centralized scheme while attaining lower computational overhead.

integrated sensing and communication (ISAC) is a promising paradigm to alleviate spectrum congestion and facilitate a variety of emerging Internet of Things (IoT) applications. However, the direct links from the ISAC base station (BS) to the users may be blocked due to the obstacles. In this article, we investigate the double-active reconfigurable intelligent surfaces (RISs) assisted ISAC, where two active RISs are used to establish virtual line-of-sight (LoS) links from the ISAC BS to the users. In addition, the sum of the minimum sensing signal-to-interference-plus-noise ratios (SINRs) among multiple targets during a series of time slots is maximized, subject to Quality of Service (QoS) and transmit power constraints, through the joint optimization of transmit, reflection and receive beamforming. We first transform this nonconvex optimization problem in the dynamic environment into a Markov decision process (MDP), and then propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm to solve it. Moreover, to enhance the generalization and stability, we integrate the generative adversarial network (GAN) into the TD3 algorithm and propose a GAN-TD3-based algorithm to handle the beamforming optimization problem. Compared with the TD3-based algorithm, the proposed GAN-TD3-based algorithm achieves the better performance and higher stability at the cost of higher computational complexity and slower convergence speed. Simulation results are presented to verify the effectiveness of our proposed algorithms and the superiority of the active RIS over the passive counterpart.

Energy efficiency (EE) optimization has attracted significant research attention for implementing green communications. With cost-effective and low-power advantages, intelligent reflecting surface (IRS) and hybrid analog-digital transceiver have recently emerged as two promising technologies of next-generation green wireless systems. In this paper, we propose a comprehensive framework for EE optimization in four types of IRS-aided hybrid analog-digital multiuser multiple-input multiple-output communication systems, including the uplink (UL) systems under the sum power and box eigenvalue constraints as well as the per-radio-frequency chain power constraints (PRPCs), and the downlink (DL) systems under the sum power constraint and the PRPCs. This framework proposes a unified design methodology to these four considered systems by separating the optimization of analog and digital matrix variables. Specifically, for the UL EE maximization problems, we firstly propose a channel alignment based algorithm to separately optimize the analog precoders at users, the analog combiner at the base station and the IRS reflecting matrix, whose computational complexity is significantly reduced as compared with the traditional alternating optimization algorithm. Then, by introducing the auxiliary variables and exploiting the Karush-Kuhn-Tucker conditions based algorithm, the optimal digital precoders at users are obtained in closed forms. Furthermore, the intractable DL EE optimization can be equivalently transformed into its virtual UL counterpart using the DL-UL duality, leading to the general applicability of the proposed framework. Extensive simulations reveal that the proposed algorithm attains the almost identical EE performance to the traditional benchmarks with a lower computational complexity.

The combination of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and an unmanned aerial vehicle (UAV) can further improve channel quality and extend coverage. However, the high-quality air-to-ground link is more vulnerable to eavesdropping by adversaries. In this paper, we investigate STAR-RIS-assisted covert communication in UAV non-orthogonal multiple access (NOMA) networks with a warden Willie, where Alice intends to transmit the covert signal to a near user Bob under the cover of a far user Carol via STAR-RIS. We aim to maximize the covert transmission rate by jointly optimizing the active and passive beamforming as well as the UAV location. The error detection probability and optimal detection threshold for Willie are first derived to obtain an analytic solution for the minimum detection error probability. Then, an alternating optimization algorithm is proposed to maximize the covert transmission rate under the condition of guaranteeing the communication of Carol and satisfying the covertness constraint of Bob. Specifically, the nonconvex problem is decomposed into three sub-problems by block coordinate descent, which are then solved using semidefinite relaxation and successive convex approximation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed covert communication scheme for STAR-RIS assisted UAV air-ground networks.

Reconfigurable intelligent surface (RIS) has gained much attention as a cost-effective solution to enhance connectivity and coverage in massive machine-type communication. However, the passive nature of RIS poses fundamental challenges to decoupling and estimating base station (BS)-RIS and RIS-device channels, as well as identifying active devices. To effectively tackle this issue, we cast the joint channel estimation and activity detection for RIS-assisted Internet-of-Things networks as a tensor-based two-layer problem by exploiting the channel sparsity and a multi-frame pilot training structure. The first layer involves the Canonical Polyadic (CP) decomposition of a third-order tensor observation, while the second layer addresses compressive sensing (CS)-based simple measurement vector (SMV) and multiple measurement vector (MMV) problems. Then, by leveraging the Bayesian inference framework, we propose a tensor-based approximate message passing (TAMP) algorithm to estimate one-hop BS-RIS channel, one-hop RIS-device channels, and active IoT devices simultaneously. Furthermore, we conduct the state evolution (SE) analysis of TAMP to theoretically characterize its MSE. Numerical results corroborate the superior estimation and detection performance of TAMP and demonstrate that our SE analysis perfectly predicts the actual MSE.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising solution to significantly enhance the security of wireless communication systems. By combining the advantages of the conventional fully-active and fully-passive RISs, a novel hybrid active-passive RIS has been anticipated to achieve the excellent communication performance at a low cost. In this paper, we aim to maximize the secrecy rate in a hybrid active-passive RIS assisted multi-input single-output multi-antenna Eve (MISOME) system, where both fixed and dynamic hybrid RIS architectures are considered. To tackle this intractable problem, we jointly optimize the transmit covariance matrix at the base station (BS), the reflection matrices of active and passive sub-RISs, as well as the element allocation matrix for the dynamic hybrid RIS. Specifically, we firstly explore the rank-1 structure of the optimal BS transmit covariance matrix. Then, for the fixed hybrid RIS, we develop an efficient two-loop successive convex approximation (SCA) based iterative algorithm, where the optimal semi-closed-form solution to each subproblem can be obtained. For the dynamic RIS, this proposed algorithm is still applicable by relaxing the binary active/passive elements allocation variables into exponent-based continuous ones. Simulation results validate the superior secrecy performance of the proposed designs over the existing fully-active and fully-passive RIS designs. Moreover, it is demonstrated that the dynamic hybrid RIS is able to strike a good balance between the passive beamforming gain and the power amplification gain to adapt to the varying propagation environment.

This paper investigates user equipment (UE) assisted device-free networked sensing in the sixth-generation (6G) integrated sensing and communication (ISAC) system, where one base station (BS) and multiple UEs, such as unmanned aerial vehicles (UAVs), serve as anchors to cooperatively localize multiple passive targets based on the range information. Three challenges arise from the above scheme. First, the UEs are not perfectly synchronized with the BSs. Second, the UE (anchor) positions are usually estimated by the Global Positioning System (GPS) and subject to unknown errors. Third, data association is challenging, since it is hard for each anchor to associate each rang estimation to the right target under device-free sensing. We first tackle the above three challenges under a passive UE based sensing mode, where UEs only passively hear the signals over the BS-target-UE paths. A two-phase UE assisted localization protocol is proposed. In Phase I, we design an efficient method to accurately estimate the ranges from the BS to the targets and those from the BS to the targets to the UEs in the presence of synchronization errors between the BS and the UEs. In Phase II, an efficient algorithm is proposed to localize the targets via jointly removing the UEs with quite inaccurate position information from the anchor set and matching the estimated ranges at the BS and the remaining UEs with the targets. Next, we also consider an active UE based sensing mode, where the UEs can actively emit signals to obtain additional range information from them to the targets. We show that this additional range information can be utilized to significantly reduce the complexity of Phase II in the aforementioned two-phase localization protocol. Numerical results show that our proposed UE assisted networked sensing scheme can achieve very high localization accuracy.

Holographic metasurface antenna (HMA) has been envisioned as a new antenna paradigm anticipated to realize massive multiple-input multiple-output (MIMO) capability with greatly reduced hardware cost and power consumption. In this paper, we develop a framework for the energy efficiency (EE) optimization in the HMA-assisted uplink (UL) multiuser MIMO (MU-MIMO) system. We consider two types of power constraints, namely, the sum power and box eigenvalue constraints (SPBECs) and the multiple weighted power constraints (MWPCs). In this framework, we firstly formulate a general EE maximization problem subject to SPBECs and propose a novel EE-oriented water-filling algorithm by jointly exploring the quasi-concave property of the EE function and introducing an actual power consumption factor. Based on this, we then develop a low-complexity two-stage algorithm to separately optimize the HMA weighting matrix and the transmit covariance matrix. Specifically, in the first stage, two different algorithms, i.e., the channel alignment based algorithm and the weighted minimum mean square error (WMMSE) based algorithm, are proposed to optimize the HMA weighting matrix. In the second stage, we apply the proposed novel EE-oriented water-filling algorithm to optimize the transmit covariance matrix by respectively introducing per-user and all-user power consumption factors. Moreover, this two-stage algorithm is applicable to the EE optimization under MWPCs by leveraging duality theory to integrate multiple power constraints into a single one. Finally, numerical simulations validate that the proposed algorithms can achieve comparable EE performance to traditional benchmark schemes with significantly reduced computational complexities.

In this paper, we analyze the covert UAV-to-UAV (U2U) communication with the ground warden under a Poisson field of ground interferers and the blockage effect of air-to-ground propagations. With the aid of stochastic geometry, we derive the average covert probability and connection outage probability to quantify the covert communication performance for the scenarios with and without interference. By comparing the two scenarios, the introduction of interference increases the minimum average covert probability. Increasing both the density and transmission power of interferers can improve the average covert probability. However, the improvement of covertness is achieved by increasing the connection outage probability. To capture the competing requirements of covertness and reliability, we analyze the effective covert communication rate defined as the product of the average covert probability, connection success probability, and data transmission rate. The UAV transceiver can also flexibly raise its flight altitude to improve effective covert communication rate. Moreover, our results reveal that covert U2U communication performs better in high blockage environments such as dense urban. In summary, this study provides theoretical guidance for designing covert U2U communication systems.

Future mobile networks are projected to support integrated sensing and communications in high-speed communication scenarios. Nevertheless, large Doppler shifts induced by time-varying channels may cause severe inter-carrier interference (ICI). Frequency domain shows the potential of reducing ISAC complexity as compared with other domains. However, parameter mismatching issue still exists for such sensing. In this paper, we develop a novel sensing scheme based on sparse Bayesian framework, where the delay and Doppler estimation problem in time-varying channels is formulated as a 3D multiple measurement-sparse signal recovery (MM-SSR) problem. We then propose a novel two-layer variational Bayesian inference (VBI) method to decompose the 3D MM-SSR problem into two layers and estimate the Doppler in the first layer and the delay in the second layer alternatively. Subsequently, as is benefited from newly unveiled signal construction, a simplified two-stage multiple signal classification (MUSIC)-based VBI method is proposed, where the delay and the Doppler are estimated by MUSIC and VBI, respectively. Additionally, the Cramér-Rao bound (CRB) of the considered sensing parameters is derived to characterize the lower bound for the proposed estimators. Corroborated by extensive simulation results, our proposed method can achieve improved mean square error (MSE) than its conventional counterparts and is robust against the target number and target speed, thereby validating its wide applicability and advantages over prior arts.

Although integrated sensing and communication (ISAC) has an advantage of mutual gain of its dual functions, it is susceptible to be eavesdropped by mobile targets due to the broadcast nature of wireless channels. In this paper, we propose a secure predictive beamforming scheme against a mobile eavesdropping target for ISAC, where the intelligent reflecting surface (IRS) is utilized to assist the sensing and secure transmission. To tackle the mobility of eavesdropping target, we first develop a secure predictive beamforming protocol and formulate a sum secrecy rate maximization problem. However, due to the non-convex objective function and the outdated channel state information (CSI), it is difficult to solve the problem directly. Thus, we develop a deep learning based predictive beamforming scheme, which incorporates the parallel convolutional neural network, the long short-term memory modules and the attention mechanism to learn the features from the historical CSI. It can directly design the beamformings for the next time slot with low computational complexity and bypass the need of CSI prediction. Simulation results show that the proposed scheme can significantly enhance the security of ISAC with low overhead.

Although deploying unmanned aerial vehicles (UAVs) can provide line-of-sight (LoS) links to extend the coverage, it also poses severe challenges for covert air-ground transmission. ln this paper, we investigate the covert air-ground finite-blocklength communication with ground blockages, where the UAV acts as an aerial relay to achieve the long-distance transmission. First, we analyze the warden detection performance with its optimal detection threshold derived, which is the worst case for the legitimate transmission. To maximize the covert transmission rate while satisfying the covertness constraint, the blocklength, the transmit power, and the position of UAV are jointly optimized. By analyzing the monotonicity of transmit power and blocklength with respect to the relative entropy, we derive their closed-form solutions. Then, we analyze the optimal hovering position of UAV from two perspectives of the distance and the elevation angle between UAV and transmitter. Specifically, we obtain an optimal elevation angle with fixed distance and the optimal distance with fixed elevation angle through analyzing the received signal-to-noise ratio and the feasible hovering regions of UAV. Numerical results demonstrate the effectiveness of the proposed covert UAV relaying scheme with ground blockages.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. To guarantee robust data security and privacy protection, covert communication can be employed in ISAC systems. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station transmits the sensing beamforming to continuously sense an aerial target while communicating with a ground receiver with a probability of 0.5 via the communication beamforming. First, we derive a closed-form expression of the detection outage probability of each warden to obtain the global detection outage probability. Under the worst case that the wardens can collusively adjust their detection thresholds to achieve the best detection performance, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate. To tackle this non-convex problem, unitary-iteration and zero-forcing schemes are proposed to transform it into convex ones via semidefinite relaxation and successive convex approximation, respectively. Numerical results demonstrate the validity of the proposed covert ISAC scheme, which can achieve a better trade-off among communication, sensing and covertness compared to benchmarks.

Integrated sensing and communication (ISAC) has been envisioned as a prospective technology to enable ubiquitous sensing and communications in next-generation wireless networks. In contrast to existing works on reconfigurable intelligent surface (RIS) aided ISAC systems using conventional phased arrays (PAs), this paper investigates a frequency diverse array (FDA)-enabled RIS-aided ISAC system, where the FDA aims to provide a distance-angle-dependent beampattern to effectively suppress the clutter, and RIS is employed to establish high-quality links between the BS and users/target. We aim to maximize sum rate by jointly optimizing the BS transmit beamforming vectors, the covariance matrix of the dedicated radar signal, the RIS phase shift matrix, the FDA frequency offsets and the radar receive equalizer, while guaranteeing the required signal-to-clutter-plus-noise ratio (SCNR) of the radar echo signal. To tackle this challenging problem, we first theoretically prove that the dedicated radar signal is unnecessary for enhancing target sensing performance, based on which the original problem is much simplified. Then, we turn our attention to the single-user single-target (SUST) scenario to demonstrate that the FDA-RIS-aided ISAC system always achieves a higher SCNR than its PA-RIS-aided counterpart. Moreover, it is revealed that the SCNR increment exhibits linear growth with the BS transmit power and the number of BS receive antennas. In order to effectively solve this simplified problem, we leverage the fractional programming (FP) theory and subsequently develop an efficient alternating optimization (AO) algorithm based on symmetric alternating direction method of multipliers (SADMM) and successive convex approximation (SCA) techniques. Numerical results demonstrate the superior performance of our proposed algorithm in terms of sum rate and radar SCNR.

In this paper, we investigate a secure transmission problem in a simultaneously transmitting and reflecting re-configurable intelligent surface (STAR-RIS) assisted integrated sensing and communication (ISAC) system. The STAR-RIS is exploited to establish line-of-sight links for sensing the target and transmitting the confidential signal to users via non-orthogonal multiple access. Specifically, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming, artificial jamming and STAR-RIS's passive transmission and reflection beamformings, while ensuring the minimum beam-pattern gain required by the target is met. To deal with the non-convex problem, we divide it into two subproblems and leverage successive convex approximation to transform them into convex ones. Then, an alternating optimization algorithm is proposed to solve the problem iteratively. Simulation results demonstrate the effectiveness of the proposed scheme and the potential of STAR-RIS to enhance the security of ISAC.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.

We study a semi-passive intelligent reflecting surface (IRS) enabled ISAC, where IRS is employed to assist the secure communication and perform target sensing. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. We derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve the performance tradeoff, we design a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for improving the spectral- and energy-efficiency of millimeter-wave (mmWave) Internet of Vehicles (IoV) communications, but the conception of their accurate channel estimation poses. Hence, the existing estimation methods mainly focus on time-invariant channels, while ignoring the Doppler effect induced by the high-velocity vehicles, which will lead to significant performance degradation. In this article, we investigate the problem of channel estimation in RIS-aided mmWave IoV systems considering the deleterious Doppler effect. First, we derive the expression of the time-varying cascaded two-hop multiple-path channels, where each delay tap is subject to multiple paths instead of having a simple one-to-one correspondence. In order to decouple the paths, the problem is formulated in the delay-domain by a series of transformations and the cascaded two-hop channel can be estimated at each delay tap. Then, we propose a pair of estimation strategies by considering different hardware constraints depending on the number of receiver antennas at the base station (BS). When a large receiver array is employed at the BS, we can exploit its high angular selectivity for distinguishing each resolvable path at a certain delay tap because they arrive from different directions. However, this cannot be achieved for small arrays, given their more limited angular resolution. Thus, the RIS reflection patterns are delicately designed for distinguishing multiple resolvable paths. After separating the paths, Doppler estimation can be performed by calculating the phase difference of the adjacent symbols. Our simulation results demonstrate the superior performance of the proposed methods within a wide range of Doppler shifts.

In this article, we investigate the analog–digital hybrid transceiver optimization for multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, aiming at maximizing the sum rate of multiple IoT devices in downlink communications. We first derive the downlink–uplink duality for the MIMO communications with analog–digital hybrid structures. Based on this, the intractable MIMO downlink sum-rate maximization is equivalently transferred into an easier-to-handle virtual uplink counterpart. In order to solve the nonconvex virtual uplink problem effectively, we resort to decouple the involved digital and analog matrix variables. On the one hand, we propose two kinds of algorithms for the analog matrices optimizations, namely, the joint design and the separate design. Specifically, the joint design optimizes the analog precoder and equalizer matrices in an alternating manner. In each iteration, an element-wise optimization algorithm is utilized to optimize the analog matrix variables under constant modulus constraints. For the separate design, the analog precoder and equalizer matrices are optimized separately via the elaborately designed space alignments with lower computational complexities. On the other hand, the digital precoders can be computed with fixed analog matrix variables, in which a modified iterative water-filling algorithm is proposed. Finally, numerical results demonstrate the superior performance advantages of the proposed algorithms over several benchmark algorithms.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been a promising enabler for the future wireless network due to its full-space coverage capability. In this article, we investigate the STAR-RIS assisted hybrid mmWave multiple-input-multiple-output system, where the two practical operating protocols, i.e., energy splitting (ES) and mode switching (MS), and the coupled transmission and reflection (T&R) phase-shift model for the STAR-RIS are considered. For each operating protocol, we aim to maximize the system weighted sum rate (WSR) by jointly optimizing the passive T&R coefficients at the STAR-RIS and the hybrid analog-digital precoder/combiners, subject to the discrete phase shift constraints. Specifically, we propose an efficient weighted minimum mean-square error based alternating optimization (AO) algorithm to address this highly coupled nonconvex problem. By leveraging the special ordered set of type 1 under the MS protocol, the optimization of both the discrete T&R coefficients and analog precoder/combiners can be equivalently transformed into the standard binary quadratic programming, which can be effectively solved by the mathematical programming with the equilibrium constraints-based exact penalty algorithm. The proposed penalty-based AO algorithm is also applicable to the WSR maximization under the ES protocol. In addition, to avoid high-complexity iterative process wherever possible, we develop a separate analog-digital beamforming scheme, where a fast projection-based gradient descent algorithm is applied to successively optimize discrete T&R coefficients and analog precoder/combiners to maximize the effective channel gain, and then the optimal digital precoder/combiners are obtained in semi-closed forms. Numerical simulation results demonstrate the superior WSR performance and complexity advantage of the proposed algorithms over the existing benchmark schemes.

Reconfigurable intelligent surface (RIS) has evolved into a promising approach to significantly improve both spectral and energy efficiencies of wireless communications. Different from the traditional fully-passive and fully-active RISs, a novel hybrid RIS composed of both active and passive reflecting elements has recently emerged, which can leverage their combined advantages to effectively mitigate the RIS-induced multiplicative path loss. In this paper, we investigate a hybrid active-passive RIS assisted wireless system from a rate-maximization perspective. Specifically, we firstly consider the multi-antenna multi-user system and aim to maximize the system weighted sum rate (WSR) by jointly optimizing the transmit precoding matrices and the active-passive RIS reflection matrix. The optimal semi-closed-form solution to each subproblem is obtained by jointly exploring the activeness of constraints and leveraging the majorization-minimization (MM) technique. To gain more useful insights into the rate maximization, we also study the special single-antenna single-user scenario, in which it is revealed that both the optimal transmit beamsteering direction and the optimal phase shifts at the hybrid RIS are independent of actual reflection amplitudes of the hybrid RIS. Numerical results demonstrate the lower complexity and superior rate performance of our proposed algorithms as compared to the existing schemes adopting the fully-passive RIS. Moreover, it is revealed that the hybrid RIS can strike a flexible balance between the square-order beamforming gain of the fully-passive RIS and the power amplification gain of the fully-active RIS by adjusting the active/passive element allocation.

Matrix-variate optimization plays a central role in advanced wireless system designs. In this paper, we aim to explore optimal solutions of matrix variables under two special structure constraints using complex matrix derivatives, including diagonal structure constraints and constant modulus constraints, both of which are closely related to the state-of-the-art wireless applications. Specifically, for diagonal structure constraints mostly considered in the uplink multi-user single-input multiple-output (MU-SIMO) system and the amplitude-adjustable intelligent reflecting surface (IRS)-aided multiple-input multiple-output (MIMO) system, the capacity maximization problem, the mean-squared error (MSE) minimization problem and their variants are rigorously investigated. By leveraging complex matrix derivatives, the optimal solutions of these problems are directly obtained in closed forms. Nevertheless, for constant modulus constraints with the intrinsic nature of element-wise decomposability, which are often seen in the hybrid analog-digital MIMO system and the fully-passive IRS-aided MIMO system, we firstly explore inherent structures of the element-wise phase derivatives associated with different optimization problems. Then, we propose a novel alternating optimization (AO) algorithm with the aid of several arbitrary feasible solutions, which avoids the complicated matrix inversion and matrix factorization involved in conventional element-wise iterative algorithms. Numerical simulations reveal that the proposed algorithm can dramatically reduce the computational complexity without loss of system performance.

To alleviate the time overhead of the handover and beam reselection caused by user mobility, a velocity-based association policy has been adopted in heterogeneous networks. However, it might introduce severe interference from cross-tier interfering base stations which are closer to the users than the serving one. To deal with this problem, we propose two cross-tier interference coordination (CTIC) schemes for mobile users in heterogeneous networks: one jointly considers the transmit power and path loss (TPL-CTIC), and the other further incorporates the directional antenna gain (TPG-CTIC). Considering the beam misalignment and the time overhead caused by user mobility, we derive the overall coverage probability and the average effective Shannon rate (ESR) of mobile users to characterize the user-perceived performance. Taking the cost of the proposed CTIC schemes into account, we further derive the average normalized throughput to evaluate the overall network performance. Numerical results demonstrate that compared with the case without CTIC, the proposed schemes significantly improve the overall coverage probability in the low signal-to-interference-plus-noise ratio regime and the average ESR. To capture the expense of proposed schemes, we define the average normalized throughput. Although TPL-CTIC performs better in terms of the overall coverage probability and the average ESR, TPG-CTIC outperforms in terms of the average normalized throughput.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications and ultra-high frequency bands are both potential enablers for satisfying extreme performance requirements of future wireless systems. Thanks to low hardware cost and power consumption, holographic metasurface antennas (HMAs) operating at high frequencies have recently emerged as an effective realization of large-scale antenna arrays, leading to greatly enlarged near-field region. In this paper, we investigate a power-efficient HMA-based near-field downlink multiuser system, where three different HMA-based arrays are considered. Specifically, we aim to minimize the total transmit power for each HMA-based array while maintaining the signal to interference plus noise ratio (SINR) constraint of each user by jointly optimizing the digital transmit precoder and the analog HMA weighting matrix. In the special single-user scenario, we validate that the original optimization problem can be decomposed into several independent subproblems each corresponding to a single HMA microstrip, whose optimal solution can be obtained by the successive convex approximation (SCA) based method. It is also revealed that the HMA-based array is capable of achieving near-field beam focusing. In the general multiuser scenario, we develop an efficient SCA-alternating direction method of multipliers (ADMM) based alternating optimization (AO) algorithm to tackle the intractable optimization problem, where the digital precoders and the HMA weighting matrix are iteratively optimized in an alternating manner. Numerical results demonstrate the superior performance of our proposed algorithms over existing benchmark schemes. It is also shown that the HMA-based array attains lower hardware overhead and power consumption as compared to the conventional hybrid array.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modifiedK-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Aiming at providing wireless communication systems with environment-perceptive capacity, emerging integrated sensing and communication (ISAC) technologies face multiple difficulties, especially in balancing the performance trade-off between the communication and radar functions. In this paper, we introduce a reconfigurable intelligent surface (RIS) to assist both data transmission and target detection in a dual-functional ISAC system. To formulate a general optimization framework, diverse communication performance metrics have been taken into account including famous capacity maximization and mean-squared error (MSE) minimization. Whereas the target detection process is modeled as a general likelihood ratio test (GLRT) due to the practical limitations, and the monotonicity of the corresponding detection probability is proved. For the single-user and single-target (SUST) scenario, the minimum transmit power for sensing has been revealed. By exploiting the optimal conditions, we validate that the optimal BS satisfies the maximum power allocation criterion and derive the optimal BS precoder in a semi-closed form. Moreover, an alternating direction method of multipliers (ADMM) based RIS design is proposed to address the non-convex radar constraint. For the sake of enhancing computational efficiency, a low-complexity RIS design is also developed based on the manifold optimization theory. Furthermore, the ISAC transceiver design for the multiple-users and multiple-targets (MUMT) scenario is also investigated, where a zero-forcing (ZF) radar receiver is adopted to cancel the interference signals from different targets. Then optimal BS precoder is derived under the maximum power allocation scheme, and the RIS phase shifts can be optimized by extending the proposed ADMM-based RIS design algorithm. Finally, the ISAC transceiver design with imperfect in-band full-duplex transceivers is also discussed and two radar receive beamformer designs have been proposed to mitigate the performance loss. Numerical simulation results verify the convergence and superior communication/sensing performance of our proposed transceiver designs.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

Beamforming design with partial channel estimation and feedback for frequency-division duplexing (FDD) reconfigurable intelligent surface (RIS) assisted systems is considered in this paper. We leverage the observation that path angle information (PAI) varies more slowly than path gain information (PGI). Then, several dominant paths are selected among all the cascaded paths according to the known PAI for maximizing the spectral efficiency of downlink data transmission. To acquire the dominating path gain information (DPGI, also regarded as the path gains of selected dominant paths) at the base station (BS), we propose a DPGI estimation and feedback scheme by jointly beamforming design at BS and RIS. Both the required number of downlink pilot signals and the length of uplink feedback vector are reduced to the number of dominant paths, and thus we achieve a great reduction of the pilot overhead and feedback overhead. Furthermore, we optimize the active BS beamformer and passive RIS beamformer by exploiting the feedback DPGI to further improve the spectral efficiency. From numerical results, we demonstrate the superiority of our proposed algorithms over the conventional schemes.

Both the reconfigurable intelligent surface (RIS) and the hybrid analog-digital antenna array have been envisioned as two cost-effective and promising technologies for achieving various types of functionality enhancement of future wireless systems. In this paper, we develop a framework for the multi-functional optimization in the RIS-aided hybrid analog-digital multiple-input multiple-output (MIMO) system, where a board of performance metrics related to diverse system functionalities are considered, such as capacity and mean square error (MSE) for information transmission (IT), Cramer-Rao bound (CRB) for radar sensing, harvested energy for energy harvesting (EH) and so on. Under this framework, we focus on two types of multi-functional optimization problems, namely, the multi-objective multi-functional optimization and the single-objective optimization subject to multi-functional constraints, and propose a unified low-complexity algorithm by separately optimizing analog and digital matrix variables. Specifically, for the multi-objective optimization, we firstly propose the numerical quadratic optimization based (QuaOpt-based) algorithm and the low-complexity channel alignment based algorithm to separately optimize analog matrices, including the RIS reflecting matrix, the analog precoder and the analog equalizer. Then, for the optimization of digital precoder, the numerical semidefinite programming (SDP)-based algorithm and the QuaOpt-based algorithm are proposed to iteratively solve the digital precoder optimization problem, while the matrix-monotonic optimization based algorithm derives the optimal closed-form solution in low computational complexity. Whereas for the single-objective optimization, the above proposed algorithms are still applicable by applying the Lagrangian duality theory to tackle the multi-functional constraints. Numerical simulation results reveal that the proposed low-complexity algorithm can achieve comparable performance to numerical algorithms.

Integrated sensing and communication (ISAC) has evolved into an influential technique to ameliorate energy and spectrum scarcity via co-designing these two functionalities. However, the target can be a potential eavesdropper aiming at wiretapping the information transmitted to the communication user. This paper studies a semi-passive intelligent reflecting surface (IRS) enabled ISAC system, where the IRS is employed to assist the secure communication and simultaneously perform the target sensing based on the echo signals received by the dedicated sensor at the IRS. Specifically, we model two types of targets, namely point targets and extended targets. The direction-of-arrival (DoA) of the former and the complete target response matrix of the latter should be estimated. Under this configuration, we derive the Cramér-Rao bound (CRB) as the performance metric of target estimation. To achieve an optimal performance trade-off, we formulate a weighted optimization problem that balances maximizing the secrecy rate and minimizing the CRB, via jointly optimizing the transmit beamforming and the phase shifts of IRS. Then, we employ the alternating optimization, successive convex approximation and semi-definite relaxation to tackle the proposed non-convex problems for the two target cases. Simulation results show the effectiveness of the proposed schemes compared with benchmarks.

Integrated sensing and communication (ISAC) has attracted plenty of attentions as an up-and-coming approach to address the spectrum congestion via sharing the same hardware platform. However, including communication information in the sensing waveform will raise the risk of eavesdropping by the sensing targets as the eavesdroppers. In this paper, we investigate a secure transmission problem in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted ISAC system, which is segmented into a sensing region and a communication region. The STAR-RIS is deployed to build line-of-sight (LoS) links for sensing the target as well as transmitting the confidential signal to users via non-orthogonal multiple access. Specifically, the sum secrecy rate is maximized by jointly optimizing the transmit beamforming, artificial jamming and STAR-RIS’s passive transmission and reflection beamformings, while ensuring the minimum beampattern gain required by the target. To deal with the non-convex problem, we divide it into two subproblems and leverage successive convex approximation to transform them into convex ones. Then, an alternating optimization algorithm is proposed to solve the problem in an iterative manner. Simulation results demonstrate the validity of the proposed scheme and the potential of STAR-RIS to enhance the security of ISAC.

Millimeter wave (mmWave) signals are sensitive to blockages in wireless channels. Traditional mmWave transceiver designs intend to harvest both beamsteering and spatial multiplexing gains, but without considering the potential change in the channel state incurred by sudden blockages. In this paper, we propose a blockage-resilient hybrid transceiver design for supporting robust data transmissions in the face of dynamic blockages. Upon exploiting the spatial structure of mmWave channels, we formulate a weighted spectral efficiency maximization problem by utilizing the statistical information concerning the potential future blockages of different path clusters, which uniquely distinguishes this work from existing transceiver optimization problems. On the basis of alternating optimization, we propose a two-stage algorithm to deal with the resultant non-convex problem riddled with highly coupled variables. First, we alternatively optimize the fully digital transmit precoder and receive equalizer by transforming the optimization problem into a quadratic form. Based on the Block Successive Upper-bound Minimization (BSUM) framework, the optimal fully digital precoder and equalizer can be found by exploiting the Karush-Kuhn-Tucker (KKT) conditions and the matrix monotonic method. Then, inspired by the sparse signal recovery philosophy, the hybrid analog/digital transceiver structure is designed for approximating the fully digital solution. Our numerical results show that the proposed design strikes an improved throughput vs. blockage-resilience trade-off compared to existing schemes, which demonstrates its superiority.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. Moreover, ISAC outperforms traditional separate radar and communication systems in terms of both power consumption and spectral efficiency. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, both cases of independent and coupled phase shifts at STAR-RIS are investigated. For independent phase shifts, we develop an alternating direction method of multipliers (ADMM)-based algorithm to decouple the original problem into several tractable subproblems that facilitates the derivation of a closed-form solution to each subproblem. In the scenario with the coupled phase shifts, we first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed schemes, demonstrating STAR-RIS’s superiority over conventional RIS. Moreover, the adopted protocol of STAR-RIS can maintain an excellent balance between performance and complexity.

Reinforcement learning based unmanned aerial vehicle (UAV) swarm communications have to address the challenges raised by the large-scale dynamic network and strong jamming and interference. In this paper, we propose a multiagent reinforcement learning based UAV swarm anti-jamming communication scheme to optimize the UAV relay selection and power allocation based on the network topology, channel states, previous performance and the network states shared by neighboring UAVs. This scheme formulates the policy distribution to improve the policy space exploration and designs a soft learning mechanism to guide the policy update and stabilize the learning process. According to transfer learning, the shared swarm experiences are exploited to accelerate the initial policy learning. We investigate the computational complexity of the proposed scheme and derive the performance bound regarding the message bit error rate, the swarm energy consumption and the utility. Simulation results show that the proposed scheme improves the swarm communication performance and saves energy consumption compared with the benchmark scheme.

In view of the nonideality of communication links in the Internet of Things (IoT) originating from transceiver hardware impairments, in this article, we introduce a general framework for hardware impairments-aware multiantenna transceiver design, which considers different availabilities of CSI at the transmitter (CSIT) and the receiver (CSIR). The well-known Kronecker model is applied to characterize stochastic channel state information (CSI) errors. For each case, we aim to minimize the (average) total mean square error (MSE) of all data streams subject to the practical per-antenna power constraints. To address the nonconvexity of the formulated problem, we propose an efficient majorization–minimization (MM)-based iterative algorithm to transform the original problem into a series of convex subproblems with semiclosed-form optimal solutions. For low-complexity implementation, we also develop an alternative scheme for directly finding a high-quality suboptimal solution by considering both worst case hardware impairments and worst case CSI errors. In particular, since an explicit expression of the average total MSE for the perfect CSIR and imperfect CSIT case is hard to derive, we instead optimize its effective upper and lower bounds. The prospective applications of our work in the two currently popular multiple-input–multiple-output (MIMO) IoT scenarios are then discussed. Furthermore, we fundamentally reveal the MSE floor effect caused by both hardware distortion and CSI imperfection in the high-SNR regime. Numerical results illustrate the excellent average total MSE and average bit error rate (BER) performance of our proposed algorithms over the adopted benchmark schemes.

Reconfigurable intelligent surface (RIS) has been envisioned as an innovative technology to assist millimeter wave (mmWave) communications. Thanks to both advantages of low hardware cost and low power consumption, the hybrid transceiver structure also becomes an integral component of mmWave systems. However, due to practical limitations of hardware components, the RIS-assisted mmWave communications usually suffer unavoidable hardware impairments (HWIs). In this paper, we aim to minimize the (sum) MSE and maximize the average rate of the hardware-impaired RIS-assisted point-to-point mmWave MIMO system, respectively, by jointly optimizing the hybrid transceiver and RIS reflection coefficients under the realistic discrete phase shift constraints. We firstly consider the single-antenna user case and propose efficient alternating optimization (AO) algorithms to solve the two intractable problems. A binary-oriented exact penalty (BEP) method is developed for the involved discrete optimization, which is able to strike a good trade-off between performance and complexity. Moreover, we analyze the optimality of AO algorithms under the cascaded line-of-sight (LoS) channel condition, and reveal both the MSE floor effect and average rate saturation effect in the high-SNR regime. The above studies are then extended to the general multi-antenna user case, where a low-complexity two-phase scheme with the aim of creating the favorable RIS-cascaded channel in the first phase and enhancing system performance in the second phase is proposed. This two-phase scheme is also demonstrated to attain the optimal performance in the LoS scenario. Numerical results validate our theoretical analysis and illustrate superior performance of the proposed algorithms over various benchmark schemes.

With the benefits of unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), they can be combined to further enhance the communication performance. However, the high-quality air-ground channel is more vulnerable to the adversarial eavesdropping. Therefore, in this paper, we propose a covert communication scheme assisted by the UAV-IRS to maximize the covert transmission rate. Specifically, the ground transmitter, Alice, secretly delivers the private message to a legitimate receiver, Bob, via the UAV-IRS, wishing that the transmission will not be observable by the warden, Willie. In addition, Willie is adversarial to Alice and the UAV-IRS, which makes his accurate location difficult to obtain. Given this fact, we first determine an optimal detection threshold and derive the error detection probability at Willie, which is the worst-case situation for the legitimate transmission. Then, we maximize the covert transmission rate by alternatively optimizing the transmit power of Alice, the IRS phase shift and the horizontal location of UAV-IRS subject to the covert requirements. Numerical results are presented to demonstrate the effectiveness of the proposed covert communication scheme assisted by UAV-IRS.

In many core problems of signal processing and wireless communications, Karush-Kuhn-Tucker (KKT) conditions based optimization plays a fundamental role. Hence we investigate the KKT conditions in the context of optimizing positive semidefinite matrix variables under nonconvex rank constraints. More explicitly, based on the properties of KKT conditions, we optimize a reconfigurable intelligent surface (RIS) aided multi-user multi-input multi-output (MU-MIMO) network. Specifically, we consider the capacity maximization and sum mean square error (MSE) minimization problems of both the RIS-aided MU-MIMO uplink (UL) and downlink (DL) under multiple weighted power constraints and rank constraints. As for the RIS-aided MU-MIMO UL, the optimal structures of the signal covariance matrices are derived based on the KKT conditions. Furthermore, an efficient procedure is designed for solving the capacity maximization and sum mean square error (MSE) minimization problems. Then the UL-DL dualities are exploited for solving the capacity maximization and MSE minimization problems of the RIS-aided MU-MIMO DL based on the results of the UL optimization. Hence in the proposed framework, the phase shifting matrix of the RIS is jointly optimized with the signal covariance matrices for both the UL and DL. Our simulation results demonstrate the performance advantages of the proposed framework.

Reconfigurable intelligent surface (RIS) is able to enhance the capacity of wireless communication systems with low overhead. Extremely large (XL)-RIS-aided millimeter-wave (mmWave) communication has become a promising key technique for future 6-th Generation (6G) systems. The performance gain brought in by XL-RIS relies on the accurate channel state information (CSI). However, channel estimation requires huge training overhead and high computational complexity due to the XL number of passive elements at RIS. Moreover, the unknown visual region (VR) infomation caused by the sensitivity of mmWave signal to random blockages makes the channel estimation more difficult. In this paper, we consider the channel estmation for XL-RIS-aided mmWave uplink system. We firstly model the XL-RIS-aided channel as a hybrid one composed of near-field RIS-to-user channel and far-field RIS-to-base station (BS) channel, where the VR issue of XL-RIS has been taken into consideration. Then we formulate the channel estimation problem as a sparse recovery problem. To solve this problem, we propose a two-stage algorithm for joint channel estimation and VR detection. Finally numerical results show that the proposed algorithms outperform the existing benchmark schemes in terms of normalized mean-squared error (NMSE) due to the VR detection and the utilization of shift common-support property among sub-channels.

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.

The swarm relay and power allocation policy determines the bit error rate and the energy consumption of unmanned aerial vehicles (UAVs) and can be optimized based on the network and jamming model, which is rarely known by UAVs. In this paper, we propose a multi-agent reinforcement learning (RL)-based UAV swarm communication scheme to optimize the relay selection and power allocation against jamming. Based on the network topology, channel states, previous performance and observations shared by the neighboring UAVs, this scheme formulates the policy distribution to improve the policy exploration and applies a policy learning mechanism to stabilize the learning process. Based on transfer learning, the shared swarm experiences are exploited to accelerate the initial learning and improve policy optimization. A deep RL-based scheme is proposed to mitigate the state quantization error for the rapidly changing channel states under high swarm moving speed and thus further improve the anti-jamming performance. This scheme designs a policy network with four fully connected layers to approximate the policy distribution and uses another two neural networks to estimate the average policy distribution and the expected long-term utility, respectively, to update the policy network for stabilized deep learning. We investigate the computational complexity and derive the performance bound regarding the bit error rate, the energy consumption and the utility. Simulation and experimental results verify the performance gain of our proposed schemes over related works.

In this paper, we propose a general framework on the hybrid analog-digital transceiver design for multi-hop communications. For the inclusive purpose, a transceiver model unifying both linear and nonlinear transceivers has been taken into account. Various performance metrics, including the most representative capacity and weighted mean-squared error (MSE), have been investigated in a unified manner. In particular, to meet practical needs for the quality of services (QoS), a general eigenvalue power constraint model is introduced, which contains a sum power constraint and box eigenvalue constraints as special cases. Specifically, by carefully designing the auxiliary analog and digital beamformers, the multi-hop transceiver optimization is decomposed into a series of independent sub-problems, where the analog beamformers for different hops are completely decoupled. Based on that, this framework establishes a majorization-minimization (MM) based analog beamformer design algorithm, which is able to handle the complicated weighted unit-modulus matrix optimizations by finding their semi-closed-form solutions. Furthermore, an efficient waterfilling algorithm is proposed for the digital beamformer designs to deal with the difficulties of optimizations subject to the multiple eigenvalue power constraints. The numerical results are provided to demonstrate the performance advantages of the proposed framework.

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation-based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

In this article, we investigate the training optimization for multiple-input–multiple-output (MIMO)-aided Internet of Things (IoTs) systems that employ subarray-based intelligent reflecting surface (IRS). In order to overcome the nonlinear relationship between two cascaded channel matrices, the IRS can be divided into a series of subarrays, for which only an equivalent cascaded channel matrix should be estimated in each subarray. Correspondingly, the training sequence should be divided into multiple segments. By sufficiently utilizing the available statistical channel state information (CSI), either mean-square error (MSE) minimization or mutual information (MUI) maximization can be taken as the performance metric for optimizing the training sequence. A variety of fairnesses among different subarray channel estimations has been taken into account. Furthermore, in order to reduce the hardware cost of the power amplifier, we propose a two-stage training sequence structure, including a fully digital filter and a constant modulus sequence. To further reduce computational complexity, various low-complexity water-filling solutions are proposed. Numerical results demonstrate the accuracy and efficiency of the proposed solutions.

In this paper, we investigate a joint device activity detection (DAD), channel estimation (CE), and data decoding (DD) algorithm for multiple-input multiple-output (MIMO) massive unsourced random access (URA). Different from the state-of-the-art slotted transmission scheme, the data in the proposed framework is split into only two parts. A portion of the data is coded by compressed sensing (CS) and the rest is low-density-parity-check (LDPC) coded. In addition to being part of the data, information bits in the CS phase also undertake the task of interleaving pattern design and CE. The principle of interleave-division multiple access (IDMA) is exploited to reduce the interference among devices in the LDPC phase. Based on the belief propagation (BP) algorithm, a low-complexity iterative message passing (MP) algorithm is utilized to decode the data embedded in these two phases separately. Moreover, combined with successive interference cancellation (SIC), the proposed joint DAD-CE-DD algorithm is performed to further improve performance by utilizing the belief of each other. Additionally, based on the energy detection (ED) and sliding window protocol (SWP), we develop a collision resolution protocol to handle the codeword collision, a common issue in the URA system. In addition to the complexity reduction, the proposed algorithm exhibits a substantial performance enhancement compared to the state-of-the-art in terms of efficiency and accuracy.

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

Lower earth orbit (LEO) satellites play an important role in the integration of space and terrestrial communication networks, which typically encounter high-mobility scenarios. It has been shown that orthogonal time frequency space (OTFS) modulation performs well in such high-mobility scenarios by transforming the time-varying channels into the delay-Doppler domain. In this paper, we develop a joint channel estimation and data detection algorithm for OTFS-based LEO satellite communications. Firstly, we adopt the powerful variational Bayesian inference (VBI) method for estimating the delay-Doppler channel vector, which contains the channel gain, the delay and the Doppler. Secondly, we exploit the unknown data symbols in an OTFS frame as ‘virtual pilots’ for improving the accuracy of channel estimation and detect them simultaneously. Our simulation results demonstrate that the proposed algorithm achieves improved channel estimation mean square error and bit error rate performance than its conventional counterparts.

The hybrid nonlinear transceiver optimization problem of reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MU-MIMO) downlink is investigated. Specifically, the Tomlinson-Harashima precoder (THP) and the hybrid transmit precoder (TPC) of the base station are jointly optimized with the linear digital receivers of mobile users. The triangular feedback matrix of the THP is optimized and the optimal solution is derived in closed form based on a matrix inequality. Moreover, in order to tackle the nonconvexity of the constant-modulus constraints imposed on the analog TPC, the Majorization-Minimization (MM) based reconfigurable optimization framework is proposed, which strikes a trade-off between the implementation complexity and system performance in a reconfigurable manner. Explicitly, our MM-based reconfigurable optimization framework is capable of optimizing the analog TPC in a dynamically reconfigurable manner on an element-by-element, column-by-column, row-by-row or block-by-block basis. Moreover, an MM-based reconfigurable algorithm is proposed for the optimization of the phase shifting matrix at RIS, which also suffers from constant-modulus constraints. In the proposed MM-based reconfigurable algorithm, the RIS can be partitioned into a series of subarrays for striking different performance vs. complexity tradeoffs. Finally, our numerical results demonstrate the performance advantages of the proposed nonlinear hybrid transceiver optimization techniques.

This paper investigates the unsourced random access (URA) scheme to accommodate numerous machine-type users communicating to a base station equipped with multiple antennas. Existing works adopt a slotted transmission strategy to reduce system complexity; they operate under the framework of coupled compressed sensing (CCS) which concatenates an outer tree code to an inner compressed sensing code for slot-wise message stitching. We suggest that by exploiting the MIMO channel information in the angular domain, redundancies required by the tree encoder/decoder in CCS can be removed to improve spectral efficiency, thereby an uncoupled transmission protocol is devised. To perform activity detection and channel estimation, we propose an expectation-maximization-aided generalized approximate message passing algorithm with a Markov random field support structure, which captures the inherent clustered sparsity structure of the angular domain channel. Then, message reconstruction in the form of a clustering decoder is performed by recognizing slot-distributed channels of each active user based on similarity. We put forward the slot-balanced$ K $-means algorithm as the kernel of the clustering decoder, resolving constraints and collisions specific to the application scene. Extensive simulations reveal that the proposed scheme achieves a better error performance at high spectral efficiency compared to the CCS-based URA schemes.

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems.

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting.

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.

Hybrid energy supply composed of batteries and radio frequency (RF) signals has been anticipated to be a prominent solution for balancing the reliability and self-sustainability of future IoT networks. The newly emerging reconfigurable intelligent surface (RIS) is also capable of greatly enhancing spectral and energy efficiencies. In this paper, by considering an asynchronous transmission protocol among all energy receivers (ERs) and assuming the perfect self-interference cancellation (SIC) at the hybrid access point (HAP), we aim to maximize sum throughput in the RIS-aided hybrid powered communication networks (HPCNs) by jointly optimizing the transmit covariance matrices of the HAP and all ERs, the RIS reflection matrix and the downlink/uplink (DL/UL) time allocation. Generally, this optimization problem is intractable to solve due to strongly coupled variables and nonconvex unit-modulus constraints. To draw more insights into this joint design, we firstly carry out feasibility analysis on this problem, and then develop a 2-block alternating optimization algorithm, which consists of the semi-closed-form solution based iterative algorithm for deriving the optimal MIMO transceivers together with the DL/UL time allocation and the alternating direction method of multipliers (ADMM) based algorithm for the RIS design. To avoid the potential high complexity of alternating optimization, we also propose a two-stage scheme, where the RIS design is independent of the others and aims to create favorable DL/UL channels. The extension of our proposed algorithms to the practical imperfect SIC case is then discussed. Numerical results illustrate the superior performance of our proposed algorithms over the baselines in terms of the achievable sum throughput, and their time effectiveness in solving large-scale problems.

Wireless powered sensor networks (WPSNs) have evolved as a promising paradigm for energy-efficient communications. Recently, the proliferation of reconfigurable intelligent surface (RIS) has further been envisioned as a cost-effective solution for improving wireless power transfer (WPT) efficiency. In this paper, from the practical perspective of balancing the network sustainability and reliability, we consider a RIS-enhanced partially WPSN that composed of wireless-powered energy receivers (ERs) and battery-powered information receivers (IRs). Assuming the partially WPSN operates in time division multiple access (TDMA) mode, the joint optimization of covariance matrices, downlink/uplink (DL/UL) time allocation and RIS reflecting coefficients are investigated under the minimum DL rate constraint among all IRs for maximizing the achievable UL sum rate. Specifically, the single-IR single-ER (SISE) case is first studied based on the assumption of separate DL/UL RIS reflecting coefficients, in which an alternating optimization algorithm is proposed with semi-closed-form optimal solutions. In order to reduce the hardware overhead and signal processing complexity, we also investigate the case of identical DL/UL RIS reflecting coefficients, in which an iterative optimization algorithm is developed to tackle the coupled DL/UL transmissions. Then, we extend our work to the multiple-IRs multiple-ERs (MIME) case, where both the optimization problems corresponding to separate and identical DL/UL RIS reflecting schemes become more challenging to solve. To circumvent this intractability, we propose a successive convex relaxation (SCA) based alternating optimization algorithm and a low-complexity two-step algorithm. Finally, numerical results demonstrate the superior UL sum rate performance of our proposed algorithms over the adopted benchmarks.

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

The large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO result in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain. For a phased array based hybrid transceiver, beam squint makes the accurate beamforming an enormous challenge since an analog beamformer/combiner cannot generate frequency-dependent phase shift constitutionally. In this paper, we propose a virtual sub-array based wideband hybrid beamforming approach to eliminate the impact of beam squint. By dividing the whole array into several virtual sub-arrays, a wider beam is generated and provides an evenly distributed array gain across the whole operating frequency band. Analytical and numerical results demonstrate the effectiveness of the proposed wideband beamforming approach.

Existing deep learning (DL) based downlink channel prediction algorithms for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems mainly utilize single-source sensing information, e.g., the uplink channels, to predict the downlink channels. With the aid of multi-source sensing information (MSI) in communication systems, this paper explores deep multimodal learning (DML) technologies to improve the accuracy of downlink channel prediction. By leveraging various modality combinations and fusion levels, we design several DML based architectures for downlink channel prediction, which can also be easily extended to other communication problems like beam prediction. Simulation results demonstrate that the proposed DML based architectures can effectively exploit the constructive and complementary information of multimodal sensory data, thus achieving better performance than existing works.

Intelligent reflecting surface (IRS) is capable of constructing the favorable wireless propagation environment by leveraging massive low-cost reconfigurable reflect array elements. In this article, we investigate the IRS-aided multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) for Internet-of-Things (IoT) networks, where the active base station (BS) transmits beamforming and the passive IRS reflection coefficients are jointly optimized for maximizing the minimum signal-to-interference-plus-noise ratio (SINR) among all information decoders (IDs), while maintaining the minimum total harvested energy at all energy receivers (ERs). Moreover, the IRS with practical discrete phase shifts is considered, and thereby the max-min SINR problem becomes an NP-hard combinatorial optimization problem with a strong coupling among optimization variables. To explore the insights and generality of this max-min design, both the single-ID single-ER (SISE) scenario and the multiple-IDs multiple-ERs (MIME) scenario are studied. In the SISE scenario, the classical combinatorial optimization techniques, namely, the special ordered set of type 1 (SOS1) and the reformulation-linearization (RL) technique, are applied to overcome the difficulty of this max-min design imposed by discrete optimization variables. Then, the optimal branch-and-bound algorithm and suboptimal alternating optimization algorithm are, respectively, proposed. We further extend the idea of alternating optimization to the MIME scenario. Moreover, to reduce the iteration complexity, a two-stage scheme is considered aiming to separately optimize the BS transmit beamforming and the IRS reflection coefficients. Finally, numerical simulations demonstrate the superior performance of the proposed algorithms over the benchmarks in both the two scenarios.

The combination of large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO results in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain, which will cause severe performance degradation if not properly treated. In particular, for a phased array based hybrid transceiver, there exists a contradiction between the requirement of mitigating the beam squint issue and the hardware implementation of the analog beamformer/combiner, which makes the accurate beamforming an enormous challenge. In this paper, we propose two wideband hybrid beamforming approaches, based on the virtual sub-array and the true-time-delay (TTD) lines, respectively, to eliminate the impact of beam squint. The former one divides the whole array into several virtual sub-arrays to generate a wider beam and provides an evenly distributed array gain across the whole operating frequency band. To further enhance the beamforming performance and thoroughly address the aforementioned contradiction, the latter one introduces the TTD lines and propose a new hardware implementation of analog beamformer/combiner. This TTD-aided hybrid implementation enables the wideband beamforming and achieves the nearoptimal performance close to full-digital transceivers. Analytical and numerical results demonstrate the effectiveness of two proposed wideband beamforming approaches.

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory,Theorem 1presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically,Theorem 1fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applyingTheorem 1to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure inTheorem 1and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

Covert communication can hide the information transmission process from the warden to prevent adversarial eavesdropping. However, it becomes challenging when the location of warden is uncertain. In this paper, we propose a covert communication scheme against a warden with uncertain locations, which maximizes the connectivity throughput between a multi-antenna transmitter and a full-duplex jamming receiver with the limit of covert outage probability (the probability of the transmission found by the warden). First, we analyze the monotonicity of the covert outage probability to obtain the optimal location for the warden. Then, under this worst situation, we optimize the transmission rate, the transmit power and the jamming power of covert communication to maximize the connection throughput. This problem is solved in two stages. First, we derive the transmit-to-jamming power ratio limit from the maximum allowed covert outage probability. With this constraint, the connection probability is maximized over the transmit-to-jamming power ratio for a fixed transmission rate. Since the connection probability and the transmission rate are coupled, the bisection method is applied to maximize the connectivity throughput via optimizing the transmission rate iteratively. Simulation results are presented to evaluate the effectiveness of the proposed scheme.

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is nonconvex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numerical results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.

In this paper, we propose a superimposed training based two-phase robust channel estimation scheme for the unmanned aerial vehicle (UAV) assisted cellular communication system, in which various unitarily-invariant channel statistics errors are considered. Specifically, in the first phase, mobile station (MS) estimates the UAV-MS channel via the UAV training sequence, of which the robust design can be solved based on convex-concave theory. While in the second phase, the superimposed training scheme is considered at the ground base station (GBS) to improve spectrum efficiency. Then the robust GBS training sequence, the information signal power and the UAV amplifying factor are jointly optimized for the partially cascaded GBS-UAV-MS channel estimation subject to GBS and UAV transmit power constraints as well as the required information signal strength at the MS. To tackle this NP-hard problem, the optimal structures of involved variables are firstly derived, based on which the robust superimposed training design is simplified and proved to be quasi-convex in the UAV amplifying factor. Particularly, for Spectral norm and Nuclear norm bounded errors, the optimal training sequence can be obtained via convex-concave theory and Golden section searchWhile for Frobenius norm bounded error, a tractable upper-bounding scheme is proposed for the robust superimposed training design. Furthermore, we extend our work into the more general probabilistic path loss scenario of UAV-ground channels, and analyze the impacts of the probabilistic path loss and RicianK-factor on channel estimation performance. Numerical results illustrate the excellent performance of the proposed superimposed training based two-phase channel estimation scheme.

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

We investigate the energy efficiency (EE) of multiple-input-multiple-output (MIMO) amplify-and-forward relaying networks relying on the realistic imperfect channel state information (CSI). Specifically, the relay jointly optimizes the source covariance and relay beamforming matrices by maximizing the EE under additive or multiplicative relay-destination CSI errors. The optimal channel-diagonalizing structure is derived for the source covariance and relay beamforming matrices under the spectral-norm constrained additive or multiplicative CSI error. Then, the existence of a saddle point is proved, which shows that the channel-diagonalizing transmission strategy is optimal in the robust EE maximization under these two types of CSI errors, and the original matrix-valued fractional robust EE problem is transformed into a scalar fractional problem. We propose the Dinkelbach method-based alternating optimization scheme for this transformed robust EE problem, which is capable of finding a locally optimal solution of the original robust EE problem efficiently, and show that the semi-closed-form solution to each of the two associated subproblems can be obtained. We then prove that the channel-diagonalizing transmission strategy remains optimal when the statistically imperfect source-relay channel is additionally imposed. We also extend our work into multi-hop MIMO relaying scenarios and prove that the channel-diagonalizing structure is optimal for the source covariance matrix and the multiple relay beamforming matrices.

The device-to-device (D2D) communications mode has been regarded as an effective technique for solving/relieving the contradiction between the exponentially increased data traffic requirements of mobile customers and the essence of scarcity of radio resources in wireless communication networks. In the presence of unfavorable direct communication links between D2D peers, cooperative relays may play an important role in enhancing both reliability and flexibility of D2D communications. Aiming at maximizing the sum throughout of the network with a low computation complexity, a new scheme, which jointly considers a number of critical aspects, such as power control, interference limit based on the location information, optimal relay selection based on delineated area, and optimal link selection based range division, etc., is proposed in this paper. Numerical results reveal that the proposed scheme is capable of substantially improving the system's performance compared with either the existing brute-force technique or the area-division scheme. Furthermore, the proposed scheme also exhibits its advantages over the existing techniques in terms of computational complexity.

Deploying sub-6-GHz network together with millimeter wave (mm-wave) is a promising solution to simultaneously achieve sufficient coverage and high data rate. In heterogeneous networks, the traditional coupled access, i.e., users are constrained to be associated with the same base station in both downlink and uplink, is no longer optimal, and the concept of downlink and uplink decoupling (DUDe) has recently been proposed. In this paper, we analyze the coverage probability and area throughput for both the downlink and uplink of sub-6-GHz/mm-wave cellular networks with decoupled access. Compared with the existing works, we take uplink power control and mm-wave interference into account. Using the tools from stochastic geometry, the expressions of signal-to-interference-plus-noise ratio coverage probability, user-perceived rate coverage probability and the area throughput are derived. The impact of decoupled access and different small cells (SCells) is investigated. In particular, analytical results reveal that with decoupled access, UEs are more likely to be associated with SCells in uplink when the network is sparse, and the uplink traffic will be offloaded from sub-6-GHz SCells to mm-wave SCells when the network is dense. Moreover, the dense deployment of mm-wave SCells rather than sub-6-GHz SCells is more reasonable, and the DUDe is a key factor in improving the performance of dense cellular networks with multi-band.

The rapid development of mobile communications requires even higher spectral efficiency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Among several NOMA schemes, it has been shown that sparse code multiple access (SCMA) is able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and the Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

In this paper, we investigate the linear transceiver optimization for multiple-inputmultiple-output (MIMO) interference networks, where multiple pairs of multi-antenna source and destination nodes communicate simultaneously. Different from most of existing works, we jointly consider three critical issues of the linear transceiver optimization for MIMO interference networks based on multi-objective optimization theory, i.e., signal transmission, energy and security. Specifically, using the modified weighted Tchebycheff method, we investigate three kinds of multi-objective optimization problems (MOOPs): 1) sum mean square error minimization and harvested energy maximization; 2) transmit power minimization and energy harvesting efficiency maximization; 3) transmit power minimization, energy harvesting efficiency maximization, and physical layer security. Based on the Charnes-Cooper transformation and penalty function method, the formulated MOOPs are transformed into convex optimization problems and thus can be effectively solved. The resulting Pareto optimal solutions set reveals the complicated but important relationships among these involved single objective optimization problems, which are usually individually investigated in the literature. Finally, numerical simulation results demonstrate the performance advantages of the proposed algorithm and corroborate the theoretical analysis.

We design novel transmission strategies to maximize the energy efficiency (EE) of the uplink multi-user multipleinput and multiple-output relay channel. In this channel, K multi-antenna users communicate with a multi-antenna base station (BS) through a multi-antenna relay. To achieve the goal of EE maximization, we propose new iterative algorithms to jointly optimize the multi-user precoder and the relay precoder under transmit power constraints for two cases. In the first case, the perfect global channel state information (CSI) is available, while in the second case, the CSI between the relay and the BS is imperfect. To surmount the non-convexity of our formulated EE optimization problems in both cases, we introduce the parameter subtractive function into the proposed algorithms. Then, the EE parameter in the parameter subtractive function is updated by Dinkelbach's algorithm in the perfect CSI case, and by the bisection method in the imperfect CSI case. Moreover, in the perfect CSI case, the relay precoder is optimized by the diagonalization operation and the multi-user precoder is optimized based on the weighted minimum mean square error method. Differently, in the imperfect CSI case, we apply the sign-definiteness lemma to promote the semidefinite programming formulation of the EE optimization problem. Furthermore, we present the numerical results to demonstrate that our proposed iterative algorithms have a good convergence rate in both cases. In addition, we show that our proposed iterative algorithms achieve a higher EE performance than the existing algorithms in both CSI cases.

Owing to the wireless signal power received by the energy harvesting (EH) node is generally higher than that of the information decoding (ID) node in simultaneous wireless information and power transfer (SWIPT) system, the confidential information becomes vulnerable to be wiretapped. Motivated, in this paper, we aim at realizing secure communications for two-user MIMO interference channel with SWIPT. Unfortunately, the formulated secrecy rate maximization problem is non-convex with respect to the covariance matrices of two transmitters, thus an alternative algorithm is proposed to solve the nonconvex optimization problem. Firstly, the orthogonal-projection-based optimization algorithm is performed to completely suppress the interference to ID receiver, then by applying the Taylor series expansion, the covariance matrix of information transmitter is optimized based on the dual optimization. Finally, numerical experiments are conducted to validate the security performance of the proposed algorithm.

When energy harvesting (EH) technique is applied in Internet of Things (IoT) to replenish energy for low power consumption sensing nodes, e.g., sensors and radio frequency identification (RFID) tags, the end-to-end (e2e) data rate is usually maximized without accounting for the energy consumption at the nodes. In this paper, however, the energy consumption at secondary users (SUs) along a cognitive relaying link is characterized by means of energy efficiency, defined as the achievable data rate per Joule. In particular, the energy states at each node is modelled as a finite-state Markov chain and the transmit power at a node is optimally allocated by jointly accounting for the interference threshold prescribed by primary users (PUs), the maximum allowable transmit power and the harvested energy at the node. To maximize the energy efficiency, a best relay selection criterion is proposed and the subsequent optimal transmit power allocation is initially formulated as a nonlinear fractional programming problem and, then, equivalently transformed into a parametric programming problem and, finally, solved analytically by using the classic Karush-Kuhn-Tucker (KKT) conditions. With extensive Monte-Carlo simulation results, the effectiveness of the proposed relay selection algorithm and corresponding optimal power allocation strategy are corroborated, in terms of the energy efficiency of SUs.

Thanks to gigahertz unlicensed spectrum, the millimeter wave (mmWave) communication becomes an important enabling technology to meet the increasing data rate demands of future communication systems. It is also well-known that wireless communications are susceptible to security threatening, especially for wireless two-way relaying networks. Hence, in this work, we propose two secrecy beamforming schemes for large mmWave two-way relaying networks. Firstly, the secrecy rate maximization problem is considered with the constraint of relay power. Then the relay transmit power is optimized to satisfy the secrecy rate requirement of network. Owing to the nonconvexity of original optimization problem, the null-space beamforming is utilized to transform both problems into the standard SOCP problem, which can be solved effectively with the convex optimization technique. Finally, numerical experiments are conducted to show the superior security performance and high energy efficiency of the two proposed secrecy beamforming schemes, respectively.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs and co-channel interference caused by resource sharing becomes a significant challenge. We consider both the total energy efficiency (EE) and individual EE optimization problems, which are fractional programming and generalized fractional programming problems, respectively, and are hard to tackle due to their non-concave nature. We first transform them into equivalent optimization problems in parametric subtractive forms, which fit in a class of non-concave optimization methods known as difference of two concave functions programming, and then solve them using Dinkelbach and branch-and-bound methods to give global optimal solutions. Due to the unaffordable complexity of the global optimal solution, we further propose sub-optimal schemes through adding constraints on the interferences to convert the non-concave problems into concave ones and to give sub-optimal solutions with reasonable complexity. The sub-optimal solution gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Small cell networks have been recognized as a promising technology to realize high spectrum and energy efficiency communications in future wireless networks. However, the capacity of small cell networks is limited by the interference among the links communicating simultaneously. Efficient resource allocation and effective interference management is definitely imperative for small cells. In this paper, we propose an algorithm to optimize the power and subcarrier allocation jointly in order to maximize the weighted sum rate for dense small cell networks. Facing with a large amount of small cells, the optimization problem is in nature a large scale optimization problem. Using advanced decomposition theory, the proposed algorithm can effectively decompose the considered optimization problem into a series of much simpler subproblems which can be efficiently solved in parallel. Finally, simulation results demonstrate that the proposed algorithm enjoys greater performance gain and faster convergence as compared to the existing schemes.

In this paper, we discuss MIMO transceiver design under a new type of power constraint named mixed power constraints. Mixed power constraint is referred to the power model in which for a given set of antennas, several subsets are constrained by sum power constraints while the other antennas are subject to individual power constraints. It includes sum power constraint and per-antenna power constraints as its special cases and it can strike a balance between complexity and performance. In our work, we try to solve the optimization problem in an analytical way instead of relying on some famous software packages e.g., CVX or SeDuMi. Firstly, the specific formula of the optimal signal covariance matrix has been derived. Based on the structure, a low complexity non-iterative solution is given in our work, which can be interpreted as a matrix version water-filling solution. This solution has a much clear engineering meaning and is suitable for practical implementations even for massive MIMO systems. Finally, simulation results demonstrate the accuracy of our theoretical results.

We analyze the achievable rate of the uplink of a single-cell multi-user distributed massive multiple-input-multiple-output (MIMO) system. Each user is equipped with single antenna and the base station (BS) is equipped with a large number of distributed antennas. We derive an analytical expression for the asymptotic ergodic achievable rate of the system under zero-forcing (ZF) detector. In particular, we consider circular antenna array, where the distributed BS antennas are located evenly on a circle, and derive an analytical expression and closed-form bounds for the achievable rate of an arbitrarily located user. Subsequently, closed-form bounds on the average achievable rate per user are obtained under the assumption that the users are uniformly located. Based on the bounds, we can understand the behavior of the system rate with respect to different parameters and find the optimal location of the circular BS antenna array that maximizes the average rate. Numerical results are provided to assess our analytical results and examine the impact of the number and the location of the BS antennas, the transmit power, and the path-loss exponent on system performance. Simulations on multi-cell networks are also demonstrated. Our work shows that circularly distributed massive MIMO system largely outperforms centralized massive MIMO system.

In this paper, we investigate space-time network coding (STNC) in cooperative multiple-input multiple-output networks, where U users communicate with a common destination D with the aid of R decode-and-forward relays. The transmit antenna selection with maximal-ratio combining (TAS/MRC) is adopted in user-destination and relay-destination links where a single transmit antenna that maximizes the instantaneous received signal-to-noise ratio is selected and fed back to transmitter by receiver and all the receive antennas are combined with MRC. In the presence of perfect feedback, we derive new exact and asymptotic closed-form expressions for the outage probability (OP) and the symbol error rate (SER) of STNC with TAS/MRC in independent but not necessarily identically distributed Rayleigh fading channels. We demonstrate that STNC with TAS/MRC guarantees full diversity order. To quantify the impact of delayed feedback, we further derive new exact and asymptotic OP and SER expressions in closed form. We prove that the delayed feedback degrades the full diversity order to (R + 1)ND, where ND is the antenna number of the destination D. Numerical and Monte Carlo simulation results are provided to demonstrate the accuracy of our theoretical analysis and evaluate the impact of network parameters on the performance of STNC with TAS/MRC.

In this paper, the performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) in a mobile ad hoc network (MANET) is analyzed. Based on the theory of stochastic geometry, both interference and the randomness in nodes' locations are incorporated into our analysis. The outage probability after the kth retransmission is analyzed and the outage performance of HARQ-IR is compared with that of Type-I HARQ and HARQ with chase combining (HARQ-CC). Our analysis reveals that the performance gains of HARQ-IR over Type-I HARQ and HARQ-CC increase with the number of retransmissions while they decrease with the path loss exponent. The network throughput in terms of transmission capacity is also analyzed. The results indicate that a certain small number of retransmissions is sufficient to achieve the maximal transmission capacity. Meanwhile, the optimal intensity of source nodes to maximize the transmission capacity is shown to be within a specific interval.

Cloud radio access network (C-RAN) is a new concept of network architecture, which brings a technical revolution into the wireless communication market and leads to some kind of all new mode of the future wireless communications. In this paper the clustering algorithm based on multi-objective optimization is investigated. The proposed algorithm aims at maximizing the throughput contribution of the Remote RF Head (RRH) to the whole system and minimizing its total power consumption with guaranteed energy efficiency of RRH. Using the novel greedy dynamic clustering algorithm, the joint capacity of RRHs is improved. The throughput of each RRH is first given using the pricing mechanism and the Pascoletti and Serafini Scalarization method is then implemented to solve the multiobjective optimization problem. Finally, the performance of the algorithm is assessed by the simulation results. It is shown that the novel dynamic clustering algorithm based on multiobjective optimization in the C-RAN architecture outperforms the traditional greedy clustering approach.

In this paper, we focus on a new small cell discovery signal which improves the detection probability in ultra-dense small cell scenario. Due to the severe inter-cell interference, the detection performance of primary synchronization signal (PSS) and secondary synchronization signal (SSS) is deteriorated in future ultra-dense small cell scenario in the 3rd Generation Partnership Project (3GPP) long term evolution (LTE) system. Thus the current supported 504 cell IDs may not be sufficient. To mitigate the impact of such inter-cell interference problems and improve the cell search performance with increasing number of detect target cells, we propose a novel defined auxiliary secondary synchronization signal (A-SSS) in cell search procedure. Our simulation results show that the detection probability of cell search can be improved effectively with the proposed method.

In this paper, the low information-exchange and robust precoding design for distributed antenna systems is investigated, which is of great importance for the new mobile network architecture namely cloud radio access networks (C-RAN). Relying on an interesting low complexity decomposition algorithm, a distributed robust linear minimum mean-square-error (LMMSE) precoding algorithm is proposed. Exploiting the elegant properties of the decomposition algorithm, the precoder design problem can be decomposed into several subproblems. In addition, all the subproblems can be performed in parallel term. Moreover for the proposed algorithm there is no need of an extra step to calculate the Lagrange multipliers at each iteration. Thus the exchanging information of the algorithm can be significantly reduced. Finally, it is demonstrated by the simulation that the proposed algorithm enjoys both satisfying convergence properties and better performance.

In this paper, the linear minimum mean square error beamformers are designed for relay-assisted cloud radio access network (C-RAN). In C-RAN, the remote radio units are separated from the baseband units to save energy cost. To further enhance network coverage, it is a good choice to duly arrange relay nodes. Regrading the per-antenna power constraints at both the relay nodes and the remote radio heads (RRHs), the beamformer matrices at the relay nodes and RRHs are jointly optimized for the relay assisted C-RAN. Since the considered problem is a non-convex optimization problem and is with multiple variables, it is in general very hard to solve. To make the design suitable for C-RAN exploiting the problem structure, two novel decomposition algorithms are proposed. One algorithm is mainly carried out at the RRHs, another is mainly performed at the relay node. Finally in the simulations, the performance of the proposed algorithms are demonstrated.

In this paper, the authors investigate the distributed power allocation for the multicell orthogonal frequency division multiple access networks by taking both the energy efficiency and the intercell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how the ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from the existing works, a biobjective problem is formulated based on the multiobjective optimisation theory, which aims at maximising the throughput contribution of the BS to the network and minimising its total power consumption at the same time. By using the method of the Pascoletti and Serafini scalarisation, the relationship between the varying parameters and the minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices in the power allocation schemes in terms of both the energy consumption and the throughput. Finally, the performance of the algorithm is assessed by the simulation results.

In this study, the authors investigate the linear minimum‐mean‐square‐error beamforming design for relay‐assisted cloud radio access network (C‐RAN). A standard C‐RAN architecture separates baseband processing units and wireless radio units in order to save energy cost. To further enhance network coverage, several relay nodes (RNs) are also deployed. Regrading the per‐antenna power constraints at both of the remote radio heads (RRHs) and the RNs in the author's work the beamforming matrices at the RRHs and RNs are ‘jointly’ optimised for the considered relay assisted C‐RAN. The considered optimisation problem is a non‐convex and multiple variable optimisation problem which is in general very hard to solve. In order to make the design suitable for large scale networks exploiting to the problem structure a novel two stage decomposition algorithms are proposed. Finally, a detailed mean‐square‐error performance comparison is given by the simulations.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

In this paper, outage performance of hybrid automatic repeat request (HARQ) technique in interference dominant mobile ad hoc networks (MANETs) is analyzed. Unlike prior analysis, interference and spatial randomness of the nodes (i.e. the randomness in the number of nodes and nodes' locations) are considered. Based on the theory of point processes, the outage probabilities of two popular HARQ techniques, that are type-I HARQ and HARQ with chase combining (HARQ-CC), are derived in closed forms. The outage performance gain of HARQ-CC over type-I HARQ is also discussed and is found to follow the scaling law of Θ (k2(k+1)/α) where α is the path loss exponent and k is the number of retransmissions. It is also demonstrated that both type-I HARQ and HARQ-CC can significantly improve the communication performance even in interference dominant MANETs. Furthermore, it is revealed that in most cases HARQ-CC is superior over type-I HARQ, however, for a dense network type-I HARQ can provide comparable performance with lower complexity than HARQ-CC and is thus more preferable.

In this paper, we investigate the performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) relaying system with orthogonal space-time block code (OSTBC) transmissions over correlated Nakagami-m fading channels. For maximal ratio combining (MRC) receiver at the destination, we provide the compact closed-form expressions for cumulative distribution function (CDF) and probability density function (PDF) of the instantaneous end-to-end signal-to-noise ratio (SNR). In addition, the exact analytical expressions are also derived for the outage probability (OP) and symbol error rate (SER) relying on CDF. Furthermore, we present the asymptotic expressions for OP and SER in the high SNR regime, from which we gain an insight into the system performance and derive the achievable diversity order and array gain. The analytical expressions are validated through Monte-Carlo simulations.

In this paper, the uplink coverage probability for heterogeneous cellular systems with range expansion is analyzed and derived in closed-form. Unlike most of the previous analyses of heterogeneous systems, the randomness of not only the number of mobile users but also their locations is taken into account in the analysis based on the theory of stochastic geometry. With the derived analytical results, the impacts of various system parameters on the uplink performance are investigated in detail. The correctness of the analytical results is also verified by simulations. These analytical results can thus serve as a guidance for the design of the heterogeneous systems.

In this paper, we investigate the joint power allocation for dual-hop amplify-and-forward (AF) MIMO-OFDM cognitive radio (CR) networks. The considered AF MIMO-OFDM CR network coexists with a primary radio (PR) network through underlay spectrum sharing. In order to mitigate the interference to the PR network, environmental learning algorithm is adopted to blindly estimate the null space of the PR user, which are orthogonal to the PR communication channels. With necessary channel state information, under independent transmit power constraints as well as the interference constraints, the power allocation of CR source and relay and subcarrier pairing over two hops are optimized jointly to maximize the CR network throughput. Furthermore, the relay node implements an effective subcarrier permutation policy to enhance the performance further at the cost of affordable complexity. Finally, the performance advantages of the proposed algorithm are demonstrated by the simulation results.

In this study, a unified linear minimum mean‐square‐error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers, for example, the quadratic forms. It is also discovered that evolving from a point‐to‐point multiple‐input–multiple‐output (MIMO) system to various advanced wireless systems such as multi‐cell coordinated systems, multi‐user MIMO systems, MIMO cognitive radio systems, amplify‐and‐forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this study is likely to be the first shot for the transceiver design for the future ever‐changing wireless systems.

In this letter, we investigate multiple-input multiple-output (MIMO) communications under energy harvesting (EH) constraints. In our considered EH system, there is one information transmitting (ITx) node, one traditional information receiving (IRx) node and multiple EH nodes. EH nodes can transform the received electromagnetic waves into energy to enlarge the network operation life. When the ITx node sends signals to the destination, it should also optimize the beamforming/precoder matrix to charge the EH nodes efficiently simultaneously. Additionally, the charged energy should be larger than a predefined threshold. Under the EH constraints, in our work both minimum mean-square-error (MMSE) and mutual information are taken as the performance metrics for the beamforming designs at the ITx node. In order to make the proposed algorithms suitable for practical implementation and have affordable overhead, our work focuses on the beamforming designs with partial CSI and this is the distinct contribution of our work. Finally, numerical results are given to show the performance advantages of the proposed algorithms.

In this paper, the performance of hybrid automatic repeat request (HARQ) technique in an ad hoc network is analyzed. Unlike most prior works on the analysis of HARQ, both time-correlated interference and feedback errors are taken into account in the analysis. Based on the theory of point processes, outage probability after the nth retransmission, delay limited throughput and mean transmission time are derived in closed forms. The analytical results are verified by simulations and the impacts of various parameters on the network performance are investigated in detail. It is found that the outage probability obeys the inverse-2/α power law over the number of retransmissions, where α is the path loss exponent. Furthermore, it is demonstrated that the feedback error significantly degrades the delay limited throughput when the ad hoc network becomes dense, while the increment of mean transmission time due to the feedback error is a concave function of the intensity of the network.

In this paper, we consider robust distributed filter-and-forward beamforming design for two-way relaying networks over the frequency selective fading channels, in which two users exchange information with the aid of multiple single-antenna relay nodes. In contrast to the prior works in which the channel state information (CSI) is available, in our work CSI for all relevant links between the users and relay nodes is not perfectly known with stochastic channel errors. Under stochastic channel errors, a robust beamforming design aiming at maximizing the total system signal-to-interference-plus-noise-ratio (SINR) under individual relay power constraints is proposed. Simulation results show that the proposed robust beamformer reduces the sensitivity of the two-way relaying systems to channel estimation errors, and performs better than the algorithm using estimated channels only.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

In this paper, we consider the robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems. THP is adopted at the source to mitigate the spatial inter-symbol interference and then a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. Based on the elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the optimization problem is greatly simplified and can be efficiently solved. Finally, the performance advantage of the proposed robust design is assessed by simulation results.

In this paper, robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems is investigated. At source node, THP is adopted to mitigate the spatial intersymbol interference. However, due to its nonlinear nature, THP is very sensitive to channel estimation errors. In order to reduce the effects of channel estimation errors, a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. With novel applications of elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the transceiver design problem reduces to a much simpler one with only scalar variables which can be efficiently solved. Finally, the performance advantage of the proposed robust design over non-robust design is demonstrated by simulation results.

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results.

In this paper, linear beamforming design for uplink amplify-and-forward relaying cellular networks, in which multiple mobile terminals rely on one relay station to communicate with the base station, is investigated. In particular, the base station, relay station and mobile terminals are all equipped with multiple antennas. Based on linear minimum mean-square-error (LMMSE) criterion and exploiting a hidden convexity in the problem, the precoder matrices at multiple mobile terminals, forwarding matrix at relay station and equalizer matrix at base station are jointly designed. Furthermore, several existing linear beamforming designs for multi-user (MU) MIMO systems and AF MIMO relaying systems can be considered as special cases of the proposed solution. Simulation results are presented to demonstrate the performance advantage of the proposed algorithm.

In this paper, we focus on a two-hop amplify-and-forward (AF) cooperative network, in which one source node equipped with multiple antennas communicates with a single-antenna destination, relying on a single-antenna relay. In order to implement beamforming, channel state information (CSI) is feeded back to the source from relay using random vector quantization (RVQ) codebook. Furthermore, one relay with the best CSI is selected in each communication. An upper bound of the outage probability of the whole system is derived and the simulation results show that this bound is very tight.

Relay transmission is promising for future wireless systems due to its significant cooperative diversity gain. The performance of dual-hop semi-blind amplify-and-forward (AF) relaying systems was extensively investigated, for transmissions over Rayleigh fading channels or Nakagami-m fading channels with integer fading parameter. For the general Nakagami-m fading with arbitrary m values, the exact closed-form system performance analysis is more challenging. In this paper, we explicitly derive the moment generation function (MGF), probability density function (PDF) and moments of the end-to-end signal-to-noise ratio (SNR) over arbitrary Nakagami-m fading channels with semi-blind AF relay. With these results, the system performance evaluation in terms of outage probability, average symbol error probability, ergodic capacity and diversity order, is conducted. The analysis developed in this paper applies to any semi-blind AF relaying systems with fixed relay gain, and two major strategies for computing the relay gain are compared in terms of system performance. All analytical results are corroborated by simulation results and they are shown to be efficient tools to evaluate system performance.

In this paper, robust transceiver design based on minimum-mean-square-error (MMSE) criterion for dual-hop amplify-and-forward MIMO relay systems is investigated. The channel estimation errors are modeled as Gaussian random variables, and then the effect are incorporated into the robust transceiver based on the Bayesian framework. An iterative algorithm is proposed to jointly design the precoder at the source, the forward matrix at the relay and the equalizer at the destination, and the joint design problem can be efficiently solved by quadratic matrix programming (QMP).

In this paper, robust joint design of linear relay precoders and destination equalizers for amplify-and-forward (AF) MIMO relay systems under Gaussian channel uncertainties is investigated. After incorporating the channel uncertainties into the robust design based on the Bayesian framework, a closedform solution is derived to minimize the mean-square-error (MSE) of the received signal at the destination. The effectiveness of the proposed robust transceiver is verified by simulations.

In this paper, we address the robust linear transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems, where both transmitters and receivers have imperfect channel state information (CSI). With the statistics of channel estimation errors in the two hops being Gaussian, we formulate the robust linear-minimum-mean-square-error (LMMSE) transceiver design problem using the Bayesian framework, and derive a closed-form solution. Simulation results show that the proposed algorithm reduces the sensitivity of the relay system to channel estimation errors, and performs better than the algorithm using estimated channel only.

This paper considers the problem of robust linear transceiver design for a dual-hop amplify-and-forward (AF) MIMO relay system, with Gaussian random channel uncertainties in both hops. By taking the channel uncertainties into account, an iterative algorithm is proposed to minimize the mean-square-error (MSE) of the output signal at the destination. Simulation results show that the proposed algorithm reduces the sensitivity of the AF MIMO relay systems to channel estimation errors, and performs better than the algorithm based on estimated channels only.