Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation (6G) wireless networks, unifying radar sensing and communication on a shared hardware platform. This paper proposes a dynamic array partitioning framework for monostatic ISAC systems to fully exploit available spatial degrees of freedom (DoFs) and reconfigurable antenna topologies, enhancing sensing performance in complex scenarios. We first establish a theoretical foundation for our work by deriving Bayesian Cramér-Rao bounds (BCRBs) under prior distribution constraints for heterogeneous target models, encompassing both point-like and extended targets. Building on this, we formulate a joint optimization framework for transmit beamforming and dynamic array partitioning to minimize the derived BCRBs for direction-of-arrival (DOA) estimation. The optimization problem incorporates practical constraints, including multi-user communication signal-to-interference-plus-noise ratio (SINR) requirements, transmit power budgets, and array partitioning feasibility conditions. To address the non-convexity of the problem, we develop an efficient alternating optimization algorithm combining the alternating direction method of multipliers (ADMM) with semi-definite relaxation (SDR). We also design novel maximum a posteriori (MAP) DOA estimation algorithms specifically adapted to the statistical characteristics of each target model. Extensive simulations illustrate the superiority of the proposed dynamic partitioning strategy over conventional fixed-array architectures across diverse system configurations.

Integrated sensing and communication (ISAC) can substantially improve spectral, hardware, and energy efficiency by unifying radar sensing and data communications. In wideband and scattering-rich environments, clutter often dominates weak target reflections and becomes a fundamental bottleneck for reliable sensing. Practical ISAC clutter includes “cold” clutter arising from environmental backscatter of the probing waveform and “hot” clutter induced by external interference and reflections from the environment whose statistics can vary rapidly over time. In this article, we develop a unified wideband multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signal model that captures both clutter types across the space, time, and frequency domains. Building on this model, we review clutter characterization at multiple levels, including amplitude statistics, robust spherically invariant random vector (SIRV) modeling, and structured covariance representations suitable for limited-snapshot regimes. We then summarize receiver-side suppression methods in the temporal and spatial domains, together with extensions to space–time adaptive processing (STAP) and space–frequency–time adaptive processing (SFTAP), and we provide guidance on selecting techniques under different waveform and interference conditions. To move beyond reactive suppression, we discuss clutter-aware transceiver co-design that couples beamforming and waveform optimization with practical communication quality-of-service (QoS) constraints to enable proactive clutter avoidance. We conclude with open challenges and research directions toward environment-adaptive and clutter-resilient ISAC for the next-generation networks.

Cell-free integrated sensing and communication (ISAC) systems have emerged as a promising paradigm for sixth-generation (6G) networks, enabling simultaneous high-rate data transmission and high-precision radar sensing through cooperative distributed access points (APs). Fully exploiting these capabilities requires a unified design that bridges system-level optimization with multi-target parameter estimation. This paper proposes an end-to-end graph learning approach to close this gap, modeling the entire cell-free ISAC network as a heterogeneous graph to jointly design the AP mode selection, user association, precoding, and echo signal processing for multi-target position and velocity estimation. In particular, we propose two novel heterogeneous graph learning frameworks: a dynamic graph learning framework and a lightweight mirror-based graph attention network (mirror-GAT) framework. The dynamic graph learning framework employs structural and temporal attention mechanisms integrated with a three-dimensional convolutional neural network (3D-CNN), enabling superior performance and robustness in cell-free ISAC environments. Conversely, the mirror-GAT framework significantly reduces computational complexity and signaling overhead through a bi-level iterative structure with shared adjacency. Simulation results validate that both proposed graph-learning-based frameworks achieve significant improvements in multi-target position and velocity estimation accuracy compared to conventional heuristic and optimization-based designs. Particularly, the mirror-GAT framework demonstrates substantial reductions in computational time and signaling overhead, underscoring its suitability for practical deployments.

Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal’s polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that jointly conducts polarimetric sensing and communications. We study the use of single-port polarization-reconfigurable antennas to adapt to channel depolarization effects, without the need for separate RF chains for each polarization. We address two core sensing tasks in IPSAC systems, target parameter estimation and target detection. For parameter estimation, we consider the problem of minimizing the mean-squared error (MSE) of the target depolarization parameter estimate, which is a critical task for various polarimetric radar applications such as rainfall forecasting, vegetation identification, and target classification. To address this nonconvex problem, we apply semi-definite relaxation (SDR) and majorization-minimization (MM) optimization techniques. Next, we consider a design that maximizes the target signal-to-interference-plus-noise ratio (SINR) leveraging prior knowledge of the target and clutter depolarization statistics to enhance the target detection performance. To tackle this problem, we modify the solution developed for mean square error (MSE) minimization subject to the same quality-of-service (QoS) constraints. Extensive simulations show that the proposed polarization reconfiguration method substantially improves the depolarization parameter MSE. Furthermore, the proposed method considerably boosts the target SINR due to polarization diversity, particularly in cluttered environments.

Reconfigurable antennas possess the capability to dynamically adjust their fundamental operating characteristics, thereby enhancing system adaptability and performance. To fully exploit this flexibility in modern wireless communication systems, this paper considers a novel tri-hybrid beamforming architecture, which seamlessly integrates pattern-reconfigurable antennas with both analog and digital beamforming. The proposed tri-hybrid architecture operates across three layers: (\textit{i}) a radiation beamformer in the electromagnetic (EM) domain for dynamic pattern alignment, (\textit{ii}) an analog beamformer in the radio-frequency (RF) domain for array gain enhancement, and (\textit{iii}) a digital beamformer in the baseband (BB) domain for multi-user interference mitigation. To establish a solid theoretical foundation, we first develop a comprehensive mathematical model for the tri-hybrid beamforming system and formulate the signal model for a multi-user multi-input single-output (MU-MISO) scenario. The optimization objective is to maximize the sum-rate while satisfying practical constraints. Given the challenges posed by high pilot overhead and computational complexity, we introduce an innovative tri-timescale beamforming framework, wherein the radiation beamformer is optimized over a long-timescale, the analog beamformer over a medium-timescale, and the digital beamformer over a short-timescale. This hierarchical strategy effectively balances performance and implementation feasibility. Simulation results validate the performance gains of the proposed tri-hybrid architecture and demonstrate that the tri-timescale design significantly reduces pilot overhead and computational complexity, highlighting its potential for future wireless communication systems.

This paper focuses on precoding design in multi-antenna systems with improper Gaussian interference (IGI), characterized by correlated real and imaginary parts. We first study block level precoding (BLP) and symbol level precoding (SLP) assuming the receivers apply a pre-whitening filter to decorrelate and normalize the IGI. We then shift to the scenario where the base station (BS) incorporates the IGI statistics in the SLP design, which allows the receivers to employ a standard detection algorithm without pre-whitenting. Finally we address the case where the channel and statistics of the IGI are unknown, and we formulate robust BLP and SLP designs that minimize the worst case performance in such settings. Interestingly, we show that for BLP, the worst-case IGI is in fact proper, while for SLP the worst case occurs when the interference signal is maximally improper, with fully correlated real and imaginary parts. Numerical results reveal the superior performance of SLP in terms of symbol error rate (SER) and energy efficiency (EE), especially for the case where there is uncertainty in the non-circularity of the jammer.

Cell-free integrated sensing and communication (ISAC) is a pivotal technology for next-generation wireless networks, where dynamic collaboration enables the full exploitation of spatial degrees of freedom (DoF) to enhance system performance. By leveraging multi-view observations and effective information sharing, cell-free ISAC systems facilitate high-precision target positioning through collaborative precoding and information fusion. To fully capitalize on the collaborative potential of cell-free ISAC, this paper introduces a novel dynamic heterogeneous graph learning framework for joint base-station (BS) mode selection, user association, transmit precoding and receive information fusion design. By incorporating structural and temporal attention mechanisms, we address the discrete, coupled decision challenges associated with BS mode selection and user association. Additionally, the graph node message-passing mechanism is employed to enable efficient precoding and processing design. Extensive simulation results demonstrate that, compared to non-collaborative ISAC systems with fixed association strategies, the proposed dynamic graph learning framework significantly improves target positioning accuracy while achieving a superior communication rate.

Orthogonal frequency-division multiplexing (OFDM) is widely considered a leading waveform candidate for integrated sensing and communication (ISAC) in 6G networks. However, the cyclic prefix (CP) used to mitigate multipath effects in communication systems also limits the maximum sensing range. Target echoes arriving beyond the CP length cause inter-symbol interference (ISI) and inter-carrier interference (ICI), which degrade the mainlobe level and raise sidelobe levels in the range-Doppler map (RDM). This paper presents a unified analytical framework to characterize the ISI and ICI caused by an insufficient CP length in multi-target scenarios. For the first time, we derive closed-form expressions for the second-order moments of the RDM under both matched filtering (MF) and reciprocal filtering (RF) processing with insufficient CP length. These expressions quantify the effects of CP length, symbol constellation, and inter-target interference (ITI) on the mainlobe and sidelobe levels. Based on these results, we further derive explicit formulas for the peak sidelobe level ratio (PSLR) and integrated sidelobe level ratio (ISLR) of the RDM, revealing a fundamental trade-off between noise amplification in RF and ITI in MF. Numerical results validate our theoretical derivations and illustrate the critical impact of insufficient CP length on sensing performance in OFDM-ISAC systems.

Reconfigurable antennas can dynamically adjust their operating characteristics, thereby enhancing adaptability and performance. To fully exploit this flexibility in modern wireless communication systems, this paper proposes a novel tri-hybrid beamforming architecture that integrates pattern-reconfigurable antennas with analog and digital beamforming. The proposed tri-hybrid architecture operates across three layers: a radiation beamformer in the electromagnetic (EM) domain for dynamic pattern alignment, an analog beamformer in the radio-frequency (RF) domain for array gain enhancement, and a digital beamformer in the baseband (BB) domain for multi-user interference mitigation. First, a comprehensive mathematical model of the tri-hybrid beamforming system is developed to provide a theoretical foundation. Then, an optimization problem is formulated to maximize the sum-rate under practical constraints, and then is solved using fractional programming (FP), a penalty-based, majorization–minimization (MM), and manifold optimization methods. Simulation results validate the performance gains achieved by the proposed tri-hybrid architecture and highlight its potential for future wireless communication systems.

Integrated sensing and communication (ISAC) technology has emerged as a key enabling technology in the sixth generation (6G) mobile communications. This paper investigates the potential of multipath exploitation for enhancing target detection in orthogonal frequency division multiplexing (OFDM)-based ISAC systems. The study aims to improve target detection performance by harnessing the diversity gain in the delay-Doppler domain. We propose a weighted generalized likelihood ratio test (GLRT) detector that effectively leverages the multi-path propagation between the base station (BS) and the target. To further enhance detection accuracy, a joint optimization framework is developed for subcarrier power allocation at the transmitter and weight coefficients of the GLRT detector. The objective is to maximize the probability of target detection while satisfying constraints on total transmit power and the communication receiver’s signal-to-noise ratio (SNR). An iterative algorithm based on the majorization-minimization (MM) method is employed to address the resulting non-convex optimization problem. Simulation results demonstrate the efficacy of the proposed algorithm and confirm the benefits of multipath exploitation for target detection in OFDM-ISAC systems under multipath-rich environments.

In this paper, we explore cooperative sensing and communication within cell-free integrated sensing and communication (ISAC) systems. Specifically, multiple transmit access points (APs) collaboratively serve multiple communication users while simultaneously illuminating a potential target, with a separate sensing AP dedicated to collecting echo signals for target detection. To improve the performance of identifying a moving target in the presence of strong interference originating from transmit APs, we employ the space-time adaptive processing (STAP) technique and jointly optimize the transmit/receive beamforming. Our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR), subject to constraints on the communication SINR and transmit power. An efficient algorithm is developed to solve the resulting non-convex optimization problem. Simulations demonstrate significant performance improvements in target detection and validate the advantages of the proposed joint STAP and beamforming design for cell-free ISAC systems.

Extremely large-scale antenna array (ELAA) is a key candidate technology for the sixth generation (6G) mobile networks. Nevertheless, using substantial numbers of antennas to transmit high-frequency signals in ELAA systems significantly exacerbates the near-field effect. Unfortunately, traditional hybrid beamforming schemes are highly vulnerable to ELAA near-field communications. To effectively mitigate severe near-field effect, we propose a novel dynamic hybrid beamforming architecture for ELAA systems, in which each antenna is either adaptively connected to one radio frequency (RF) chain for signal transmission or deactivated for power saving. For the case that instantaneous channel state information (CSI) is available during each channel coherence time, a real-time dynamic hybrid beamforming design is developed to maximize the achievable sum rate under the constraints of the constant modulus of phase-shifters (PSs), non-overlapping dynamic connection network and total transmit power. When instantaneous CSI cannot be easily obtained in real-time, we propose a two-timescale dynamic hybrid beamforming design, which optimizes analog beamformer in long-timescale and digital beamformer in short-timescale, with the goal of maximizing ergodic sum-rate under the same constraints. Simulation results demonstrate the advantages of the proposed dynamic hybrid beamforming architecture and the effectiveness of the developed algorithms for ELAA near-field communications.

Integrated Sensing and Communication (ISAC) has emerged as a key enabler for future wireless systems. The recently developed symbol-level precoding (SLP) technique holds significant potential for ISAC waveform design, as it leverages both temporal and spatial degrees of freedom (DoFs) to enhance multi-user communication and radar sensing capabilities. Concurrently, reconfigurable intelligent surfaces (RIS) offer additional controllable propagation paths, further amplifying interest in their application. However, previous studies have encountered substantial computational challenges due to the complexity of jointly designing SLP-based waveforms and RIS passive beamforming. In this paper, we propose a novel model-driven learning approach that jointly optimizes waveform and beamforming by unfolding the iterative alternative direction method of multipliers (ADMM) algorithm. Two joint design algorithms are developed for radar target detection and direction-of-arrival (DoA) estimation tasks in a cluttered RIS-ISAC system. While ensuring the communication quality-of-service (QoS) requirements, our objectives are: 1) to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for target detection, and 2) to minimize the Cramér-Rao bound (CRB) for DoA estimation. Simulation results verify that our proposed model-driven learning algorithms achieve satisfactory communication and sensing performance, while also offering a substantial reduction in computational complexity, as reflected by the average execution time.

In this paper, we consider an integrated sensing, communications and computation (ISCC) energy-constrained system, where multiple users with limited energy budgets execute local computing and concurrently offload data to a dual-functional radar communication base station (DFRC-BS), while the DFRC-BS conducts target detection through radar sensing at the same time. We propose an active reconfigurable intelligent surface (ARIS)-assisted ISCC scheme, denoted by ARIS-ISCC, in which the ARIS is employed to facilitate the data offloading from the users to DFRC-BS. To enhance the data collection capability across radar sensing, communication offloading and local computation of the proposed ARIS-ISCC scheme, a weighted total computation bits (WTCB) maximization problem is formulated constrained by the users’ energy limits, power budgets constraints for the DFRC-BS and ARIS, and temporal restrictions. To tackle the high-coupling and non-convexity of the problem, we initially utilize the fractional programming (FP) to reframe the original objective function. Then, we employ the alternating optimization (AO) algorithm to decompose the reformulated problem into distinct sub-problems, which enables us to iteratively optimize one set of variables while keeping others fixed. We derive the closed-form solutions of each sub-problem by developing the Lagrange dual method and linear programming. Numerical results validate the advantages of the proposed ARIS-ISCC scheme compared to the conventional benchmarks regarding to the WTCB.

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow a substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy.

Integrated sensing and communication (ISAC) is a key enabling technique for future wireless networks owing to its efficient hardware and spectrum utilization. In this paper, we focus on dual-functional waveform design for a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system, which is considered to be a promising solution for practical deployment. Since the dual-functional waveform carries communication information, its random nature leads to high range-Doppler sidelobes in the ambiguity function, which in turn degrades radar sensing performance. To suppress range-Doppler sidelobes, we propose a novel symbol-level precoding (SLP)-based waveform design for MIMO-OFDM ISAC systems by fully exploiting the available temporal degrees of freedom. Our goal is to minimize the range-Doppler integrated sidelobe level (ISL) while satisfying the constraints of target illumination power, multi-user communication quality of service (QoS), and constant-modulus transmission. To solve the resulting non-convex waveform design problem, we develop an efficient algorithm using the majorization-minimization (MM) and alternative direction method of multipliers (ADMM) methods. Simulation results show that the proposed waveform has significantly reduced range-Doppler sidelobes compared with signals designed only for communications and other baselines. In addition, the proposed waveform design achieves target detection and estimation performance close to that achievable by waveforms designed only for radar, which demonstrates the superiority of the proposed SLP-based ISAC approach.

Integrated sensing and communication has been identified as an enabling technology for forthcoming wireless networks. In an effort to achieve an improved performance trade-off between multiuser communications and radar sensing, this paper considers a dynamically-partitioned antenna array architecture for monostatic ISAC systems, in which each element of the array at the base station can function as either a transmit or receive antenna. To fully exploit the available spatial degrees of freedom for both communication and sensing functions, we jointly design the partitioning of the array between transmit and receive antennas together with the transmit beamforming in order to minimize the direction-of-arrival (DOA) estimation error, while satisfying constraints on the communication signal-to-interference-plus-noise ratio and the transmit power budget. An alternating algorithm based on Dinkelbach’s transform, the alternative direction method of multipliers, and majorization-minimization is developed to solve the resulting complicated optimization problem. To reduce the computational complexity, we also present a heuristic three-step strategy that optimizes the transmit beamforming after determining the antenna partitioning. Simulation results confirm the effectiveness of the proposed algorithms in significantly reducing the DOA estimation error.

Integrated sensing and communication (ISAC) is a key enabling technology for future 6G communication systems. Recently emerged symbol-level precoding (SLP) is considered to possess substantial potential for ISAC waveform design, owing to its ability to enhance multi-user communication and radar sensing performances by simultaneously leveraging both tempo-ral and spatial design degrees of freedom (DoFs). Considering the high complexity challenges brought by existing model-driven optimization based SLP design approaches, in this paper we pro-pose a lightweight SLP-Inception-Net and efficient data-driven deep learning algorithm to solve the highly complex SLP design problem. In particular, we propose a phase-based activation function method to guarantee the equality constraints and use the soft loss to ensure the inequality constraints. Simulation results verify that our proposed deep learning algorithm achieves comparable communication and radar sensing performance to the prior optimization-based approaches, while providing a noteworthy lOOO-fold reduction in computational complexity in terms of average execution time.

Integrated sensing and communication (ISAC) is a promising technology in future wireless systems owing to its efficient hardware and spectrum utilization. In this paper, we consider a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC system and propose a novel waveform design to provide better radar ranging performance by taking range sidelobe suppression into consideration. In specific, we aim to design the MIMO-OFDM dual-function waveform to minimize its integrated sidelobe level (ISL) while satisfying the quality of service (QoS) requirements of multi-user communications and the transmit power constraint. To achieve a lower ISL, the symbol-level precoding (SLP) technique is employed to fully exploit the degrees of freedom (DoFs) of the waveform design in both temporal and spatial domains. An efficient algorithm utilizing majorization-minimization (MM) framework is developed to solve the non-convex waveform design problem. Simulation results reveal radar ranging performance improvement and demonstrate the benefits of the proposed SLP-based low-range-sidelobe waveform design in ISAC systems.

Extremely large-scale antenna array (ELAA) is deemed as one of several key candidate technologies for the sixth generation (6G) mobile networks. Nevertheless, the near-field effect poses a significant challenge for ELAA systems as a result of employing a substantial quantity of antennas for the transmission of high-frequency signals. Furthermore, the practical implementation of ELAA by employing a hybrid beamforming framework boosts the impact of the near-field effect on the system performance. In order to effectively address this severe near-field effect, we propose a novel dynamic hybrid beamforming architecture, in which each antenna is either adaptively connected to one radio frequency (RF) chain for signal transmission, or deactivated for power saving. The dynamic hybrid beamforming design algorithm is developed to maximize the achievable sum-rate under the constraints of the constant modulus of phase shifters, non-overlapping dynamic connection network, and the total transmit power. To address the resulting complicated nonconvex design problem, we employ the fractional programming (FP) method to transform the objective function into a more tractable form and exploit the manifold-based algorithm to tackle the nonconvex constraint. The simulation results demonstrate the advantages of the proposed dynamic hybrid beamforming and the effectiveness of the FP-manifold-based algorithm in ELAA near-field communication systems.

Constructive interference exploited by symbol-level (SL) signal processing is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SL-DFRC signal design problem which maximizes the radar performance under communication performance constraints. We exploit the symmetrical non-convexity property of the communication-independent radar sensing metric to develop low- complexity yet efficient algorithms. We first propose a radar-to- DFRC (R2DFRC) algorithm that relies on the non-convexity of the radar sensing metric to find a set of radar-only solutions. Based on these solutions, we further exploit the symmetrical property of the radar sensing metric to efficiently design the DFRC signal. Since the radar sensing metric is independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, therefore reducing the computational complexity. We then develop an accelerated R2DFRC algorithm that further reduces the complexity. Finally, we demonstrate the superiority of the proposed algorithms compared to existing methods in terms of both radar sensing and communication performance as well as computational complexity.

Integrated sensing and communication (ISAC) has become a crucial technology in future wireless systems. The recently emerged symbol-level precoding (SLP) technique is promising for ISAC waveform design since it can provide better multi-user communication and radar sensing performance by leveraging both temporal and spatial design degrees of freedom (DoFs). Previous research has been confronted with the huge computation burden challenge brought by the SLP design. In this paper, we propose a novel model-driven deep learning based SLP design by unfolding an iterative alternative direction method of multiplier (ADMM) algorithm, in order to efficiently solve the joint ISAC waveform and reconfigurable intelligent surface (RIS) beamforming design problem for RIS assisted ISAC systems. Specifically, our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR) while satisfying the communication quality-of-service (QoS) requirements. Simulation results verify that our proposed model-driven deep learning algorithm achieves comparable radar sensing performance to the prior optimization-based approaches, while providing a noteworthy reduction in computational complexity in terms of average execution time.

The non-linearity of power amplifiers (PAs) in multiple antenna transmitters will cause spatial distortions and beam dispersion, which may lead to significant performance degradation. In this paper, we investigate the distortion-aware beamforming design in a multiuser multiple-input single-output (MU-MISO) system. Using a typical third-order memoryless polynomial distortion model, the impact of the nonlinear PA on the performance of MU-MISO is firstly analyzed by evaluating the receive signal-to-interference-plus-noise ratio (SINR) of UEs. Then, we aim to propose a distortion-aware beamforming scheme that can effectively pre-compensate for the beam dispersion caused by nonlinear PA distortion. Our objective is to maximize the sum-rate under the constraint of the transmit power by considering the effect of nonlinear PA distortion. The complex non-convex optimization problem is efficiently solved by an alternating optimization algorithm that utilizes the fractional programming (FP), penalty-based, and majorization-minimization (MM) methods. Finally, simulation studies demonstrate the substantial performance improvement achieved by utilizing the proposed distortion-aware beamforming scheme to mitigate nonlinear PA distortion and confirm the effectiveness of the developed beamforming design algorithm.

To unleash the potential of reconfigurable intelligent surface (RIS), in this paper we propose a novel dual-functional access point (DF-AP) for enhancing energy efficiency (EE) in the cell-free network. The DF-AP can dynamically switch between transmitter and reflector modes based on the wireless environment and the communication requirement. In the transmitter mode, the DF-AP works as a RIS-based transmitter, which can directly send data to users to improve the throughput of the system and ensure the quality of service (QoS). While in the reflector mode, the DF-AP operates as a conventional RIS, which passively reflects signals towards desired directions to increase coverage in an energy-efficient way. We propose a fractional programming (FP)-manifold-heuristic method to design the active beamforming of access points (APs) and DF-APs in the transmitter mode, the passive beamforming of DF-APs in the reflector mode, and the operation mode of DF-APs to maximize the system EE. The superiority of this DF-AP architecture and the effectiveness of the proposed design algorithm are then validated by simulation.

In this paper, we explore the physical layer security (PLS) of an active reconfigurable intelligent surface (RIS) assisted multi-input single-output (MISO) system in the presence of a passive eavesdropper (Eve), whose channel is unknown to the legitimate transmitter. In this practical scenario, we first propose an artificial noise (AN) based PLS approach, which can effectively deteriorate the eavesdropping by jointly designing the disturbance signal and the active RIS. However, the AN scheme requires a vast amount of energy to emit the noise signal, which can be potentially suppressed by Eve using more antennas. To tackle this issue, we then introduce another novel reconfigurable intelligent fading (RIF) approach, which utilizes the active RIS to rapidly manipulate the propagation environment, so that Eve suffers a fast fading channel, while the legitimate user (LU) still experiences a slow fading channel. Since Eve encounters difficulties in effectively estimating her fast fading channel, she cannot perform reliable legitimate information detection in a coherent manner, thereby significantly reducing the risk of information leakage and enabling secure communications. Simulation results demonstrate the effectiveness of both proposed approaches. Moreover, the RIF scheme achieves a substantial improvement in security performance compared to the AN scheme, particularly for the cases with limited transmit power.

Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and easily blocked transmission links, which motivates researchers to investigate cooperative ISAC networks. In order to further improve the degrees of freedom (DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e., base station (BS) mode selection problem, is meaningful to be studied. However, to our best knowledge, this crucial problem has not been extensively studied in existing works. In this paper, we consider the joint BS mode selection, transmit beamforming, and receive filter designs for cooperative cell-free ISAC networks, where multi-BSs cooperatively serve communication users and detect targets. An efficient joint beamforming design algorithm and three different heuristic BS mode selection methods are proposed to solve the non-convex NP-hard problem. Simulation results demonstrates the advantages of cooperative ISAC networks, the importance of BS mode selection, and the effectiveness of proposed algorithms.

Reconfigurable Intelligent Surfaces (RIS) have been proposed as a revolutionary technology with the potential to address several critical requirements of 6G communication systems. Despite its powerful ability for radio environment reconfiguration, the “double fading” effect constricts the practical system performance enhancements due to the significant path loss. A new active RIS architecture has been recently proposed to overcome this challenge. However, existing active RIS studies rely on an ideal amplification model without considering the practical hardware limitation of amplifiers, which may cause performance degradation using such inaccurate active RIS mod-eling. Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics. Then, based on the new amplification model, we propose a novel joint transmit beamforming and RIS reflection beamforming design considering the incident signal power on practical active RIS for multiuser multi-input single-output (MU-MISO) communication system. Fractional programming (FP), majorization minimization (MM) and block coordinate descent (BCD) methods are used to solve for the complex problem. Simulation results indicate the importance of the consideration of practical amplifier hardware characteristics in the joint beamforming designs and demonstrate the effectiveness of the proposed algorithm compared to other benchmarks.

Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform. Recently emerged symbol-level precoding (SLP) technique exhibits advancement in ISAC systems by leveraging the waveform design degrees of freedom (DoFs) in both temporal and spatial domains. However, traditional SLP-based ISAC systems are designed in a modular paradigm, which potentially limits the overall performance of communication and radar sensing. The high complexity of existing SLP design algorithms is another issue that hurdles the practical deployment. To break through the bottleneck of these approaches, in this paper we propose an end-to-end approach to jointly design the SLP-based dual-functional transmitter and receivers of communication and radar sensing. In particular, we aim to utilize deep learning-based methods to minimize the symbol error rate (SER) of communication users, maximize the detection probability, and minimize the root mean square error (RMSE) of the target angle estimation. Multi-layer perceptron (MLP) networks and a long short term memory (LSTM) network are respectively applied to the transmitter, communication users and radar receiver. Simulation results verify the feasibility of the proposed deep-learning-based end-to-end optimization for ISAC systems and reveal the effectiveness of the proposed neural networks for the end-to-end design.

In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultra-reliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multi-user multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. We then extend the proposed joint design algorithms to the scenario with imperfect self-interference cancellation. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for direct-of-arrival (DoA) estimation than for target detection.

Integrated sensing and communication (ISAC), which simultaneously performs sensing and communication functions within a shared frequency band and hardware platform, has emerged as a promising technology for future wireless systems. Nevertheless, the weak echo signal received by the low-sensitivity ISAC receiver significantly constrains sensing performance in scenarios involving obstructed targets. Active reconfigurable intelligent surface (RIS) has become a prospective solution by situationally manipulating the wireless propagations and amplifying the signals. In this paper, we investigate active RIS-empowered ISAC systems to enhance radar echo signal quality as well as communication performance. In particular, we focus on the joint design of the base station (BS) transmit precoding and the active RIS reflection beamforming to optimize the parameter estimation performance in terms of Cramér-Rao bound (CRB) subject to the communication users’ signal-to-interference-plus-noise ratio (SINR) requirements. An efficient algorithm based on alternating optimization, semidefinite relaxation (SDR), and majorization-minimization (MM) is proposed to solve the formulated challenging non-convex problem. Finally, simulation results validate the effectiveness of the developed algorithm and the potential of employing active RIS in ISAC systems to enhance direct-of-arrival (DoA) estimation performance.

Integrated sensing and communication (ISAC) has been envisioned as a key enabling technique for future wireless communication networks. In order to realize a better performance trade-off between multiuser communications and radar sensing, in this paper we focus on the transmit/receive antenna selection problem in mono-static ISAC systems. Unlike conventional fixed and evenly divided antenna allocation strategy, we propose to jointly optimize antenna selection and transmit beamforming according to different communication requirements and available resources. In particular, we aim to minimize the root-mean-square-error (RMSE) of direction-of-arrival (DoA) estimation, while satisfying the communication signal-to-interference-plus-noise ratio (SINR) requirements, the transmit power budget, and the inherent constraints on antenna selection. An efficient alternating algorithm based on alternative direction method of multipliers (ADMM) and majorization-minimization (MM) methods and matrix transformations/derivations is developed to solve the resulting problem. Simulation results confirm that flexible antenna allocation enables lower RMSE of DoA estimation in ISAC systems and demonstrate the effectiveness of the proposed joint antenna selection and beamforming design algorithm.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the performance of communication systems by dynamically adjusting wireless environment. In this paper, we aim to jointly optimize RIS matrices and beam-forming at base stations (BSs) in RIS-assisted multi-cell systems. Considering the high complexity of traditional algorithms, we adopt deep reinforcement learning algorithms (DRL) for the difficult joint design task. Moreover, to overcome the performance drawbacks of centralized DRL algorithms with large state-action spaces, we propose distributed DRL algorithms based on the distributed distributional deterministic policy gradient (D4PG) method and the federated learning (FL) framework. Experimental results demonstrate that distributed DRL algorithms can considerably improve learning efficiency while reducing complexity and communication overhead. Moreover, it can be applied to various systems with satisfactory performance and has better robustness compared to the centralized DRL algorithms.

Channel state information (CSI) acquisition is a crucial but challenging issue in reconfigurable intelligent surface (RIS)-aided systems due to the passive property of RIS. In this paper, we investigate channel estimation in practical RIS-assisted multiuser orthogonal frequency division multiplexing access (OFDMA) systems. Different from prior works which assume that the RIS has an ideal reflection model (i.e., each reflecting element has constant amplitude, variable phase shift, and the same response for signals at different subcarriers), in this work the channel estimation is investigated with a practical RIS reflection model by considering the amplitude-phase-frequency relationship of the reflected signals. Aiming at enhancing the accuracy and efficiency of the channel estimation, a novel channel estimation method with pilot subcarrier allocation is developed. Simulation results demonstrate the necessity of considering practical RIS reflection model for channel estimation and the effectiveness of our proposed channel estimation method and pilot subcarrier allocation scheme.

Dual-function radar-communication (DFRC) systems, which can efficiently utilize the congested spectrum and costly hardware resources by employing one common waveform for both sensing and communication (S&C), have attracted increasing attention. While the orthogonal frequency division multiplexing (OFDM) technique has been widely adopted to support high-quality communications, it also has great potentials of improving radar sensing performance and providing flexible S&C. In this paper, we propose to jointly design the dual-functional transmit signals occupying several subcarriers to realize multi-user OFDM communications and detect one moving target in the presence of clutter. Meanwhile, the signals in other frequency subcarriers can be optimized in a similar way to perform other tasks. The transmit beamforming and receive filter are jointly optimized to maximize the radar output signal-to-interference-plus-noise ratio (SINR), while satisfying the communication SINR requirement and the power budget. An majorization minimization (MM) method based algorithm is developed to solve the resulting non-convex optimization problem. Numerical results reveal the significant wideband sensing gain brought by jointly designing the transmit signals in different subcarriers, and demonstrate the advantages of our proposed scheme and the effectiveness of the developed algorithm.

Space-time adaptive processing (STAP) is an effective method for multi-input multi-output (MIMO) radar systems to identify moving targets in the presence of multiple interferers. The idea of joint optimization in both spatial and temporal domains for radar detection is consistent with the symbol-level precoding (SLP) technique for MIMO communication systems, that optimizes the transmit waveform according to instantaneous transmitted symbols. Therefore, in this paper we combine STAP and constructive interference (CI)-based SLP techniques to realize dual-functional radar-communication (DFRC). The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraints and the constant modulus power constraint. An efficient algorithm based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve the non-convex optimization problem. Simulation results verify the effectiveness of the proposed DFRC scheme and the associate algorithm.

In this paper, we investigate the physical layer security of multiuser multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence systems, in which the strong radar signals are exploited as inherent jamming signals to disrupt mali-cious receptions. The transmit beamformers of communication and radar systems are jointly designed to ensure the secure transmission by minimizing the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while satisfying the quality-of-service (QoS) of legitimate transmission, the requirement of radar target detection, and the transmit power constraints of radar and communication systems. An efficient fractional programming (FP) and semi-definite relaxation (SDR) based algorithm is proposed to solve the non-convex optimization problem. Simulation results verify the advancement of the proposed joint transmit beamforming on secure transmission for radar and communication coexistence systems and the effectiveness of the associate design algorithm.

Reconfigurable intelligent surface (RIS) is a promising solution to adaptively manipulate wireless propagation with low-cost passive devices. However, the traditional passive RIS can offer sufficient signal strength only when receivers are very close to it. Moreover, the users at the back side of it cannot be well served due to its reflective property. In this paper we introduce a novel reflection and relay dual-functional RIS architecture, which can simultaneously realize passive reflection and active relay functionalities. The problem of joint transmit beamforming and dual-functional RIS design is investigated to maximize the achievable sum-rate of a multiuser multiple-input single-output (MU-MISO) system. Based on fractional programming (FP) theory and majorization-minimization (MM) technique, we propose an efficient iterative transmit beamforming and RIS design algorithm. Simulation results demonstrate the superiority of the introduced dual-functional RIS architecture and the effectiveness of the proposed algorithm.

Dual-function radar-communication (DFRC), which can simultaneously perform both radar and communication functionalities using the same hardware platform, spectral resource and transmit waveform, is a promising technique for realizing integrated sensing and communication (ISAC). Space-time adaptive processing (STAP) in multi-antenna radar systems is the primary tool for detecting moving targets in the presence of strong clutter. The idea of joint spatial-temporal optimization in STAP-based radar systems is consistent with the concept of symbol-level precoding (SLP) for multi-input multi-output (MIMO) communications, which optimizes the transmit waveform for each of the transmitted symbols. In this paper, we combine STAP and SLP and propose a novel STAP-SLP-based DFRC system that enjoys the advantages of both techniques. The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraint and various waveform constraints including constant-modulus, similarity and peak-to-average power ratio (PAPR). An efficient algorithm framework based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve these complicated non-convex optimization problems. Simulation results verify the effectiveness of the proposed STAP-SLP-based MIMO-DRFC scheme and the associate algorithms.

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement.

Cell-free millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communications have been proposed as promising enablers for the next generation wireless networks. In this paper, we study the user association and hybrid beamforming in cell-free mmWave systems without full channel state information (CSI) acquisition. We consider a cloud radio access network (C-RAN), where multiple remote radio heads (RRHs) are distributed to communicate with users via analog beamforming, and connected to a centralized baseband unit (BBU) through fronthaul links which executes digital beamforming. We aim to jointly design user association, hybrid beamforming, and fronthaul compression with the aid of uplink training. A train-and-design framework is developed to achieve this goal. In particular, we first propose a two-stage uplink training approach to assist RRH-level design, during which the analog beamforming and user association are obtained. After that, digital beamforming and fronthaul compression are optimized at BBU based on the training results. Two performance metrics are considered in this paper, i.e. weighted sum-rate maximization and max-min fairness. Simulation results demonstrate the effectiveness of the proposed train-and-design framework for both sum-rate maximization and max-min fairness performance metrics. It is shown that the proposed algorithms can achieve comparable performance to the full-digital beamformer.

Symbol-level precoding (SLP), which converts the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol-error-rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol precoder design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop efficient design algorithms for typical max-min fairness and power minimization problems. In particular, after dividing all users into several groups, the precoders for each group are separately designed on a symbol-by-symbol basis by only utilizing the symbol information of the users in that group, in which the intra-group MUI is exploited using the concept of constructive interference (CI) and the inter-group MUI is also effectively suppressed. In order to further reduce the computational complexity, we utilize the Lagrangian dual, Karush-Kuhn-Tucker (KKT) conditions and the majorization-minimization (MM) method to transform the resulting problems into more tractable forms, and develop efficient algorithms for obtaining closed-form solutions to them. Extensive simulation results illustrate that the proposed G-SLP strategy and design algorithms dramatically reduce the computational complexity without causing significant performance loss compared with the traditional SLP schemes.

Reconfigurable intelligent surface (RIS) is regarded as a promising technology with great potential to boost wireless networks. Affected by the “double fading” effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS. Recently, active RIS is introduced to combat the double fading effect by actively amplifying incident signals with the aid of integrated reflection-type amplifiers. In order to reduce the hardware cost and energy consumption due to massive active components in the conventional fully-connected active RIS, a novel hardware-and-energy efficient sub-connected active RIS architecture has been proposed recently, in which multiple reconfigurable electromagnetic elements are driven by only one amplifier. In this paper, we first develop an improved and accurate signal model for the sub-connected active RIS architecture. Then, we investigate the joint transmit precoding and RIS reflection beamforming (i.e., the reflection phase-shift and amplification coefficients) designs in multiuser multiple-input single-output (MU-MISO) communication systems. Both sum-rate maximization and power minimization problems are solved by leveraging fractional programming (FP), block coordinate descent (BCD), second-order cone programming (SOCP), alternating direction method of multipliers (ADMM), and majorization-minimization (MM) methods. Extensive simulation results verify that compared with the conventional fully-connected structure, the proposed sub-connected active RIS can significantly reduce the hardware cost and power consumption, and achieve great performance improvement when power budget at RIS is limited.

Intelligent reflecting surface (IRS) has been widely considered as one of key enabling techniques for the future wireless networks owing to its ability of constructing favorable propagation environment by controlling the phase shifts of reflected electromagnetic (EM) waves that impinge on the surface. While an IRS only focuses on the reflective implementation, recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual-functionality of manipulating signal reflection and transmission. Thus, an IOS can provide service coverage for both sides of it. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system, in which the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Our goal is to jointly optimize the transmit beamformers at base station (BS), the reflective and transmissive phase-shifts of IOS, and the reflection-to-transmission ratio of IOS to minimize the total transmit power for the MU-MISO system, subject to the signal-to-interference-plus-noise ratio (SINR) requirements of individual users. An efficient iterative algorithm is presented to solve this non-convex optimization problem. Simulation results verify the advantage of the IOS-assisted wireless communication system and the efficiency of the associate beamforming design algorithm.

This paper investigates hybrid beamforming design in cooperative millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. We focus on the sum-rate maximization problem and aim to design the hybrid beamformer to maximize the weighted achievable sum-rate subject to the constraint of maximum transmit power for all base stations (BSs) and the constant modulus of phase shifters (PSs). Due to non-convexity of the weighted sum-of-logarithmic function, we firstly transform the objective function into an equivalent form with the aid of fractional programming (FP) theory. Then, we propose an iterative hybrid beamforming algorithm, in which manifold optimization is first employed to solve analog beamformer and then a closed-form solution of digital beamformer is derived by Lagrange multiplier method. Numerical results demonstrate the remarkable advantages of proposed hybrid beamforming algorithm compared with other classical approaches.

Symbol-level precoding (SLP), which can convert the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol error rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol SLP design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop an efficient design algorithm for a typical max-min fairness problem. This practical G-SLP strategy divides all users into several groups. SLP is utilized for the users within each group to convert intra-group MUI into constructive interference, meanwhile the inter-group MUI is also suppressed. In particular, we first use Lagrangian and Karush-Kuhn-Tucker (KKT) conditions to simplify the G-SLP design problem and then propose an iterative majorization-minimization (MM) based algorithm to solve it. Simulation results illustrate that the proposed G-SLP strategy dramatically reduces the computational complexity without causing significant performance loss compared with the traditional SLP scheme.

In dual-functional radar-communication (DFRC) systems, the transmit beamforming has attracted extensive attentions since it can simultaneously provide radar sensing functionality and high-rate wireless communications. Unlike the conventional linear precoding technology, in this paper we propose to employ the recently emerged symbol-level precoding technique in DFRC systems, expecting to take advantages of the multiuser interference for improving both radar and communication performance. The difference between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of communication users and the constant envelope power constraint. Some derivations are developed based on the penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the non-convex problem into two solvable sub-problems, which are iteratively solved using efficient algorithms. Simulations illustrate the effectiveness of the symbol-level precoding in DFRC systems and the proposed algorithm.

Intelligent reflecting surface (IRS) is deemed as a promising and revolutionizing technology for future wireless communication systems owing to its capability to intelligently change the propagation environment and introduce a new dimension into wireless communication optimization. Most existing studies on IRS are based on an ideal reflection model. However, it is difficult to implement an IRS which can simultaneously realize any adjustable phase shift for the signals with different frequencies. Therefore, the practical phase shift model, which can describe the difference of IRS phase shift responses for the signals with different frequencies, should be utilized in the IRS optimization for wideband and multi-band systems. In this paper, we consider an IRS-assisted multi-cell multi-band system, in which different base stations (BSs) operate at different frequency bands. We aim to jointly design the transmit beamforming of BSs and the reflection beamforming of the IRS to minimize the total transmit power subject to signal to interference-plus-noise ratio (SINR) constraints of individual user and the practical IRS reflection model. With the aid of the practical phase shift model, the influence between the signals with different frequencies is taken into account during the design of IRS. Simulation results illustrate the importance of considering the practical communication scenario on the IRS designs and validate the effectiveness of our proposed algorithm.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

Recently emerged symbol-level precoding techniques can exploit multi-user interference (MUI) by transforming it into constructive signals at receivers and thus contribute to symbol detection. This paper adopts this concept and aims to investigate the exploitation of MUI to enhance both physical layer security against eavesdropping and the quality of legitimate transmissions. Particularly, we consider the problem of secure symbol-level precoding in multi-user multi-input single-output (MU-MISO) wiretap systems for M-ary quadrature amplitude modulation (M-QAM) signals. Our goal is to design the symbol-level precoder to minimize the average transmit power while guaranteeing the quality of service (QoS) of all legitimate transmissions as well as ensuring security against eavesdropping. In order to tackle this unaffordable large scale problem, we propose to decompose it into several sub-problems. Then, an efficient modified Hooke-Jeeves pattern search algorithm is further utilized to solve the Lagrangian dual functions of these sub-problems. Simulation results validate the exploitation of MUI for security and illustrate the effectiveness of our proposed secure symbol-level precoding algorithm.

Hybrid precoding combined with large-scale antenna arrays is considered as a key enabling technology for millimeter wave (mmWave) communications for its advantages in both reducing the number of power-hungry radio frequency (RF) chains and providing for spatial multiplexing. In this paper, we consider a dynamically sub-connected hybrid architecture with hardware-efficient low-resolution phase shifters (PSs) for a wide-band mmWave multi-user multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system. In this architecture, each RF chain is adaptively connected to a non-overlapping subarray corresponding to channel state information (CSI). Thus, multiple-antenna diversity can be fully utilized to mitigate the performance loss caused by the use of low-resolution PSs. Aiming at maximize the average sum-rate of the considered mmWave MU-MISO-OFDM system, we develop an iterative algorithm based on penalty dual decomposition (PDD) methods. Simulation results demonstrate the advantages of the considered dynamically sub-connected hybrid architecture.

This paper considers the cloud radio access network (C-RAN) based millimeter-wave (mmWave) cell-free communications, where multiple remote radio heads (RRHs) are distributed to provide reliable communication links to users via analog beamforming and connected to centralized baseband unit (BBU) which carries out digital signal processing. We aim to jointly design the user association and analog/digital hybrid beamforming along with fronthaul compression to maximize the minimum signal to interference-plus-noise ratio (SINR) among users while satisfying the fronthaul capacity constraints. To solve this difficult combinatory problem, we propose to first obtain the user association and analog beamforing to maximize the minimum beamforming gain among users. Then, given the effective baseband channel, the digital beamformer and quantization noise covariance matrix still cannot be calculated directly due to the non-convexities of objective function and fronthaul constraint. To efficiently solve this problem, we transform the objective function into convex terms based on fractional programming method and iteratively calculate the digital beamformer and quantization noise covariance matrix until convergence is achieved. Simulation results show that the proposed algorithm can achieve comparable performance to the full-digital beamforming.

Intelligent reflecting surface (IRS) is considered as an enabling technology for future wireless communication systems since it can intelligently change the wireless environment to improve the communication performance. In this paper, an IRS-enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and the reflection of IRS to maximize the average sum-rate over all subcarriers. With the aid of the relationship between sum-rate maximization and mean square error (MSE) minimization, an efficient joint beamformer and IRS design algorithm is developed. Simulation results illustrate that the proposed algorithm can offer significant average sum-rate enhancement, which confirms the effectiveness of the use of the IRS for wideband wireless communication systems.

Intelligent reflecting surface (IRS) has emerged as a promising solution to enhance wireless communications in a low-cost and hardware-efficient fashion. Besides, symbol-level precoding (SLP) technique has attracted considerable attentions recently for its advantages in converting multiuser interference (MUI) into useful signal. In this paper, we investigate the symbol-level precoding in IRS-assisted multiuser multiple-input single-output (MU-MISO) systems to minimize the transmit power while guarantee the quality-of-service (QoS) of information transmissions. In order to solve this joint optimization problem, we develop an efficient iterative algorithm to decompose it into the precoder design and IRS design problems. To tackle the non-convex IRS design problem, we propose to use the log-sum-exp function to smooth the objective and map it into the Riemannian space, where the Riemannian conjugate gradient (RCG) algorithm is employed to solve this problem. Simulation results prove the significant performance improvement of IRS and illustrate the effectiveness of our proposed algorithm.

Multi-user interference (MUI) is usually considered to be a harmful component in wireless communications. However, recently emerged symbol-level precoding techniques can constructively exploit the MUI by transforming it into useful signal at the receiver and contribute to symbol detection. This paper adopts this concept and aims to investigate the exploitation of the MUI to enhance both physical layer security against eavesdropping and the quality of legitimate transmissions. Particularly, we consider the problem of secure symbol-level precoding in multi-user multi-input single-output (MU-MISO) wiretap systems. Our goal is to design the symbol-level precoder to guarantee the quality of service (QoS) of all legitimate transmissions as well as ensure security against eavesdropping. The symbol-level precoder algorithms for physical layer security are developed under different assumptions about the availability of channel state information (CSI) of the legitimate and eavesdropping channels. Extensive simulation results validate the exploitation of MUI for security and illustrate the effectiveness of our proposed secure symbol-level precoding algorithms.