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.

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 proposes a promising framework of multi-functional service incorporating sensing targets (STs), information receivers (IRs), and energy receivers (ERs) in an active reconfigurable intelligent surface (RIS)-aided integrated sensing, communication, and power transfer (ISCPT) system. In the proposed system, we aim to maximize the weighted sum of the received radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the transmit beamforming at the multi-functional base station (MFBS), the coefficients of active RIS, and the radar receive filter coefficients. Meanwhile, the constraints of the SINR of IRs, energy harvesting (EH) requirements of ERs, the power budget for the MFBS and active RIS, and the amplification gain should be satisfied. To guarantee the generality of formulated problems, we further incorporate the self-interference effects of echo signals, multi-target echo interference, simultaneous detection of multiple STs, and a nonlinear EH model into the generalized system model. Due to the presence of echo interference and multi-target echo interference, the MFBS transmits the dedicated sensing signal with the communication to enhance the sensing performance. The formulated problem is tackled by developing an efficient alternating optimization (AO) algorithm combined with fractional programming (FP) and majorization-minimization (MM) techniques. Finally, the numerical results reveal the impact of system parameters on the sensing performance, the trade-off relationship between multiple functionalities, and the deployment strategy of RIS. The main findings are as follows: 1) Active RIS is remarkably superior to passive RIS for ISCPT systems, especially for closer to the receivers with a 40 dB performance gain. 2) Comparatively, the radar sensing SINR is more sensitive to the number of active RIS units, while the SINR of IRs is more sensitive to the number of antennas at the base station. These results demonstrate that the proposed system holds the potential for practical deployment.

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.

Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of learned knowledge. While existing EFCIL methods leverage knowledge distillation to alleviate forgetting, they still face two critical challenges: semantic shift and decision bias. Specifically, the embeddings of old tasks shift in the embedding space after learning new tasks, and the classifier becomes biased towards new tasks due to training solely with new data, hindering the balance between old and new knowledge. To address these issues, we propose the Dual-Projection Shift Estimation and Classifier Reconstruction (DPCR) approach for EFCIL. DPCR effectively estimates semantic shift through a dual-projection, which combines a learnable transformation with a row-space projection to capture both task-wise and category-wise shifts. Furthermore, to mitigate decision bias, DPCR employs ridge regression to reformulate a classifier reconstruction process. This reconstruction exploits previous in covariance and prototype of each class after calibration with estimated shift, thereby reducing decision bias. Extensive experiments demonstrate that, on various datasets, DPCR effectively balances old and new tasks, outperforming state-of-the-art EFCIL methods. Our codes are available at https://github.com/RHe502/ICML25-DPCR.

Invisible watermarking is widely used to protect digital images from unauthorized use. Accurate assessment of watermarking efficacy is crucial for advancing algorithmic development. However, existing statistical metrics, such as PSNR, rely on access to original images, which are often unavailable in text-driven generative watermarking and fail to capture critical aspects of watermarking, particularly visibility. More importantly, these metrics fail to account for potential corruption of image content. To address these limitations, we propose WMarkGPT, the first multimodal large language model (MLLM) specifically designed for comprehensive watermarked image understanding, without accessing original images. WMarkGPT not only predicts watermark visibility but also generates detailed textual descriptions of its location, content, and impact on image semantics, enabling a more nuanced interpretation of watermarked images. Tackling the challenge of precise location description and understanding images with vastly different content, we construct three visual question-answering (VQA) datasets: an object location-aware dataset, a synthetic watermarking dataset, and a real watermarking dataset. We introduce a meticulously designed three-stage learning pipeline to progressively equip WMarkGPT with the necessary abilities. Extensive experiments on synthetic and real watermarking QA datasets demonstrate that WMarkGPT outperforms existing MLLMs, achieving significant improvements in visibility prediction and content description. The datasets and code are released at https://github.com/TanSongBai/WMarkGPT.

Precise navigation signals are essential for unmanned aerial vehicles (UAVs) to achieve accurate positioning and task execution. However, global positioning system (GPS) signals are highly vulnerable to spoofing and jamming attacks, posing serious threats to flight safety. Therefore, this paper proposes an intelligent detection algorithm based on federated learning (FL) for identifying spoofing and jamming signals in unmanned aerial vehicle (UAV) navigation. Firstly, the algorithm innovatively integrates the local feature extraction ability of the temporal convolutional network (TCN) and the global dependency modeling advantage of the Transformer network, constructing a TCN-Transformer model to capture the features of navigation data efficiently. Secondly, a distributed construction network is adopted, where weight aggregation and updates are performed via federated learning, enabling global optimization without data sharing. Furthermore, an accuracy-weighted aggregation strategy is introduced, dynamically assigning weights based on the detection performance of each client mode. This encourages contributions of high-quality data and computational resources, thereby enhancing the overall model performance. Simulation results demonstrate that the proposed method outperforms traditional algorithms in detecting navigation spoofing and jamming, offering superior detection accuracy and robustness.

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.

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.

As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, its deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance. A promising strategy is to treat RIS as a public resource managed by an RIS provider (RP), which can enhance resource allocation efficiency by allowing dynamic access for multiple OPs. However, the intricate nature of coordinating management and optimizing RIS configurations significantly complicates the implementation process. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) approach that decomposes the complicated RIS resource optimization problem into several subtasks. Specifically, a top-level RP-agent is responsible for RIS allocation, while low-level OP-agents control their assigned RISs and handle beamforming, RIS phase-shifts, and user association. By utilizing the semi-Markov decision process (SMDP) theory, we establish a sophisticated interaction mechanism between the RP and OPs, and introduce an advanced hierarchical proximal policy optimization (HPPO) algorithm. Furthermore, we propose an improved sequential-HPPO (S-HPPO) algorithm to address the curse of dimensionality encountered with a single RP-agent. Experimental results validate the stability of the HPPO algorithm across various environmental parameters, demonstrating its superiority over other benchmarks for joint resource optimization. Finally, we conduct a detailed comparative analysis between the proposed S-HPPO and HPPO algorithms, showcasing that the S-HPPO algorithm achieves faster convergence and improved performance in large-scale RIS allocation scenarios.

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.

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.

This paper studies an intelligent reflecting surface (IRS)-aided multi-antenna simultaneous wireless information and power transfer (SWIPT) system where anM-antenna access point (AP) servesKsingle-antenna information users (IUs) andJsingle-antenna energy users (EUs) with the aid of an IRS with phase errors. We explicitly concentrate on overloaded scenarios whereK+J>MandK≥M. Our goal is to maximize the minimum throughput among all the IUs by optimizing the allocation of resources (including time, transmit beamforming at the AP, and reflect beamforming at the IRS), while guaranteeing the minimum amount of harvested energy at each EU. Towards this goal, we propose two user grouping (UG) schemes, namely, the non-overlapping UG scheme and the overlapping UG scheme, where the difference lies in whether identical IUs can exist in multiple groups. Different IU groups are served in orthogonal time dimensions, while the IUs in the same group are served simultaneously with all the EUs via spatial multiplexing. The two problems corresponding to the two UG schemes are mixed-integer non-convex optimization problems and difficult to solve optimally. We first provide a method to check the feasibility of these two problems, and then propose efficient algorithms for them based on the big-M formulation, the penalty method, the block coordinate descent, and the successive convex approximation. Simulation results show that: 1) the non-robust counterparts of the proposed robust designs are unsuitable for practical IRS-aided SWIPT systems with phase errors since the energy harvesting constraints cannot be satisfied; 2) the proposed UG strategies can significantly improve the max-min throughput over the benchmark schemes without UG or adopting random UG; 3) the overlapping UG scheme performs much better than its non-overlapping counterpart when the absolute difference betweenKandMis small and the EH constraints are not stringent.

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.

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

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.

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

In this paper, we consider the weighted sum of transferred power maximization under the secrecy rate (SR) constraints in a secure simultaneous wireless information and power transfer (SWIPT) communication network assisted by reconfigurable intelligent surfaces (RIS). To tackle this challenging problem, we combine the cutting-the-edge successive convex approximation (SCA) and penalty dual decomposition (PDD) methods and have successfully developed a novel iterative solution. Compared to the existing literature, our newly proposed algorithm can apply to the most generic system setting that has arbitrary number of information and/or energy receivers. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of RIS deployment.

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.

For unmanned aerial vehicle (UAV)-assisted wireless networks, the continuous trajectory designs generally suffer from infinite number of variables of the continuous UAV trajectory. In this paper, to avoid unexpected trajectory approximation and overcome the difficulty in obtaining an accurate continuous trajectory, we aim at characterizing an analytical optimal solution for jointly designing the resource allocation and continuous UAV trajectory. We focus on a scenario with UAV at a fixed altitude being deployed to assist the wireless communication with a ground user. With limited energy accessible for the wireless transmissions, we construct a throughput maximization problem for jointly optimizing the continuous transmit power and the UAV's continuous trajectory. Via duality analysis, we obtain the features of the optimal power control and successfully convert the dual problem to a series of pure trajectory design problems, which can be optimally addressed based on a mechanical equivalence approach. Afterwards, we accordingly propose an algorithm for optimally solving the dual problem, from which the optimal joint solution can be analytically constructed in a closed form. Finally, we also verify our proposed algorithm and confirm the optimality of the obtained solution via simulations.

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.

The unmanned aerial vehicles (UAVs) have been widely used in civilian environments, due to its high flexibility, low cost and ease of deployment. In this paper, an UAV-enabled mobile edge computing (MEC) system is studied, in which the UAV serves as an aerial mobile base station to provide services for a group of ground user equipments (UEs) with computation task requests. We jointly optimize the time allocation, resource allocation and the UAV flying trajectory to minimize the time required for the UAV to complete the task, subject to the constraints of different kinds of resources, energy and velocity. Due to the non-convexity of the formulated problem, we first transform it to a feasibility check problem and then divide it onto three convex optimization subproblems. By using the block coordinate descent method and the successive convex approximate (SCA) method, we propose an efficient iterative algorithm to solve the three subproblems alternately with ensured convergence. Extensive simulation results show that the proposed joint optimization algorithm reduces the task completion time compared with other schemes.

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.

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.

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

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.

Internet of things (IoT) technology is an essential enabler to realize ubiquitous connections and pervasive intelli-gence for the future wireless communication system. The energy self-sustainability based on the wireless power transfer technique and the coexistence with heterogeneous networks will become two predominant attributes of IoT networks. In this paper we consider the system design in a context of coexistence of a wireless powered sensor network and a cellular system, both of which share common spectrum bandwidth and are assisted by intelligent reflecting surface (IRS). Specifically, the wireless sensors exploit the harvested energy from the cellular base station (BS) to transfer information to a data sink. We aim to design a cooperation scheme via jointly optimizing the time allocation of channel use, collaborative beamforming across networks and IRS phase-shifting control to improve the sensing network's throughput while guaranteeing the cellular users' quality of service. This design problem leads to a highly nonconvex and difficult mathematical optimization problem. Via utilizing the penalty-duality-decomposition (PDD) and successive convex approximation (SCA) methods, we have managed to develop an alternative optimization solution. Nu-merical results verify the effectiveness of our algorithm and demonstrate the benefits that come from the cooperative network design.

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.

This paper investigates hybrid beamforming for cooperative multi-user millimeter-wave (mmWave) multiple-input multiple-output (MIMO) networks. We aim to jointly design the user scheduling and hybrid beamforming to maximize the sum-rate subject to the transmit power of each base station. Due to the non-convexity of constant modulus of phase shifters and objective function, the problem is mathematically intractable. We propose a low-complexity two-step scheme, in which user scheduling and analog beamforming are first obtained to maximize the sum-beamforming-gain, followed by digital beamforming calculation based on weighted minimum-mean-square-error (wMMSE) approach. We further extend the hybrid beamforming design to dynamic sub-array architecture, where a novel antenna selection algorithm is developed. Simulation results demonstrate the effectiveness of the proposed algorithms, which can outperform other state-of-the-art approaches.

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, 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 IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

Cognitive industrial Internet of Things (CIIoT) can extend available spectrum resources by accessing the spectrum licensed to primary user (PU) on the premise of not disturbing the PU's communications. However, additional spectrum sensing and long-time working may consume much energy of CIIoT. In this article, a CIIoT with wireless energy harvesting (WEH) is proposed to harvest the radio frequency energy of PU's signal, and energy-efficient resource allocations in different spectrum access modes are presented to maximize the average transmission rate of CIIoT while guaranteeing its energy saving requirements. The underlay and overlay spectrum access modes for CIIoT with WEH are described, respectively, in which the energy-efficient resource allocations are formulated as joint optimization problems that can be solved using the alternating direction optimization and water-filling algorithm. By combining underlay and overlay modes, a hybrid spectrum access mode is proposed to enable the CIIoT to access both idle and busy spectrum without limiting its transmission power at the absence of PU. Simulation results show that the CIIoT with WEH can consume less power to achieve larger transmission rate, and the hybrid mode outperforms the underlay and overlay modes in the aspects of transmission rate and energy saving.

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.

Analog/digital hybrid beamforming is considered as a key enabling multiple antenna technology for implementing millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications since it can reduce the number of costly and power-hungry radio frequency (RF) chains while still providing for spatial multiplexing. In this paper, we introduce a novel hybrid beamforming architecture with dynamic antenna subarrays and hardware-efficient low-resolution phase shifters (PSs) for a wideband mmWave MIMO orthogonal frequency division multiplexing (MIMO-OFDM) system. By dynamically connecting each RF chain to a non-overlapping antenna subarray via a switch network and PSs, multiple-antenna diversity can be exploited to mitigate the performance loss due to the employment of practical low-resolution PSs. For this dynamic hybrid beamforming architecture, we jointly design the hybrid precoder and combiner to maximize the average spectral efficiency of the mmWave MIMO-OFDM system. In particular, the spectral efficiency maximization problem is first converted to a mean square error (MSE) minimization problem. Then, an efficient iterative hybrid beamformer algorithm is developed based on classical block coordination descent (BCD) methods. An analysis of the convergence and complexity of the proposed algorithm is also provided. Extensive simulation results demonstrate the superiority of the proposed hybrid beamforming algorithm with dynamic subarrays and low-resolution PSs.

Analog/digital hybrid beamforming architectures with large-scale antenna arrays have been widely considered in millimeter wave (mmWave) communication systems because they can address the tradeoff between performance and hardware efficiency compared with traditional fully-digital beamforming. Most of the prior work on hybrid beamforming focused on fully-connected architecture or partially-connected scheme with fixed-subarrays, in which the analog beamformers are usually realized by infinite-resolution phase shifters (PSs). In this paper, we introduce a novel hybrid beamforming architecture with dynamic subarrays and hardware-efficient low-resolution PSs for mmWave multiuser multiple-input single-output (MU-MISO) systems. By dynamically connecting each RF chain to a non-overlap subarray via a switch network and PSs, we can exploit multiple-antenna and multiuser diversities to mitigate the performance loss due to the use of practical low-resolution PSs. An iterative hybrid beamformer design algorithm is first proposed based on fractional programming (FP), aiming at maximizing the sum-rate performance of the MU-MISO system. In an effort to reduce the complexity, we also present a simple heuristic hybrid beamformer design algorithm for the dynamic subarray scheme. Extensive simulation results demonstrate the advantages of the proposed hybrid beamforming architecture with dynamic subarrays and low-resolution PSs compared to existing fixed-subarray schemes.

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.

Millimeter wave (mmWave) communications with large-scale antenna arrays and hardware-efficient analog/digital hybrid beamforming have been widely considered as one of the key technologies to enable very high data rate in the fifth generation (5G) applications. Meanwhile, physical layer security (PLS) in mmWave wiretap systems and secure hybrid beamformer designs have drawn increasing attention to safeguard 5G-and-beyond networks. However, in existing literatures, infinite or high-resolution phase shifters (PSs) are often assumed to implement fine-tunable analog beamformers, which are impractical due to high hardware cost and power consumption. In this paper, we consider the problem of hybrid beamformers design with practicallow-resolutionPSs for secure transmission in mmWave wiretap multi-input multi-output (MIMO) systems. We aim to develop secure hybrid beamforming algorithms to maximize the secrecy rate according to different availabilities of eavesdropper's channel state information (CSI). Particularly, when eavesdropper's CSI is available, the proposed algorithm first determines the secure analog beamformers by an iterative algorithm, then finds the digital beamformers which can further enhance the security. If eavesdropper's CSI is unknown, we develop an artificial noise (AN)-based secure hybrid beamforming approach. Simulation results demonstrate that our proposed algorithms can provide significant secrecy performance improvement.

Analog beamformer with large-scale antenna arrays has been widely considered in millimeter wave (mmWave) communication systems because of its superiority in hardware cost and energy consumption compared with traditional fully digital beamforming schemes. In this paper, we introduce an efficient dynamic subarray analog beamforming architecture with low-resolution phase shifters (PSs) for mmWave multiuser multipleinput single-output (MU-MISO) systems. In an effort to mitigate the performance loss due to the use of low- resolution PSs, each user can dynamically select a non-overlap subarray from total transmit antennas and use corresponding subarray analog beamformer to transmit signals. This dynamic subarray analog beamforming architecture can utilize the multi- antenna/multiuser diversities by dynamically adapting to channel state information (CSI) of users. An efficient dynamic subarray analog beamformer design algorithm is also presented, which aims at maximizing the sum-rate of the MU-MISO system. Simulation results demonstrate that the proposed dynamic analog beamforming solution can significantly outperform the conventional fixed-subarray schemes.

This paper investigates analog beamforming with large-scale antenna arrays for single-group multicast transmission. We focus on the max-min fair (MMF) problem and aim to design the analog beamformer with infinite and finite resolution phase shifters (PSs), respectively, to maximize the minimum signal-to-noise ratio (SNR) over all users subject to a transmit power constraint. However, the constant magnitude and infinite/finite phase constraints imposed by PSs frustrate the access of an optimal solution of analog beamformer. We thus formulate a sub-optimal MMF problem alternatively and propose a low-complexity algorithm, which iteratively determines each element of analog beamformer to conditionally maximize the minimum SNR among users. The computational complexities of our proposed algorithms are linear in the number of antennas. Simulation results illustrate that our proposed analog beamformer design can achieve satisfactory performance which is close to the full-digital case and outperform the other state-of-the-art schemes.

Economical and energy-efficient analog/digital hybrid beamforming has been widely considered as a promising approach for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. While most hybrid beamforming techniques consider a fully-connected structure with a large number of phase shifters (PSs), the partially-connected structure has drawn more attention recently since it requires much less PSs and can further improve energy-efficiency. However, the impractical assumption of infinite or high resolution of PSs in existing solutions frustrates the real-world deployment of hybrid beamforming designs, and low- resolution PSs are typically adopted to reduce the hardware complexity and power consumption. In an effort to achieve maximum hardware efficiency, this paper focuses on the partially-connected architecture with one-bit (binary) PSs and considers the problem of joint hybrid precoder and combiner design for such mmWave MIMO systems. We propose to successively design the analog beamformers associated with each pair of sub- array, aiming at conditionally maximizing the spectral efficiency. A novel binary analog precoder and combiner optimization algorithm is proposed under a rank-1 approximation of the interference-included equivalent channel with polynomial complexity in the number of antennas. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the advantages of proposed hardware-efficiency hybrid precoder and combiner design.

Advances of mobile technology and global booming of “smartphone economy” promote a tremendous growth of smartphone-oriented applications with an exponential increase of mobile traffic in the past decade, resulting in expectable saturation of LTE spectrum in the next few years. Mobile-to-mobile (M2M) communications, capable of extending LTE capacity with enhanced spectrum efficiency, have been considered as a promise solution to this problem. One popular approach to deploy the M2M technology is to establish an M2M subsystem as an underlay to the current LTE network so that the M2M link can share the same radio resource with LTE regular links. Although this scheme can further explore the spectrum efficiency, it is challenging to design such an M2M subsystem without harmful interference to LTE regular users. We propose in this paper a novel proactive interference cancellation mechanism to form an interference-free underlay. The major innovation of the proposed framework is to use the codebook-based precoding technique to design appropriate precoders for eNB and M2M transmitter in order to achieve maximal reception interference suppression proactively at the transmitter side. Since the codebook-based precoding technique has already been employed in LTE, the proposed scheme is compatible to current LTE systems and applicable for real-world implementation. This is in strong contrast to several existing interference cancellation M2M schemes that highly rely on the unrealistic assumption on theoretical-oriented channel state information (CSI) feedback. Preliminary simulation results demonstrate the efficiency of the proposed scheme.

Acting like a legitimate user via sending identical pilot signals, the pilot spoofing attack launched by an active eavesdropper can disrupt the reception of the legitimate receiver and, more importantly, cause severe information leakage. Although such an attack can be detected by the recently presented random-training-aided (RTA) pilot spoofing detector with high accuracy and low complexity, the assumption that the active eavesdropper remains silent during the random phase limits the usage of RTA algorithm in practice. In addition, a natural question to ask is how to secure the data transmission after spoofing detection. Motivated by these two aspects, in this paper we first investigate the robustness of RTA pilot spoofing detector and illustrate that it can provide high detection accuracy even when the eavesdropper is active during the random phase. Then, we further propose a zero-forcing (ZF)-based secure transmission approach to protect the legitimate transmission from eavesdropping in case of the missed detection of the active eavesdropper. Simulation studies demonstrate the robustness of the RTA pilot spoofing detector and the satisfactory performance of the proposed secure transmission strategy.

Millimeter wave (mmWave) communications have been considered as a key technology for future 5G wireless networks. In order to overcome the severe propagation loss of mmWave channel, multiple-input multiple-output (MIMO) systems with analog/digital hybrid precoding and combining transceiver architecture have been widely considered in mmWave systems. However, physical layer security (PLS) in mmWave MIMO systems and the secure hybrid beamformer design have not been well investigated. In this paper, we consider the problem of hybrid precoder and combiner design for secure transmission in mmWave MIMO systems in order to protect the legitimate transmission from eavesdropping. When eavesdropper's channel state information (CSI) is known, we first propose a joint analog precoder and combiner design algorithm which can prevent the information leakage to the eavesdropper. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further maximize the secrecy rate. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we develop an artificial noise (AN)-based hybrid beamforming approach, which can jam eavesdropper's reception while maintaining the quality-of-service (QoS) of intended receiver at the pre-specified level. Simulation results demonstrate that our proposed algorithms offer significant secrecy performance improvement compared with other hybrid beamforming algorithms.

In realistic steganalysis applications, the mismatched problem can lead to the degradation of performance in steganalysis. The main reason is the discrepancy of feature distributions between training set and testing set. In this paper, we present a Contribution-based Feature Transfer (CFT) algorithm for JPEG mismatched steganalysis. CFT tries to learn two transformations to transfer training set features by evaluating both the sample feature and dimensional feature contributions. We can obtain new feature representations so as to approach the feature distribution of the testing samples. The comparison to prior arts reveals the superiority of CFT on the experiments for the mismatched JPEG steganalysis in the heterogeneous cover source scenario.

Millimeter wave (mmWave) communications have been considered as a key technology for future 5G wireless networks since it can provide orders‐of‐magnitude wider bandwidth than current cellular bands. To overcome the severe propagation loss of the mmWave channel, an economic and energy‐efficient analogue/digital hybrid precoding and combining transceiver architecture is widely used in mmWave massive multiple‐input multiple‐output (MIMO) systems. The digital precoding/combining layer offers more freedom than pure analogue one and enables multi‐stream transmission. In this study, the authors consider the problem of codebook‐based joint hybrid precoder and combiner design for multi‐stream transmission in mmWave MIMO systems. The authors propose to jointly select an analogue precoder and combiner pair for each data stream successively, which can maximise the channel gain as well as suppress the interference between different data streams. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further mitigate the interference and maximise the sum‐rate. Both fully‐connected and partially‐connected hybrid beamforming structures are investigated. Simulation results demonstrate that the proposed algorithms exhibit prominent advantages in combating interference between different data streams and offer satisfactory performance improvements compared with the existing codebook‐based hybrid beamforming schemes.

In this letter, we propose a novel classification strategy called the coupled dictionary learning for target recognition in synthetic aperture radar (SAR) images. First, we train structured synthesis dictionaries to reflect the difference among each category. Second, we introduce a shared dictionary to reduce the effect of common features, such as the high similarity caused by specular reflection. Finally, we use the analysis dictionary to improve the efficiency of recognition by eliminating the constraint of the l0-norm or l1-norm of sparse code. Experimental results on Moving and Stationary Target Acquisition and Recognition data set indicate that our method can achieve better performance in SAR target recognition than the state-of-the-art methods, such as tritask joint sparse representation and CKLR. Especially, this method can be more robust when the depressions have obvious changes.

Interference alignment (IA) is an emerging technique for interference management for multi-user networks. Due to the superposition of signals from legitimate users at the eavesdropper, the IA-based network seems to be more secure than conventional wireless networks. Nevertheless, when adequate antennas are equipped, the legitimate information can still be eavesdropped. In this paper, we analyze the performance of the external eavesdropper, and propose an artificial noise (AN) scheme for IA-based networks without the knowledge of eavesdropper's channel state information. In this scheme, a single-stream AN is generated by each user, which will disrupt the eavesdropping without introducing any additional interference to the legitimate transmission of IA-based networks. Simulation results are provided to show the effectiveness of the proposed anti-eavesdropping scheme for IA-based networks.

In this study, the authors consider additive spread‐spectrum (SS) data embedding in transform‐domain host data. Conventional additive SS embedding schemes use an equal‐amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude‐adaptive SS embedding scheme. Particularly, symbol‐by‐symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit‐error‐rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi‐carrier/multi‐message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude‐adaptive SS embedding scheme can provide order‐of‐magnitude performance improvement over several other state‐of‐the‐art SS embedding schemes.

In interference alignment (IA)-based networks, interferences are constrained into certain subspaces at the unintended receivers, and the desired signal can be recovered free of interference. Due to the superposition of signals from legitimate users at the eavesdropper, the IA-based network seems to be more secure than conventional wireless networks. Nevertheless, when adequate antennas are equipped, the legitimate information can still be eavesdropped. In this paper, we analyze the performance of the external eavesdropper, and propose two anti-eavesdropping schemes for IA-based networks. When the channel state information (CSI) of eavesdropper is available, zero-forcing scheme can be utilized, in which the transmitted signals are zero-forced at the eavesdropper through the precoding of transmitters in IA-based networks. Furthermore, a more generalized artificial noise (AN) scheme is proposed for IA-based networks without the knowledge of eavesdropper's CSI. In this scheme, a single-stream AN is generated by each user, which will disrupt the eavesdropping without introducing any additional interference to the legitimate transmission of IA-based networks. In addition, the feasibility conditions, transmission rate, and eavesdropping rate are analyzed in detail, and an iterative algorithm to achieve the scheme is also designed. Extensive simulation results are provided to verify our analysis results and show the effectiveness of the proposed anti-eavesdropping schemes for IA-based networks.

Discriminative dictionary learning (DDL) has been applied to various pattern classification problems. Despite satisfying experimental results, most existing discriminative dictionary learning methods emphasize too much on the role of l0or l1-norm sparsity, while the underlying local structure of original data is totally ignored. In this paper, we present a novel dictionary learning method, named Locality Sensitive Discriminative Dictionary Learning (LSDDL), which combines basic dictionary learning scheme and locality relationship of original data which is propagated to the coding vectors. The learned discriminative dictionary can map the original data points into a new space in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments clearly show that our method has very competitive performance in contrast to previous discriminative dictionary learning methods.

Feature extraction is a crucial and difficult issue in pattern recognition tasks with the high-dimensional and multiple features. To extract the latent structure of multiple features without label information, multi-view learning algorithms have been developed. In this paper, motivated by manifold learning and multi-view Non-negative Matrix Factorization (NM-F), we introduce a novel feature extraction method via multi-view NMF with local graph regularization, where the inner-view relatedness between data is taken into consideration. We propose the matrix factorization objective function by constructing a nearest neighbor graph to integrate local geometrical information of each view and apply two iterative updating rules to effectively solve the optimization problem. In the experiment, we use the extracted feature to cluster several realistic datasets. The experimental results demonstrate the effectiveness of our proposed feature extraction approach.

Relative attributes provide accurate information for image processing to describe which image is more natural, more open, etc. Robustness of relative attribute learning depends on the labeled comparative image pairs. However, manually labeling is a labor intensive and time-consuming task. In this paper, a semi-supervised learning approach based on group sparse is proposed to discover pairwise comparisons automatically. We generate an initial level division of the labeled training images for the basic of new constraints. Then, group sparse representation for the unlabeled images is introduced by embedding the level information into the dictionary. The semi-supervised process is conducted by selecting samples which have minimum reconstruction errors and adding new constraints to the model by comparing the selected ones with the samples in dictionary. Experiments on three public datasets demonstrate the effectiveness of our proposed method.

In this study, the authors investigate eavesdropping mitigation for wireless communications with physical‐layer security techniques. They consider a multicasting scenario with one transmitter, multiple trusted receivers and multiple eavesdroppers, each equipped with one antenna. Aiming at protecting trusted communications from data interception, they develop two novel waveform designs for eavesdropping reduction under two scenarios: (i) when the eavesdroppers’ channel state information (CSI) is known, they design the waveform and transmit energy which can minimise the maximal signal‐to‐interference‐plus‐noise ratios (SINRs) at the eavesdroppers while achieving guaranteed SINRs at the trusted receivers and (ii) when the eavesdroppers’ CSIs are not available, they adopt ‘artificial noise’ (AN) strategy aiming at generating as much disturbance to eavesdroppers as possible. A novel waveform and transmit power design is proposed which can maximise the AN's energy with assured quality‐of‐service at trusted receivers. Extensive simulation studies illustrate the efficiency of the proposed designs for eavesdropping mitigation in wireless communications.

This paper considers the problem of intelligent jamming attack on a MIMO wireless communication link with a transmitter, a receiver, and an adversarial jammer, each equipped with multiple antennas. We present an optimal jamming signal design, which can maximally disrupt the MIMO transmission when the transceiver adopts an anti-jamming mechanism. In particular, signal-to-jamming-plus-noise ratio (SJNR) at the receiver is used as the anti-jamming reliability metric of the legitimate MIMO transmission. The jamming signal design is developed under the most crucial scenario for the jammer where the legitimate transceiver adopt jointly designed maximum-SJNR transmit beamforming and receive filter to suppress/mitigate the disturbance from the jammer. Under this best anti-jamming scheme, we aim to optimize the jamming signal to minimize the receiver's maximum-SJNR under a given jamming power budget. The optimal jamming signal designs are developed in different cases with accordance to the availability of channel state information (CSI) at the jammer. The analytical approximations of the jamming performance in terms of average maximum-SJNR are also provided. Extensive simulation studies confirm our analytical predictions and illustrate the efficiency of the designed optimal jamming signal on disrupting MIMO communications.

We investigate the problem of embedding data in raw video sequences with minimum video mean-square distortion for any required data recovery error rate. In particular, for any given video frame sequence and any (block) transform domain of interest, we find the optimal carrier and scalar parametrized linear operator on the video data that maximize the output signal-to-interference-plus-noise ratio (SINR) of the maximum-SINR data receiver filter or, equivalently, minimize the average embedding distortion for any target message extraction error rate. The procedure is extended from single-carrier to multi-carrier (multiple messages) embedding. As a practical consideration, a sub-optimal computationally efficient embedding algorithm is also proposed. Extensive experimental results demonstrate that sub-optimal embedding as described has video distortion versus data extraction error rate performance comparable to optimal embedding. Our studies also demonstrate the robustness of the optimal (and sub-optimal) embedding schemes to H.264 compliant encoding.

We develop a novel waveform design approach to minimize the likelihood that a message transmitted wirelessly between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, first, with knowledge of the eavesdropper's channel state information (CSI) we find the optimal waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. Simulation studies demonstrate our analytical developments and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions.

Wireless physical-layer security is an emerging field of research aiming at preventing eavesdropping in an open wireless medium. In this paper, we propose a novel waveform design approach to minimize the likelihood that a message transmitted between trusted single-antenna nodes is intercepted by an eavesdropper. In particular, with knowledge first of the eavesdropper's channel state information (CSI), we find the optimum waveform and transmit energy that minimize the signal-to-interference-plus-noise ratio (SINR) at the output of the eavesdropper's maximum-SINR linear filter, while at the same time provide the intended receiver with a required pre-specified SINR at the output of its own max-SINR filter. Next, if prior knowledge of the eavesdropper's CSI is unavailable, we design a waveform that maximizes the amount of energy available for generating disturbance to eavesdroppers, termed artificial noise (AN), while the SINR of the intended receiver is maintained at the pre-specified level. The extensions of the secure waveform design problem to multiple intended receivers are also investigated and semidefinite relaxation (SDR) -an approximation technique based on convex optimization- is utilized to solve the arising NP-hard design problems. Extensive simulation studies confirm our analytical performance predictions and illustrate the benefits of the designed waveforms on securing single-input single-output (SISO) transmissions and multicasting.

Compressed sensing is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, in particular, we consider a video system where acquisition is carried out in the form of direct compressive sampling (CS) with no other form of sophisticated encoding. Therefore, the burden of quality video sequence reconstruction falls solely on the receiver side. We show that effective implicit motion estimation and decoding can be carried out at the receiver or decoder side via sparsity-aware recovery. The receiver performs sliding-window interframe decoding that adaptively estimates Karhunen–Loève bases from adjacent previously reconstructed frames to enhance the sparse representation of each video frame block, such that the overall reconstruction quality is improved at any given fixed CS rate. Experimental results included in this paper illustrate the presented developments.

We consider the problem of extracting blindly data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We develop a novel multicarrier/signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multicarrier spread-spectrum embedding. Neither the original host nor the embedding carriers are assumed available. Experimental studies on images show that the developed algorithm can achieve recovery probability of error close to what may be attained with known embedding carriers and host autocorrelation matrix.

This paper considers the problem of blindly extracting data embedded over a wide band in a spectrum (transform) domain of a digital medium (image, audio, video). We first develop a multi-signature iterative generalized least-squares (M-IGLS) core procedure to seek unknown data hidden in hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding signatures are assumed available. Then, cross-correlation enhanced M-IGLS (CC-M-IGLS), a procedure described herein in detail that is based on statistical analysis of repeated independent M-IGLS processing of the host, is seen to offer most effective hidden message recovery. Experimental studies on images show that the proposed CC-M-IGLS algorithm can achieve recovery probability of error close to what may be attained with known embedding signatures and host autocorrelation matrix.

We consider the problem of passive spread-spectrum steganalysis where the objective is to decide the presence or absence of spread-spectrum hidden data in a given image (a binary hypothesis testing problem). Unlike conventional feature-based approaches, we describe an unsupervised (blind) low-complexity approach based on generalized least-squares principles that may enable rapid high-volume image processing. Extensive experiments on image sets and comparisons with existing steganalysis techniques demonstrate most satisfactory classification performance measured in probability of correct detection versus induced false alarm rate.

We consider the problem of cognitive code-division channelization (simultaneous power and code-channel allocation) for secondary transmission links co-existing with an unknown primary code-division multiple-access (CDMA) system. We first develop a blind primary-user identification scheme to detect the binary code sequences (signatures) utilized by primary users. To create a secondary link we propose two alternative procedures -one of moderate and one of low computational complexity- that optimize the secondary transmitting power and binary code-channel assignment in accordance with the detected primary code channels to avoid "harmful" interference. At the same time, the optimization procedures guarantee that the signal-to-interference-plus-noise ratio (SINR) at the output of the maximum SINR linear secondary receiver is no less than a certain threshold to meet secondary transmission quality of service (QoS) requirements. The extension of the channelization problem to multiple secondary links is also investigated. Simulation studies presented herein illustrate the theoretical developments.

We derive new bounds on the total squared correlation (TSC) of quaternary (quadriphase) signature/sequence sets for all lengths L and set sizes K. Then, for all K, L, we design minimum-TSC optimal sets that meet the new bounds with equality. Direct numerical comparison with the TSC value of the recently obtained optimal binary sets shows under what K, L realizations gains are materialized by moving from the binary to the quaternary code-division multiplexing alphabet. On the other hand, comparison with the Welch TSC value for real/complexfield sets shows that, arguably, not much is to be gained by raising the alphabet size above four for any K, L.

We derive new bounds on the total squared correlation (TSC) of quaternary (quadriphase) signature/sequence sets for all lengths L and set sizes K. Then, for all K, L, we design minimum-TSC optimal sets that meet the new bounds with equality. Direct numerical comparison with the TSC value of the recently obtained optimal binary sets shows under what K, L realizations gains are materialized by moving from the binary to the quaternary code-division multiplexing alphabet. On the other hand, comparison with the Welch TSC value for real/complex-field sets shows that, arguably, not much is to be gained by raising the alphabet size above four for any K,L. The sum-capacity (as well as the maximum squared correlation and total asymptotic efficiency) of minimum TSC quaternary sets is also evaluated in closed-form and contrasted against the sum capacity of minimum-TSC optimal binary and real/complex sets.