Integrated Sensing and Communication (ISAC) is a key emerging 6G technology. Despite progress, ISAC still lacks scalable methods for joint AP clustering and user/target scheduling in distributed deployments under fronthaul limits. Moreover, existing ISAC solutions largely rely on centralized processing and full channel state information, limiting scalability. This paper addresses joint access point (AP) clustering, user and target scheduling, and AP mode selection in distributed cell-free ISAC systems operating with constrained fronthaul capacity. We formulate the problem as a mixed-integer linear program (MILP) that jointly captures interference coupling, RF-chain limits, and sensing requirements, providing optimal but computationally demanding solutions. To enable real-time and scalable operation, we propose ASSENT (ASSociation and ENTity selection), a graph neural network (GNN) framework trained on MILP solutions to efficiently learn association and mode-selection policies directly from lightweight link statistics. Simulations show that ASSENT achieves near-optimal utility while accurately learning the underlying associations. Additionally, its single forward pass inference reduces decision latency compared to optimization-based methods. An open-source Python/PyTorch implementation with full datasets is provided to facilitate reproducible and extensible research in cell-free ISAC.

Covert communications provide a stronger privacy protection than cryptography and physical-layer security (PLS). However, previous works on covert communications have implicitly assumed the validity of channel reciprocity, i.e., wireless channels remain constant or approximately constant during their coherence time. In this work, we investigate covert communications in the presence of a disco RIS (DRIS) deployed by the warden Willie, where the DRIS with random and time-varying reflective coefficients acts as a “disco ball”, introducing time-varying fully-passive jamming (FPJ). Consequently, the channel reciprocity assumption no longer holds. The DRIS not only jams the covert transmissions between Alice and Bob, but also decreases the error probabilities of Willie’s detections, without either Bob’s channel knowledge or additional jamming power. To quantify the impact of the DRIS on covert communications, we first design a detection rule for the warden Willie in the presence of time-varying FPJ introduced by the DRIS. Then, we define the detection error probabilities, i.e., the false alarm rate (FAR) and the missed detection rate (MDR), as the monitoring performance metrics for Willie’s detections, and the signal-to-jamming-plus-noise ratio (SJNR) as a communication performance metric for the covert transmissions between Alice and Bob. Based on the detection rule, we derive the detection threshold for the warden Willie to detect whether communications between Alice and Bob is ongoing, considering the time-varying DRIS-based FPJ. Moreover, we conduct theoretical analyses of the FAR and the MDR at the warden Willie, as well as SJNR at Bob, and then present unique properties of the DRIS-based FPJ in covert communications. We present numerical results to validate the derived theoretical analyses and evaluate the impact of DRIS on covert communications.

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.

In this paper, we study a secure integrated sensing and communication (ISAC) system employing a full-duplex base station with sensing capabilities against a mobile proactive adversarial target—a malicious unmanned aerial vehicle (M-UAV). We develop a game-theoretic model to enhance communication security, radar sensing accuracy, and power efficiency. The interaction between the legitimate network and the mobile adversary is formulated as a non-cooperative Stackelberg game (NSG), where the M-UAV acts as the leader and strategically adjusts its trajectory to improve its eavesdropping ability while conserving power and avoiding obstacles. In response, the legitimate network, acting as the follower, dynamically allocates resources to minimize network power usage while ensuring required secrecy rates and sensing performance. To address this challenging problem, we propose a low-complexity successive convex approximation (SCA) method for network resource optimization combined with a deep reinforcement learning (DRL) algorithm for adaptive M-UAV trajectory planning through sequential interactions and learning. Simulation results demonstrate the efficacy of the proposed method in addressing security challenges of dynamic ISAC systems in 6G, i.e., achieving a Stackelberg equilibrium with robust performance while mitigating the adversary’s ability to intercept network signals.

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.

In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidths, designing mmWave systems with low-resolution ADCs is beneficial. To tackle this issue, we propose a channel estimation design using task-based quantization that considers the underlying hybrid analog and digital architecture in order to improve the system performance under finite bit-resolution constraints. Our goal is to accomplish a channel estimation task that minimizes the mean squared error distortion between the true and estimated channel. We develop two types of channel estimators: a cascaded channel estimator for an RIS with purely passive elements, and an estimator for the separate RIS-related channels that leverages additional information from a few semi-passive elements at the RIS capable of processing the received signals with radio frequency chains. Numerical results demonstrate that the proposed channel estimation designs exploiting task-based quantization outperform purely digital methods and can effectively approach the performance of a system with unlimited resolution ADCs. Furthermore, the proposed channel estimators are shown to be superior to baselines with small training overhead.

Non-orthogonal multiple access (NOMA) is a promising technology for next-generation wireless communication systems due to its enhanced spectral efficiency. However, wireless communication is facing increasing requirements for security. To that end, jamming mitigation using multi-antennas has emerged as an important research topic. In this paper, we consider an uplink NOMA system with a reconfigurable intelligent surface (RIS) that assists the uplink users and, at the same time, mitigates the jammer. Our goal is to minimize the total users’ transmitted power under signal-to-interference-plus-noise ratio constraints at the base station. To be effective, typically a high-dimensional RIS is needed, leading to a large optimization problem, which in general faces convergence problems. We propose an iterative algorithm for this high-dimensional non-convex optimization problem that converges with a jammer comprising as many as 64 antennas, and an RIS with 128 elements. More specifically, we introduce a design and optimize the performance of an absorptive RIS (A-RIS). Compared to a standard RIS, we show that an A-RIS can dramatically reduce the users’ required transmit power and successfully mitigate the jammer. The A-RIS is in particular useful in cases when the number of jammer antennas is of the same order as the number of A-RIS elements.

Extremely large-scale antenna arrays (XL-arrays) and ultra-high frequencies are two fundamental technologies for future sixth-generation (6G) wireless networks, providing enhanced system capacity and substantial bandwidth expansion. To fully leverage these technological advancements, conventional far-field models must be replaced by more accurate near-field spherical-wave propagation models. This paper investigates a near-field communication system comprising a hybrid analog-digital beamforming base station (BS) and multiple mobile users, aiming to maximize system sum-rate through optimized codebook design, beam selection, and digital precoding. To accommodate dynamic user distributions, we propose two model-agnostic meta-learning (MAML)-based frameworks that enable prompt adaptation by learning well-initialized models for fine tuning. The first framework integrates the MAML method with a deep neural network (DNN) to design near-field codebooks tailored to the user distributions, addressing the limitations of conventional uniform codebooks. The second framework employs a joint neural network (NN) for beam selection and digital precoding, combining deep reinforcement learning (DRL) and deep unfolding. The DRL NN formulates beam selection as a Markov Decision Process, while the deep-unfolding NN approximates optimal digital precoding through a lightweight iterative algorithm without matrix inversion. Simulation results show that the proposed frameworks significantly outperform conventional methods, achieving superior generalization and overall performance in dynamic near-field scenarios.

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

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

This work studies near-field secure communications through transmit beamfocusing. We examine the benefit of having a protected eavesdropper-free zone around the legitimate receiver, and we determine the worst-case secrecy performance against a potential eavesdropper located anywhere outside the protected zone. A max-min optimization problem is formulated for the beamfocusing design with and without artificial noise transmission. Despite the NP-hardness of the problem, we develop a synchronous gradient descent-ascent framework that approximates the global maximin solution. A low-complexity solution is also derived that delivers excellent performance over a wide range of operating conditions. We further extend this study to a scenario where it is not possible to physically enforce a protected zone. To this end, we consider secure communications through the creation of a virtual protected zone using a full-duplex legitimate receiver. Numerical results demonstrate that exploiting either the physical or virtual receiver-centered protected zone with appropriately designed beamfocusing is an effective strategy for achieving secure near-field communications.

Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an efficient sensing-assisted long-term beam tracking framework that selects optimal beams from a codebook for current and multiple future time slots. We first design a large attention-enhanced neural network (NN) to fully exploit past visual observations for beam tracking. A convolutional NN extracts compact image features, while gated recurrent units with attention capture the temporal dependencies within sequences. The large NN then acts as the teacher to guide the training of a lightweight student NN via knowledge distillation. The student requires shorter input sequences yet preserves long-term beam prediction ability. Numerical results demonstrate that the teacher achieves Top-5 accuracies exceeding 93% for current and six future time slots, approaching state-of-the-art performance with a 90% reduction of model parameters. The student closely matches the teacher's performance while reducing the number of model parameters by over 1670% and cutting complexity by over 450%, despite operating with 60% shorter input sequences. This improvement significantly enhances data efficiency, reduces latency, and reduces power consumption in sensing and processing.

Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target JCAS system to ensure fairness and balance between communications and sensing performance. We jointly optimize the transmit and receive beamformers to maximize the weighted sum of the minimum communications rate and sensing mutual information. The formulated problem is highly challenging due to its non-smooth and non-convex nature. To overcome the challenges, we reformulate the problem into an equivalent but more tractable form. We first solve this problem by alternating optimization (AO) and then propose a machine learning algorithm based on the AO approach. Numerical results show that our scheme scales effectively with the number of the communications users and provides better performance with shorter run time compared to conventional optimization approaches.

This work studies near-field secure communications empowered by beamfocusing and demonstrates, for the first time, the benefit of having a protected eavesdropper-free zone around the legitimate receiver. We consider the worst-case secrecy performance against an eavesdropper potentially located anywhere outside the protected zone. Under this consideration, a max-min optimization problem for beamfocusing design is formulated, which can be interpreted as a two-player sequential game between the transmitter and eavesdropper. Despite the NPhardness of the problem, we propose a synchronous gradient descent ascent framework that approximates the global maximin solution. Moreover, we present a low-complexity heuristic beamfocusing solution that delivers excellent performance over a wide range of scenarios. Numerical results demonstrate that exploiting the receiver-centered protected zone with appropriately designed beamfocusing is an effective strategy for achieving near-field secure communications.

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International audience

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

Non-orthogonal multiple access (NOMA) is a promising technology garnering significant attention among the Internet of Things (IoT) community due to its superior spectral efficiency. This work addresses the rate region boundary enhancement of downlink NOMA systems under imperfect successive interference cancellation (SIC) with advanced improper Gaussian signaling (IGS), which provides additional degrees of freedom for system design. We investigate a universal scenario in which two users adopt improper signaling and their transmit powers are optimized. We first formulate the achievable rate of both users in terms of the impropriety degree of the IGS. First, the analytical expressions for the best improper transmission are characterized by jointly optimizing the users’ power and the impropriety degree for the perfect SIC case. Then, a deep Q network (DQN)-based approach is provided to find the rate region of the IGS-aided NOMA system under imperfect SIC. Simulations presented for the downlink NOMA system support the analysis, illustrating that IGS can efficiently enhance the rate region of the NOMA system compared to proper signaling.

Motivated by progress in data-driven supervised learning, semantic communication has witnessed remarkable advancements in improving the efficiency of data transmission under various channel conditions. These advancements typically require a substantial amount of training data for offline training, which is challenging in practical systems. Therefore, in this work, we propose O2SC, a one-shot online-learning framework for semantic communication to achieve adaptive transmission under different channel conditions. Since semantic communication relies on acquired channel state information (CSI), we jointly design the channel estimation and semantic communication processes. Specifically, we introduce a denoising module based on one-shot self-supervised learning, allowing semantic communication systems to adapt to new channel conditions without the need to collect extensive training data. The denoising module is utilized to eliminate noise in the received data samples, using only the data samples themselves. Following this, we further exploit meta-learning to allow the system to quickly adapt to diverse channel conditions, by finding an appropriate initialization for each data sample in a timely way. Simulation results demonstrate that the proposed method achieves performance close to that of supervised learning-based approaches while also providing improved generalizability across different channel conditions.

Massive multiple-input multiple-output (MIMO) technology has revolutionized wireless communication by significantly enhancing spectral efficiency, however its high energy consumption has become a key concern. There is increasing research interest in implementing massive MIMO systems using low-resolution digital-to-analog converters (DACs) to reduce the hardware cost and energy consumption. Meanwhile, the broadcast nature of wireless communications systems poses security risks, exposing user information to potential eavesdroppers (Eve), and this issue has been studied less in the context of low-resolution massive MIMO systems. This paper investigates the potential of low-resolution massive MIMO systems to enhance physical layer security (PLS) without relying on artificial noise (AN). We propose a novel spatial Sigma-Delta modulation technique that strategically leverages quantization noise to obscure confidential communications from Eve, even with limited channel state information. Our design shifts quantization noise away from legitimate users while maintaining its presence near Eve, thus improving PLS. We formulate the resulting non-convex, semi-infinite design problem and apply a proximal majorization-minimization (PMM) algorithm, ensuring convergence to a Karush–Kuhn–Tucker (KKT) point. To enhance computational efficiency, we introduce a proximal distance algorithm (PDA) that addresses the constraints independently, yielding closed-form solutions for projections and proximal operators. Extensive numerical experiments validate our approach, demonstrating effective noise shaping for both users and Eve. Our findings illustrate that quantization noise can be a valuable asset in securing communications in low-resolution massive MIMO systems.

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.

Precoding is a critical and long-standing technique in multi-user communication systems. However, the majority of existing precoding methods do not consider channel coding in their designs. In this paper, we consider the precoding problem in multi-user multiple-input single-output (MISO) systems, incorporating channel coding into the design. By leveraging the error-correcting capability of channel codes we increase the degrees of freedom in the transmit signal design, thereby enhancing the overall system performance. We first propose a novel data-dependent precoding framework for coded MISO systems, referred to aschannel-coded precoding(CCP), which maximizes the probability that information bits can be correctly recovered by the channel decoder. This proposed CCP framework allows the transmit signals to produce data symbol errors at the users’ receivers, as long as the overall information BER performance can be improved. We develop the CCP framework for both one-bit and multi-bit error-correcting capacity and devise a projected gradient-based approach to solve the design problem. We also develop a robust CCP framework for the case where knowledge of perfect channel state information (CSI) is unavailable at the transmitter, taking into account the effect of both noise and channel estimation errors. Finally, we conduct numerous simulations to verify the effectiveness of the proposed CCP and its superiority compared to existing precoding methods, and we identify situations where the proposed CCP yields the most significant gains.

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration.

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a “disco” IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are in-effective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs.

This paper presents a secrecy performance study of a wiretap communication system with finite blocklength (FBL) transmissions over Rayleigh fading channels, based on the definition of an average information leakage (AIL) metric. We evaluate the exact and closed-form approximate AIL performance, assuming that only statistical channel state information (CSI) of the eavesdropping link is available. Then, we reveal an inherent statistical relationship between the AIL metric in the FBL regime and the commonly-used secrecy outage probability in conventional infinite blocklength communications. Aiming to improve the secure communication performance of the considered system, we formulate a blocklength optimization problem and solve it via a low-complexity approach. Next, we present numerical results to verify our analytical findings and provide various important insights into the impacts of system parameters on the AIL. Specifically, our results indicate that i) compromising a small amount of AIL can lead to significant reliability improvements, and ii) the AIL experiences a secrecy floor in the high signal-to-noise ratio regime.

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

Reconfigurable intelligent surface (RIS) technology can enhance the performance of wireless systems, but an ad-versary can use such technology to deteriorate communication links. This paper explores an RIS-based attack on multi-user wireless systems that require channel reciprocity for time-division duplexing (TDD). We demonstrate that deploying an RIS with a non-diagonal phase shift matrix can compromise channel reciprocity and lead to poor TDD performance. The attack can be achieved without transmission of signal energy, without channel state information (CSI), and without synchronization with the legitimate system, and thus it is difficult to detect and counteract. We provide an extensive set of simulation studies on the impact of such an attack on the achievable sum rate of the legitimate system, and we design a heuristic algorithm for optimizing the attack in cases where some partial knowledge of the CSI is available. Our results demonstrate that this channel reciprocity attack can significantly degrade the performance of the legitimate system.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can enable full spatial coverage. In this paper, we investigate an artificial intelligence (AI)-empowered STAR-RIS-assisted multi-user communication system. We aim to maximize the system sum-rate by jointly optimizing the mode selection matrix of the STAR-RIS elements, the passive beamforming matrix of the STAR-RIS, and the beamforming matrix of the base station (BS). Due to the mixed-timescale structure, we propose a joint neural network (NN) design consisting of an advantage pointer-critic (APC) NN to optimize the discrete variables in the mode selection matrix, a fully connected NN, and a deep-unfolding NN to optimize the beamforming matrices. Specifically, the element mode selection problem is formulated as a Markov decision process (MDP), and an APC NN is carefully designed to solve it. The passive beamforming matrix of the STAR-RIS is optimized by employing a fully connected NN. Then, we apply an iterative weighted minimum mean-square error (WMMSE)-based deep-unfolding NN for the BS beamforming design. Simulation results verify that our jointly trained NN can outperform the conventional algorithms with reduced overhead.

Due to the simultaneous downlink and uplink transmissions in reconfigurable intelligent surface (RIS)-empowered frequency division duplexing (FDD) communication systems, it is necessary to design the RIS phase shifts to balance the performance of both directions at the same time. Focusing on a single-user multiple-input multiple-output system, we aim to maximize a weighted sum-rate for the downlink and uplink. To address the resulting non-convex optimization problem, we employ an alternating optimization (AO) algorithm, which includes two techniques for optimizing the phase shifts at the RIS. A manifold optimization-based algorithm is applied for the first technique, and a lower-complexity AO approach is developed for the second. Our numerical results demonstrate that the proposed algorithms lead to substantial enhancement of the entire system compared to existing baseline schemes.

The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology into IoT systems has gained popularity as a means of accomplishing this objective. Electrocardiograms (ECGs) have become a promising biometric tool for security because their intrinsic and dynamic nature makes them difficult to steal and forge for replay attacks. However, as with other biometric-based security approaches, attacks on ECG biometric systems have been developed. This study examines the vulnerability of template-based ECG biometric systems to reconstruction attacks. These attacks involve exposing the biometric templates of registered subjects in a hacked database and attempting to reconstruct their ECGs to spoof the system. Both deep learning models and “heuristic” approaches are used to perform this task, depending on the intruder’s level of knowledge of the template construction. Several reconstruction attack strategies are proposed and evaluated for fiducial- and PCA-based systems using ECGs from the Physikalisch-Technische Bundesanstalt database of 285 subjects. The experimental results demonstrate a reconstruction similarity of at least 0.93 and an increase in the false-positive identification-error rate of more than 73%.

In this paper, an uplink signal detection problem is considered for Internet-of-Things (IoT) networks. Owing to the imperfections of IoT devices including I/Q imbalance and amplifier non-linearity, exact end-to-end channel models and accurate channel state information (CSI) are typically unavailable at the receiver, which obstructs the application of traditional model-based signal detection algorithms. A consensus has been reached recently that Deep learning (DL) is a promising tool to cope with this problem. However, for the IoT scenarios under consideration, devices typically transmit data using short packets with few pilot symbols, the amount of which is insufficient for each device to individually train a detector. In order to combat the data scarcity barrier and enable few-shot learning, a novel training paradigm is proposed where pilot symbols from different devices are aggregated in an intelligent manner to train a universal signal detector. Specifically, this paper devises a knowledge-driven signal detector architecture following the modular design methodology typically used in classical communication system receivers. Under this framework, three neural networks (NNs), a signal classifier, a channel feature extractor, and a signal feature extractor are created to form decision statistics and produce estimates of the transmitted symbols. Furthermore, borrowing ideas from domain adaptation, a novel component referred to as a link discriminator is integrated into the architecture to improve its generalizability. The proposed signal detector exploits pilot symbols from various IoT devices to train a universal detector that can be applied to different channel conditions without retraining, including those not seen in the training phase. Simulation results verify the superiority of the proposed knowledge-driven detector compared with existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less data.

This article investigates block-level interference exploitation (IE) precoding for multiuser multiple-input-single-output (MU-MISO) downlink systems. To overcome the need for symbol-level IE precoding to frequently update the precoding matrix, we propose to jointly optimize all the precoders or transmit signals within a transmission block. The resultant precoders only need to be updated once per block, and while not necessarily constant over all the symbol slots, we refer to the technique as block-level slot-variant IE precoding. Through a careful examination of the optimal structure and the explicit duality inherent in block-level power minimization (PM) and signal-to-interference-plus-noise ratio (SINR) balancing (SB) problems, we discover that the joint optimization can be decomposed into subproblems with smaller variable sizes. As a step further, we propose block-level slot-invariant IE precoding by adding a structural constraint on the slot-variant IE precoding to maintain a constant precoder throughout the block. A novel linear precoder for IE is further presented, and we prove that the proposed slot-variant and slot-invariant IE precoding share an identical solution when the number of symbol slots does not exceed the number of users. Numerical simulations demonstrate that the proposed precoders achieve a significant complexity reduction compared against benchmark schemes, without sacrificing performance.

Reconfigurable intelligent surface (RIS) technology, given its ability to favorably modify wireless communication environments, will play a pivotal role in the evolution of future communication systems. This paper proposes rate maximization techniques for both single-user and multiuser MIMO systems, based on the well-known weighted minimum mean square error (WMMSE) criterion. Using a suitable weight matrix, the WMMSE algorithm tackles an equivalent weighted mean square error (WMSE) minimization problem to achieve the sum-rate maximization. By considering a more practical RIS system model that employs a tensor-based representation enforced by the electromagnetic behavior exhibited by the RIS panel, we detail both the sum-rate maximizing and WMSE minimizing strategies for RIS phase shift optimization by deriving the closed-form gradient of the WMSE and the sum-rate with respect to the RIS phase shift vector. Our simulations reveal that the proposed rate maximization technique, rooted in the WMMSE algorithm, exhibits superior performance when compared to other benchmarks.

To reap the benefits of reconfigurable intelligent surfaces (RIS), channel state information (CSI) is generally required. However, CSI acquisition in RIS systems is challenging and often results in very large pilot overhead, especially in unstructured channel environments. Consequently, the RIS channel estimation problem has attracted a lot of interest and also been a subject of intense study in recent years. In this paper, we propose a decision-directed RIS channel estimation framework for general unstructured channel models. The employed RIS contains some hybrid elements that can simultaneously reflect and sense the incoming signal. We show that with the help of the hybrid RIS elements, it is possible to accurately recover the CSI with a pilot overhead proportional to the number of users. Therefore, the proposed framework substantially improves the system spectral efficiency compared to systems with passive RIS arrays since the pilot overhead in passive RIS systems is proportional to the number of RIS elements times the number of users. We also perform a detailed spectral efficiency analysis for both the pilot-directed and decision-directed frameworks. Our analysis takes into account both the channel estimation and data detection errors at both the RIS and the BS. Finally, we present numerous simulation results to verify the accuracy of the analysis as well as to show the benefits of the proposed decision-directed framework.

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.

A promising approach to deal with the high hardware cost and energy consumption of massive MIMO transmitters is to use low-resolution digital-to-analog converters (DACs) at each antenna element. This leads to a transmission scheme where the transmitted signals are restricted to a finite set of voltage levels. This paper is concerned with the analysis and optimization of a low-cost quantized precoding strategy, referred to as linear-quantized precoding, for a downlink massive MIMO system under Rayleigh fading. In linear-quantized precoding, the signals are first processed by a linear precoding matrix and subsequently quantized component-wise by the DAC. In this paper, we analyze both the signal-to-interference-plus-noise ratio (SINR) and the symbol error probability (SEP) performances of such linear-quantized precoding schemes in an asymptotic framework where the number of transmit antennas and the number of users grow large with a fixed ratio. Our results provide a rigorous justification for the heuristic arguments based on the Bussgang decomposition that are commonly used in prior works. Based on the asymptotic analysis, we further derive the optimal precoder within a class of linear-quantized precoders that includes several popular precoders as special cases. Our numerical results demonstrate the excellent accuracy of the asymptotic analysis for finite systems and the optimality of the derived precoder.

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the$\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, but also pose a huge risk for physical layer security. Existing works have illustrated that a disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties (like a “disco ball”), can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS can be employed to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by DIRS-based fully-passive jammers (FPJs), an anti-jamming precoder is proposed that requires only the statistical characteristics of the DIRS-based ACA channels instead of their CSI. The statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is derived based on the statistical characteristics. Furthermore, we prove that the anti-jamming precoder can achieve the maximum signal-to-jamming-plus-noise ratio (SJNR). To acquire the ACA statistics without changing the system architecture or cooperating with the illegitimate DIRS, we design a data frame structure that the legitimate access point (AP) can use to estimate the statistical characteristics. During the designed data frame, the LUs only need to feed back their received power to the legitimate AP when they detect jamming attacks. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJs and the feasibility of the designed data frame used by the legitimate AP to estimate the statistical characteristics.

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

This paper investigates reconfigurable intelligent surface (RIS)-aided frequency division duplexing (FDD) communication systems. Since the downlink and uplink signals are simultaneously transmitted in FDD, the phase shifts at the RIS should be designed to support both transmissions. Considering a single-user multiple-input multiple-output system, we formulate a weighted sum-rate maximization problem to jointly maximize the downlink and uplink system performance. To tackle the non-convex optimization problem, we adopt an alternating optimization (AO) algorithm, in which two phase shift optimization techniques are developed to handle the unit-modulus constraints induced by the reflection coefficients at the RIS. The first technique exploits the manifold optimization-based algorithm, while the second uses a lower-complexity AO approach. Numerical results verify that the proposed techniques rapidly converge to local optima and significantly improve the overall system performance compared to existing benchmark schemes.

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.

This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently. When the primary base station (PBS) shares data and perfect channel state information (CSI) with the cognitive base station (CBS), we derive an SLP approach that minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both primary users (PUs) and cognitive users (CUs). The resulting optimization has a quadratic objective and linear inequality (LI) constraints, which can be solved by standard convex methods. For the case of imperfect CSI from the PBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate the uncertain channels, we adopt a max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the quantized PBS CSI and employ a stochastic constraint to formulate the problem. Both robust approaches also result in a quadratic objective with LI constraints. Simulation results show that, rather than simply trying to eliminate the network’s cross-interference, the proposed robust SLP schemes enable the primary and secondary networks to aid each other in meeting their quality of service constraints.

Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MIMO channels, based on which a hierarchical beam training scheme is proposed to reduce the training overhead; 2) a combined angular-distance codebook is designed for hybrid-far-near-field MIMO channels, based on which a two-stage beam training scheme is proposed to achieve alignment in the angular and distance domains separately. For maximizing the achievable rate while reducing the complexity, an alternating optimization algorithm is proposed to carry out the joint optimization iteratively. Specifically, the RIS coefficient matrix is optimized through the beam training process, the optimal combining matrix is obtained from the closed-form solution for the mean square error (MSE) minimization problem, and the active beamforming matrix is optimized by exploiting the relationship between the achievable rate and MSE. Numerical results reveal that: 1) the proposed beam training schemes achieve near-optimal performance with a significantly decreased training overhead; 2) compared to the angular-only far-field channel model, taking the additional distance information into consideration will effectively improve the achievable rate when carrying out beam design for near-field communications.

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and the UAV’s trajectory. While the overall optimization problem is nonconvex, we tackle it by applying a block successive convex approximation (BSCA) approach to divide the original problem into three subproblems and solve them separately. Then, an overall iterative algorithm is proposed to obtain the final design with guaranteed convergence. Our proposed low-complexity algorithm incorporates robust trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of the UAV-relay’s mission. Simulation results demonstrate significant performance improvements compared to various benchmark schemes and provide useful design insights on the coding blocklengths and transmit powers along the trajectory of the UAV.

Physical-layer security (PLS) is a promising technique to complement more traditional means of communication security in beyond-5G wireless networks. However, studies of PLS are often based on ideal assumptions such as infinite coding blocklengths or perfect knowledge of the wiretap link’s channel state information (CSI). In this work, we study the performance of finite blocklength (FBL) transmissions using a new secrecy metric — the average information leakage (AIL). We evaluate the exact and approximate AIL with Gaussian signaling and arbitrary fading channels, assuming that the eavesdropper’s instantaneous CSI is unknown. We then conduct case studies that use artificial noise (AN) beamforming to analyze the AIL in both Rayleigh and Rician fading channels. The accuracy of the analytical expressions is verified through extensive simulations, and various insights regarding the impact of key system parameters on the AIL are obtained. Particularly, our results reveal that allowing a small level of AIL can potentially lead to significant reliability enhancements. To improve the system performance, we formulate and solve an average secrecy throughput (AST) optimization problem via both non-adaptive and adaptive design strategies. Our findings highlight the significance of blocklength design and AN power allocation, as well as the impact of their trade-off on the AST.

Reconfigurable intelligent surface (RIS) technology is emerging as a promising technique for performance enhancement for next-generation wireless networks. This paper investigates the physical layer security of an RIS-assisted multiple-antenna communication system in the presence of random spatially distributed eavesdroppers. The RIS-to-ground channels are assumed to experience Rician fading. Using stochastic geometry, exact distributions of the received signal-to-noise-ratios (SNRs) at the legitimate user and the eavesdroppers located according to a Poisson point process (PPP) are derived, and closed-form expressions for the secrecy outage probability (SOP) and the ergodic secrecy capacity (ESC) are obtained to provide insightful guidelines for system design. First, the secrecy diversity order is obtained as 2/α2, where α2denotes the path loss exponent of the RIS-to-ground links. Then, it is revealed that the secrecy performance is mainly affected by the number of RIS reflecting elements,N, and the impact of the number of transmit antennas and transmit power at the base station is marginal. In addition, when the locations of the randomly located eavesdroppers are unknown, deploying the RIS closer to the legitimate user rather than to the base station is shown to be more efficient. Moreover, it is also found that the density of randomly located eavesdroppers, λe, has an additive effect on the asymptotic ESC performance given by log2(1/λe). Finally, numerical simulations are conducted to verify the accuracy of these theoretical observations.

This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; (3) The physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications.

Emerging intelligent reflecting surfaces (IRSs) significantly improve system performance, while also pose a huge risk for physical layer security. A disco IRS (DIRS), i.e., an illegitimate IRS with random time-varying reflection properties, can be employed by an attacker to actively age the channels of legitimate users (LUs). Such active channel aging (ACA) generated by the DIRS-based fully-passive jammer (FPJ) can be applied to jam multi-user multiple-input single-output (MU-MISO) systems without relying on either jamming power or LU channel state information (CSI). To address the significant threats posed by the DIRS-based FPJ, an anti-jamming strategy is proposed that requires only the statistical characteristics of DIRS-jammed channels instead of their CSI. Statistical characteristics of DIRS-jammed channels are first derived, and then the anti-jamming precoder is given based on the derived statistical characteristics. Numerical results are also presented to evaluate the effectiveness of the proposed anti-jamming precoder against the DIRS-based FPJ.

Integrated sensing and communication (ISAC) is expected to be offered as a fundamental service in the upcoming sixth-generation (6G) communications standard. However, due to the exposure of information-bearing signals to the sensing targets, ISAC poses unique security challenges. In recent years, intelligent reflecting surfaces (IRSs) have emerged as a novel hardware technology capable of enhancing the physical layer security of wireless communication systems. Therefore, in this paper, we consider the problem of transmit and reflective beamforming design in a secure IRS-enabled ISAC system to maximize the beampattern gain at the target. The formulated non-convex optimization problem is challenging to solve due to the intricate coupling between the design variables. Moreover, alternating optimization (AO) based methods are inefficient in finding a solution in such scenarios, and convergence to a stationary point is not theoretically guaranteed. Therefore, we propose a novel successive convex approximation (SCA)-based second-order cone programming (SOCP) scheme in which all of the design variables are updated simultaneously in each iteration. The proposed SCA-based method significantly outperforms a penalty-based benchmark scheme previously proposed in this context. Moreover, we also present a detailed complexity analysis of the proposed scheme, and show that despite having slightly higher per-iteration complexity than the benchmark approach the average problem-solving time of the proposed method is notably lower than that of the benchmark scheme.

In this paper, we propose a secure short-packet communication (SPC) system involving an unmanned aerial vehicle (UAV)-aided relay in the presence of a terrestrial passive eavesdropper. The considered system, which is applicable to various next-generation Internet-of-Things (IoT) networks, exploits a UAV as a mobile relay, facilitating the reliable and secure exchange of intermittent short packets between a pair of remote IoT devices with strict latency. Our objective is to improve the overall secrecy throughput performance of the system by carefully designing key parameters such as the coding blocklengths and the UAV trajectory. However, this inherently poses a challenging optimization problem that is difficult to solve optimally. To address the issue, we propose a low-complexity algorithm inspired by the block successive convex approximation approach, where we divide the original problem into two subproblems and solve them alternately until convergence. Numerical results demonstrate that the proposed design achieves significant performance improvements relative to other benchmarks, and offer valuable insights into determining appropriate coding blocklengths and UAV trajectory.

Overlay cognitive radio (CR) networks include a primary and cognitive base station (BS) sharing the same frequency band. This paper focuses on designing a robust symbol-level pre-coding (SLP) scheme where the primary BS shares data and quantized channel state information (CSI) with the cognitive BS. The proposed approach minimizes the cognitive BS transmission power under symbol-wise Safety Margin (SM) constraints for both the primary and cognitive systems. We apply the additive quantization noise model to describe the statistics of the quantized PBS CSI and employ a stochastic constraint to formulate the optimization problem, which is then converted to be deterministic. Simulation results show that the robust SLP protects the primary users from the effect of the imperfect CSI and simultaneously offers significantly improved energy efficiency compared to nonrobust methods.

Hybrid beamforming (HBF) is a key enabler for millimeter-wave (mmWave) communications systems, but HBF optimizations are often non-convex and of large dimension. In this paper, we propose an efficient deep unfolding-based HBF scheme, referred to as ManNet-HBF, that approximately maximizes the system spectral efficiency (SE). It first factorizes the optimal digital beamformer into analog and digital terms, and then reformulates the resultant matrix factorization problem as an equivalent maximum-likelihood problem, whose analog beamforming solution is vectorized and estimated efficiently with ManNet, a lightweight deep neural network. Numerical results verify that the proposed ManNet-HBF approach has near-optimal performance comparable to or better than conventional model-based counterparts, with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, it attains 98.62% the SE of the Riemannian manifold scheme but 13250 times faster.

This paper explores reconfigurable intelligent surfaces (RIS) for mitigating cross-system interference in spectrum sharing applications. Unlike conventional reflect-only RIS that can only adjust the phase of the incoming signal, a hybrid RIS is considered that can configure the phase and modulus of the impinging signal by absorbing part of the signal energy. We investigate two spectrum sharing scenarios: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver, and (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that optimizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is formulated, and then solved through fractional programming. Numerical results show that with a sufficient number of elements, the hybrid RIS can in many cases completely eliminate the interference, unlike a conventional non-absorptive RIS.

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.

Non-orthogonal multiple access (NOMA) has become a promising technology for next-generation wireless communications systems due to its capability to provide access for multiple users on the same resource. In this paper, we consider an uplink power-domain NOMA system aided by a reconfigurable intelligent surface (RIS) in the presence of a jammer that aims to maximize its interference on the base station (BS) uplink receiver. We consider two kinds of RISs, a regular RIS whose elements can only change the phase of the incoming wave, and an RIS whose elements can also attenuate the incoming wave. Our aim is to minimize the total power transmitted by the user terminals under quality-of-service constraints by controlling both the propagation from the users and the jammer to the BS with help of the RIS. The resulting objective function and constraints are both non-linear and non-convex, so we address this problem using numerical optimization. Our numerical results show that the RIS can help to dramatically reduce the per user required transmit power in an interference-limited scenario.

Integrated sensing and communication (ISAC) is a key enabler for next-generation wireless communication systems to improve spectral efficiency. However, the coexistence of sensing and communication functionalities can cause harmful interference. In this paper, we propose to use a reconfigurable intelligent surface (RIS) in conjunction with ISAC to address this issue. The RIS is composed of a large number of low-cost elements that can adjust the amplitude and phase shift of impinging signals, thus providing a relatively high beamforming gain. To maximize the sum-rate of the communication system, we jointly optimize the beamformer at the base station (BS) and the phase shifts at the RIS, subject to a threshold on the interference power, the unit-norm constraint of the transmit power, and the unit modulus constraint of the RIS phase shifts. To efficiently tackle this NP-hard problem, we first reformulate the problem into a more tractable form using the fractional programming (FP) technique. Then, we exploit the geometrical properties of the constraints and adopt an alternating manifold-based optimization to compute the optimal active beamformer and the RIS phase shifts, respectively. Simulation results demonstrate that the proposed RIS-assisted design significantly reduces the mutual interference and improves the system sum-rate for the communication system.

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize "open-set" identification. With the help of its scalable structure and "transfer learning," new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy.

In this paper, we consider a beamforming vector design and device selection problem in over-the-air computation (AirComp) for federated learning. Since the learning performance improves as more devices participate in the federated learning aggregation, we formulate a beamforming vector optimization problem that maximizes the number of selected devices under a given target aggregation mean-squared error. This AirComp uplink beamforming problem with device selection is shown to have the same form as the downlink multicast beamforming problem with user selection, which establishes the AirComp-multicasting duality. We design a low-complexity algorithm based on the projected subgradient method that is orders of magnitude faster than conventional semidefinite relaxation-based algorithms and faster than local model training on the devices, which makes it possible to implement the proposed wireless federated learning in real time. Numerical results show that the proposed algorithm provides significant multiple antenna beamforming gains and achieves the performance of the ideal federated learning system with no aggregation errors.

In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.

Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of domain knowledge in the few-bit quantization process. While the first data detection network, B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the indeterminant gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into the weight matrices of its channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gains in estimation accuracy by the proposed deep learning framework.

This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems in the presence of hardware impairments (HIs) at the RIS and the transceivers. We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum approximate ergodic secrecy rate (WMAESR), subject to the transmission power constraints at the BS and unit-modulus constraints at the RIS. To solve the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem by invoking the penalty convex–concave procedure (CCP) method and the closed-form fractional programming (FP) criterion, and then directly solves them by using CVX. The second method leverages the minorization-maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems that admit closed-form solutions. Simulation results verify the performance gains of the proposed robust transmission methods over existing non-robust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.

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

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.

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

The implementation of full-duplex (FD) theoretically doubles the spectral efficiency of cellular communications. We propose a multiuser FD cellular network relying on an intelligent reflecting surface (IRS). The IRS is deployed to cover a dead zone while suppressing user-side self-interference (SI) and co-channel interference (CI) by carefully tuning the phase shifts of its massive low-cost passive reflection elements. To ensure network fairness, we aim to maximize the weighted minimum rate (WMR) of all users by jointly optimizing the precoding matrix of the base station (BS) and the reflection coefficients of the IRS. Specifically, we propose a low-complexity minorization-maximization (MM) algorithm for solving the subproblems of designing the precoding matrix and the reflection coefficients, respectively. Simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to realize FD cellular communications.

Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately, low-resolution ADCs significantly distort the received signals, and thus make data detection much more challenging. In this paper, we develop a new deep neural network (DNN) framework for efficient and low-complexity data detection in low-resolution massive MIMO systems. Based on reformulated maximum likelihood detection problems, we propose two model-driven DNN-based detectors, namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems, respectively. The proposed OBMNet and FBMNet detectors have unique and simple structures designed for low-resolution MIMO receivers and thus can be efficiently trained and implemented. Numerical results also show that OBMNet and FBMNet significantly outperform existing detection methods.

In this paper, we develop a wireless physical-layer surveillance scheme where two devices (M1and M2) work cooperatively to eavesdrop on and intervene in a suspicious transmission link from a source (S) to a destination (D). Unlike existing approaches which rely on the use of a multi-antenna fullduplex radio as the monitor, in our scheme, M1and M2are both single-antenna nodes operating in half-duplex mode. Within any odd time slot, M1sends a jamming signal to deteriorate the signal reception at D and M2eavesdrops on the transmission from S. During the next (even) slot, M1overhears the signal sent from S, and M2forwards its received signal during the previous slot to realize jamming. In this manner, the jamming signal received at M1can be perfectly removed after self-interference cancellation, and the signals from S during the two consecutive slots are jointly decoded with high reliability, thus enabling successful surveillance. On the other hand, the detection performance at D is heavily degraded due to the injection of the jamming signal, thereby preventing information leakage from S to D.

Security is a primary concern in Internet-of-Things (IoT) applications, and biometric recognition is considered to be a promising solution. In this article, we propose a novel electrocardiogram (ECG)-based biometric recognition scheme that can potentially strengthen the security of IoT-based patient monitoring systems. A biometric system is designed to operate in either verification or identification mode, and we concentrate on applying the proposed approach to the latter due to its difficulty and popularity in existing studies. Through the concept of “subspace oversampling,” we are able to create distinct and irreversible templates for an enrollee to avoid the cross-matching problem and privacy invasion. With the help of “subspace matching,” the identity of unknown subjects can be determined using only their beat bundles without any additional information required for template construction. Moreover, the proposed scheme includes a method for unregistered subject exclusion to avoid falsely linking an initially unidentifiable subject to someone in the database, further strengthening its security. The performance of the proposed scheme was evaluated using the ECGs of 287 subjects from the Physikalisch Technische Bundesanstalt data set. The experimental results demonstrated the linkability of the constructed templates as low as 0.0938, and beat bundles reconstructed from the templates of a given subject were more likely to be identified as those from another user. An identification rate of 99.02% was obtained even when the proposed exclusion scheme was incorporated; meanwhile, the corresponding false-positive identification error rate was 0.44% under a dictionary attack with real ECGs.

The Viterbi algorithm is widely adopted in digital communication systems because of its capability of realizing maximum-likelihood signal sequence detection. However, implementation of the Viterbi algorithm requires instantaneous channel state information (CSI) to be available at the receiver. This is difficult to satisfy in some emerging communication systems such as molecular communications, underwater optical communications, etc, where the underlying channel models are highly complex or completely unknown. ViterbiNet, developed in the prior literature, is a promising framework to cope with this challenge, where deep learning (DL) techniques are combined with the Viterbi Algorithm to enable near-optimal signal detection without CSI. This paper offers a non-trivial variation of ViterbiNet based on generative adversarial networks (GAN). Specifically, a novel architecture using GAN is designed to directly learn the channel transition probability (CTP) from receiver observations, which is the only part of the Viterbi algorithm that is channel-dependent. With the learned CTP, the classical Viterbi algorithm can be implemented without modifications. To make the proposed architecture applicable to time-varying channels, we further develop two methods to fine-tune the learned CTP online. In the first method, pilots within each frame are exploited to update the CTP learning network; In the second method, a decision-directed approach is devised to generate training data in real-time, which is utilized to re-train the learning network. By combining these two approaches, the receiver is able to track the dynamic channel conditions without being trained from scratch. Numerical simulations demonstrate the superiority of the proposed design compared to existing methods.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

We study closed-form interference-exploitation precoding for multi-level modulations in the downlink of multi-user multiple-input single-output (MU-MISO) systems. We consider two distinct cases: first, when the number of served users is not larger than the number of transmit antennas at the base station (BS), we mathematically derive the optimal precoding structure based on the Karush-Kuhn-Tucker (KKT) conditions. By formulating the dual problem, the precoding problem is transformed into a pre-scaling operation using quadratic programming (QP) optimization. We further consider the case where the number of served users is larger than the number of transmit antennas at the BS. By employing the pseudo inverse, we show that the optimal solution of the pre-scaling vector is equivalent to a linear combination of the right singular vectors corresponding to zero singular values, and derive the equivalent QP formulation. We also present the condition under which multiplexing more streams than the number of transmit antennas is achievable. For both considered scenarios, we propose a modified iterative algorithm to obtain the optimal precoding matrix, as well as a sub-optimal closed-form precoder. Numerical results validate our derivations on the optimal precoding structures for multi-level modulations, and demonstrate the superiority of interference-exploitation precoding for both scenarios.

In this article, we develop an alternate-jamming-aided wireless physical-layer surveillance protocol where two devices (M1and M2) work cooperatively to eavesdrop on and intervene in a suspicious transmission link from a source (S) to a destination (D). Unlike existing approaches which rely on the use of a multi-antenna full-duplex radio as the monitor, in our protocol, M1and M2are both single-antenna nodes operating in half-duplex mode, which alternately perform proactive eavesdropping and jamming to mimic the behavior of a full-duplex monitor. Within any time slot, M1sends a jamming signal to deteriorate the signal reception at D and M2eavesdrops on the transmission from S. During the next slot, M1overhears the signal sent from S, and M2forwards its received signal during the previous slot to realize jamming. In this manner, the jamming signal received at M1can be perfectly removed after self-interference cancellation, and the signals from S during the two consecutive slots are jointly decoded with high reliability, thus enabling successful surveillance. On the other hand, the detection performance at D is heavily degraded due to the injection of the jamming signal, thereby preventing information leakage from S to D. The performance of the proposed protocol is analyzed in terms of the eavesdropping non-outage probability, the surveillance success probability, as well as the symbol error probability. Theoretical analysis and simulation results demonstrate the superiority of our design compared to competing solutions in the literature.

Radio-frequency fingerprints (RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods can not be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization (NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.

Based on the concept of constructive interference (CI), multiuser interference (MUI) has recently been shown to be beneficial for communication secrecy. A few CI-based secure precoding algorithms have been proposed that use both the channel state information (CSI) and knowledge of the instantaneous transmit symbols. In this article, we examine the CI-based secure precoding problem with a focus on smart eavesdroppers that exploit statistical information gleaned from the precoded data for symbol detection. Moreover, the impact of correlation between the main and eavesdropper channels is taken into account. We first modify an existing CI-based precoding scheme to better utilize the destructive impact of the interference. Then, we point out the drawback of both the existing and the new modified CI-based precoders when faced with a smart eavesdropper. To address this deficiency, we provide a general principle for precoder design and then give two specific design examples. Finally, the scenario where the eavesdropper's CSI is unavailable is studied. Numerical results show that although our modified CI-based precoder can achieve a better energy-secrecy trade-off than the existing approach, both have a limited secrecy benefit. On the contrary, the precoders developed using the new CI-design principle can achieve a much improved tradeoff and significantly degrade the eavesdropper's performance.

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

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.

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we present several linear receivers based on the Bussgang decomposition that show significant performance gains over conventional linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based detector, namely OBMNet, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.

While constructive interference offers indirect advantages in physical layer security by reducing the transmit power required to achieve a desired performance level, additional gains are possible by choosing the symbols to degrade the eavesdropper's ability to decode the desired data. An algorithm was recently proposed for this purpose, but it assumes an eavesdropper that employs simple nearest-neighbor decoding, and that only exploits a portion of the space available for destructive interference (DI). In this paper, we modify the technique to exploit the full DI region, but we show that even with this improvement, the general approach is vulnerable to an intelligent eavesdropper who can perform maximum likelihood detection. Based on this observation, we propose an alternative approach that, while requiring increased transmit power, can yield the best possible security.

Massive MIMO using low-resolution digital-to-analog converters (DACs) at the base station (BS) is an attractive downlink approach for reducing hardware overhead and for reducing power consumption, but managing the large quantization noise effect is a challenge. Spatial Sigma-Delta (ΣΔ) modulation is a recently emerged technique for tackling the aforementioned effect. Assuming a uniform linear array at the BS, it works by shaping the quantization noise as high spatial-frequency, or angle, noise. By restricting the user-serving region to be within a smaller angular region, the quan-tization noise incurred by the users can be effectively reduced. We previously showed that, under the one-bit DAC case, the quantization noise can be satisfactorily contained using a simple first-order ΣΔ modulation scheme. In this work we study the potential of spatial ΣΔ modulation in the two-bit DAC case and under second-order modulation. Our empirical results indicate that second-order spatial ΣΔ modulation provides better quantization noise suppression.

Low-resolution Analog-to-Digital Converters (ADCs) have emerged as a practical solution for reducing cost and power consumption for massive Multiple-Input Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation is formulated as an SVM problem, and then a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. Finally, we propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

To enable the low-cost ubiquitous Internet of Things, passive backscatter communication is envisioned as one of the most prominent and promising techniques; however, the underlying security issues associated with practical finite-alphabet signaling from the perspective of physical-layer security (PLS) have not been well studied. Despite several preliminary efforts on improving the eavesdropper's decoding error probability through PLS approaches, this article comprehensively investigates the secrecy rate performance of a secure multiantenna radio-frequency identification (RFID) system with a finite-alphabet input at the RFID tag. Unlike conventional noise-injection schemes, a randomized continuous wave (CW) signal is exploited at the RFID reader for security enhancement, and an analytical framework is proposed to evaluate the impact of exploiting either full or only statistical knowledge of the randomized CW signal at the reader and the eavesdropper, respectively. The secrecy rate is maximized by designing the transmitted randomized CW signal to tackle the stability-variance tradeoff between balancing legitimate signal reception and eavesdropper mitigation. In particular, we show that the proposed scheme also poses a tradeoff between the received additive and multiplicative noise at the eavesdropper for the special case of a single-antenna eavesdropper. Moreover, the more practical case where the eavesdropper's instantaneous channel state information is unavailable is studied under different fading conditions. The numerical results verify the accuracy of the proposed approximations and show that introducing a small variance into the CW signal can greatly improve the system secrecy.

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.

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta (ΣΔ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the ΣΔ approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the ΣΔ array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the ΣΔ approach for both MRC and zero-forcing receivers.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., “entropy”, introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations.

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.

We analyze the performance of multiple-input multiple-output (MIMO) links with one-bit output quantization in terms of achievable rates and characterize their performance loss compared to unquantized systems for general channel statistical models and general channel state information (CSI) at the receiver. One-bit ADCs are particularly suitable for large-scale millimeter wave MIMO Communications (massive MIMO) to reduce the hardware complexity. In such applications, the signal-to-noise ratio per antenna is rather low due to the propagation loss. Thus, it is crucial to analyze the performance of MIMO systems in this regime by means of information-theoretical methods. Since an exact and general information-theoretic analysis is not possible, we resort to the derivation of a general asymptotic expression for the mutual information in terms of a second-order expansion around zero SNR. We show that up to second order in the SNR, the mutual information of a system with two-level (sign) output signals incorporates only a power penalty factor of π/2 (1.96 dB) compared to systems with infinite resolution for all channels of practical interest with perfect or statistical CSI. An essential aspect of the derivation is that we do not rely on the common pseudo-quantization noise model.

The use of low-resolution analog-to-digital converters (ADCs) is considered to be an effective technique to reduce the power consumption and hardware complexity of wireless transceivers. However, in systems with low-resolution ADCs, obtaining channel state information (CSI) is difficult due to significant distortions in the received signals. The primary motivation of this paper is to show that learning techniques can mitigate the impact of CSI unavailability. We study the blind detection problem in multiple-input-multiple-output (MIMO) systems with low-resolution ADCs using learning approaches. Two methods, which employ a sequence of pilot symbol vectors as the initial training data, are proposed. The first method exploits the use of a cyclic redundancy check (CRC) to obtain more training data, which helps improve the detection accuracy. The second method is based on the perspective that the to-be-decoded data can itself assist the learning process, so no further training information is required except the pilot sequence. For the case of 1-bit ADCs, we provide a performance analysis of the vector error rate for the proposed methods. Based on the analytical results, a criterion for designing transmitted signals is also presented. Simulation results show that the proposed methods outperform existing techniques and are also more robust.

This paper investigates unmanned aerial vehicle (UAV)- aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver using transmit- backscatter (TB) protocol. Our goal is to maximize the system ergodic capacity by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. The resulting optimization problem is shown to be non-convex, which is challenging to solve. We consider two different circuit power consumption models, namely dynamic and static models. We first consider the dynamic model, and decompose the original problem into three sub- problems, and an iterative algorithm is proposed to optimize three subproblems alternately by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity using the Lagrangian dual method. Simulation results show that the proposed scheme is able to achieve significant throughput gains over the compared benchmarks.

In this paper, we propose a novel zero-forcing (ZF)- based beamforming (BF) scheme for a wireless powered cognitive satellite-terrestrial network (CSTN) operated in the millimeter wave band. Assuming that the satellite and base station are equipped with multiple antennas, we aim at maximizing the sum rate of the CSTN while satisfying the signal-to-interference-plus-noise- ratio requirements for both the information receivers (IRs) and earth stations, the energy harvesting requirements of the energy receivers (ERs), and the secrecy constraints at the ERs. Since the resulting optimization problem is mathematically intractable, we propose a novel multi-beam-based ZF BF scheme to generate beamforming vectors to serve the IRs and ERs. Specifically, the original nonconvex problem is decomposed into two independent subproblems. The first subproblem, which features beam orthogonality constraints, leads to closed form solutions for the beamforming vectors. The second subproblem, aiming at finding the optimal power allocation, is solved via the S-procedure. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results.

We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (ΣΔ) analogto-digital converter (ADC) array. The ΣΔ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed ΣΔ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs.

In this paper, we investigate secure transmission in a massive multiple-input multiple-output system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ, low-resolution DACs typically result in a secrecy rate loss, but in certain cases, they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ, low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ, the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated by reducing the AN power. All the derived analytical results are verified by numerical simulations.

Relying on physiological or behavioral traits for identity recognition, biometric technologies offer several advantages over conventional possession- and knowledge-based approaches and are now widely used in diverse applications. However, as most biometrics (e.g., fingerprints, irises, etc.) in use are extrinsic, susceptible to replay attacks, and could result in the disclosure of individuals' physiological and pathological conditions, security and privacy concerns must be considered. In this paper, several electrocardiogram (ECG)-based cancelable biometric schemes are proposed to mitigate such concerns. The intrinsic and dynamic nature of ECGs and their inherent indication of life make them extremely difficult to steal or counterfeit. Using the concept of “signal subspace collapsing,” distinct biometric templates associated with an individual's ECGs can be constructed such that it is possible to revoke a compromised template like a password. By incorporating different strategies for common subspace suppression, the well-known multiple signal classification method can effectively determine the identity of any individual just via his/her ECGs. Unlike existing cancelable biometrics, the recognition can be accomplished without knowledge of the distortion transformation, which further increases the difficulty of recovering the original ECGs from their templates. Various experiments with real ECGs from 285 subjects are conducted to illustrate the efficacy of the proposed schemes.

In this paper, we study delay-intolerant covert communications in additive white Gaussian noise (AWGN) channels with a finite block length, i.e., a finite number of channel uses. Considering the maximum allowable number of channel uses to be N, it is not immediately clear whether the actual number of channel uses, denoted by n, should be as large as N or smaller for covert communications. This is because a smaller n reduces a warden's chance to detect the communications due to fewer observations, but also reduces the chance to transmit information. We show that n = N is indeed optimal to maximize the amount of information bits that can be transmitted, subject to any covert communication constraint in terms of the warden's detection error probability. To better make use of the warden's uncertainty due to the finite block length, we also propose to use uniformly distributed random transmit power to enhance covert communications. Our examination shows that the amount of information that can be covertly transmitted logarithmically increases with the number of random power levels, which indicates that most of the benefit of using random transmit power is achieved with just a few different power levels.

In this contribution, we investigate a coarsely quantized multi-user multiple-input single-output downlink communication system, where we assume 1-bit digital-to-analog converters at the base station antennas. First, we analyze the achievable sum rate lower-bound using the Bussgang decomposition under new assumptions. In the presence of the non-linear quantization, our analysis indicates the potential merit of reconsidering traditional signal processing techniques in coarsely quantized systems, i.e., reconsidering transmit covariance matrices whose rank is equal to the rank of the channel. Furthermore, in the latter part of this paper, we propose a linear precoder design that achieves the predicted increase in performance compared with a state-of-the-art linear precoder design. Moreover, our linear signal processing algorithm allows for higher order modulation schemes to be employed.

Recently a number of nonlinear precoding algorithms have been developed for designing a downlink transmit signal that is constrained by some nonlinearity, such as one-bit quantization, power-amplifier saturation or constant modulus. These methods use iterative search algorithms to directly design the signal that is transmitted from each antenna. Since the dimension of the search space equals the number of antennas, the computational complexity of these approaches can be high for massive MIMO scenarios. Thus, in this paper we pose the problem in a smaller dimensional space by constraining the signal prior to the nonlinearity to be the output of a linear precoder. The search is then over the vector of predistorted symbols at the input to the linear precoder, which is typically much smaller than the number of antennas. We focus on algorithms that minimize the bit error rate at the receivers, and show that performance can be obtained that is similar to algorithms that operate directly in the antenna domain.

We consider a multipair half-duplex relay communication network, where the relay is deployed with one-analog-to-digital converters and one-bit digital-to-analog converters. To suppress the interpair interference and quantization artifacts, we propose nonlinear precoding schemes to forward the quantized signals at the relay. We first present a technique based on gradient projection, and then show how to refine the solution using ordered quantization and perturbation methods. For the single-user case with BPSK symbols, we obtain a closed-form solution for the optimal transmit vector. Numerical results verify that the proposed precoding design significantly outperforms quantized linear precoding strategies.

This paper proposes a new channel training (CT) scheme for a full-duplex receiver to enhance physical layer security. Equipped with NBfull-duplex antennas, the receiver simultaneously receives the information signal and transmits artificial noise (AN). In order to reduce the non-cancellable self-interference due to the transmitted AN, the receiver has to estimate the self-interference channel prior to the data communication phase. In the proposed CT scheme, the receiver transmits a limited number of pilot symbols that are known only to itself. Such a secret CT scheme prevents an eavesdropper from estimating the jamming channel from the receiver to the eavesdropper, hence effectively degrading the eavesdropping capability. We analytically examine the connection probability (i.e., the probability of the data being successfully decoded by the receiver) of the legitimate channel and the secrecy outage probability due to eavesdropping for the proposed secret CT scheme. Based on our analysis, the optimal power allocation between CT and data/AN transmission at the legitimate transmitter/receiver is determined. Our examination shows that the newly proposed secret CT scheme significantly outperforms the non-secret CT scheme that uses publicly known pilots when the number of antennas at the eavesdropper is larger than one.

Coarsely quantized massive multiple-input multiple-output (MIMO) systems are gaining more interest due to their power efficiency. We present a new precoding technique to mitigate the multi-user interference and the quantization distortions in a downlink multi-user MIMO system with coarsely quantized constant envelope (QCE) signals at the transmitter. The transmit signal vector is optimized for every desired received vector taking into account a relaxed version of the QCE constraint. The optimization is based on maximizing the safety margin to the decision thresholds of the receiver constellation modulation. Due to the linear property of the objective function and the constraints, the optimization problem is formulated as a linear programming problem. The simulation results show a significant gain in terms of the uncoded bit error rate compared to the existing precoding techniques.

We study multi-user massive multiple-input single-output systems and focus on downlink transmission for PSK modulation, where the base station employs a large antenna array with low-cost 1-bit digital-to-analog converters (DACs). The direct combination of existing beamforming schemes with 1-bit DACs is shown to lead to an error floor at medium-to-high SNR regime, due to the coarse quantization of the DACs with limited precision. In this paper, based on the constructive interference, we consider both a quantized linear beamforming scheme where we analytically obtain the optimal beamforming matrix and a non-linear mapping scheme where we directly design the transmit signal vector. Due to the 1-bit quantization, the formulated optimization for the non-linear mapping scheme is shown to be non-convex. The non-convex constraints of the 1-bit DACs are first relaxed into convex, followed by an element-wise normalization to satisfy the 1-bit DAC transmission. We further propose a low-complexity symbol scaling scheme that consists of three stages, in which the quantized transmit signal on each antenna element is selected sequentially. Numerical results show that the proposed symbol scaling scheme achieves a comparable performance to the optimization-based non-linear mapping approach, while the corresponding performance-complexity tradeoff is more favorable for the proposed symbol scaling method.

In this contribution, we provide an information theoretical analysis of coarsely-quantized downlink Single-User (SU)- Multiple Input Single Output (MISO) communication systems. We address the question of whether traditional signal processing techniques, i.e., proper signaling and channel rank transmit covariance matrices, are still optimal with respect to maximizing the data rate. We investigate the mutual information lower bound based on the Bussgang theorem, in the SU-MISO downlink scenario, where we assume 1-bit quantized Digital-to-Analog Converters (DACs) in the transmit antennas at the Base Station (BS). We prove that at low Signal-to-Noise Ratio (SNR), existing signal processing techniques maximize the data rate. However, at higher SNR we show, using counter examples, that the data rates can be improved using different signal processing techniques. These results show the potential merit of reconsidering signal processing techniques in coarsely- quantized SU-MISO downlink scenarios.

We study the impact of using one-bit analog-to-digital and digital-to-analog converters in a multipair amplify-and-forward MIMO relaying system. The relay estimates the channel state information using training data, and then uses the channel estimate to perform maximum ratio combining and maximum ratio transmission. An exact achievable rate is derived for the system under general assumptions on the quantization noise, and then a closed-form asymptotic approximation is derived, which enables efficient evaluation of the impact of key parameters on system performance. Contrary to the conventional unquantized systems, the performance is seen to depend on the specific pilot sequences that are employed. In addition, the sum rate gap between the double quantized relay system and an ideal unquantized system is shown to be a factor of 4/π2in the low source power regime.

Linear precoders have been shown to perform reasonably well at low SNR when the basestation of a MIMO downlink employs one-bit digital-to-analog converters to quantize the precoder outputs. However, at medium-to-high SNRs, an error floor is encountered due to the coarse quantization. This paper examines methods for slightly perturbing the transmitted signal prior to quantization in an effort to improve downlink performance at higher SNRs. The perturbation is performed with the goal of minimizing the worst-case probability of error among the user terminals, and assumes that the symbols to be transmitted are drawn from a finite alphabet constellation. Two different types of perturbations are studied, and it is found via simulation that the methods can provide dramatic gains in downlink performance.

In this work, we propose a new channel training (CT) scheme to enhance physical layer security in a full-duplex wiretap channel, where the multi-antenna and full-duplex receiver simultaneously receives the information signal and transmits artificial noise (AN). In order to suppress the self-interference caused by AN, the receiver has to estimate the self-interference channel prior to the data communication phase. In the proposed CT scheme, the receiver transmits limited pilot symbols which are known only to itself, which prevents the eavesdropper from estimating the jamming channel from the receiver to the eavesdropper, hence effectively degrades the eavesdropping capability. Compared with the traditional CT scheme that uses publicly known pilots, the newly proposed secret CT scheme offers significantly better performance when the number of antennas at the eavesdropper is larger than one, e.g., Ne> 1. The optimal power allocation between CT and data/AN transmission at the legitimate transmitter/receiver is determined for the proposed secret CT scheme.

We consider uplink transmission for a massive multiuser multiple-input multiple-output (MU-MIMO) system in which the base station (BS) is equipped with a mixed analog-to-digital converter (ADC) architecture. In this architecture, a portion of the antennas at the BS is connected to low-power one-bit ADCs while the other is fed to power-hungry high-resolution ADCs. By taking into account the mixed-ADC architecture, we derive a closed-form expression for the sum spectral efficiency (SE) when maximum ratio combining (MRC) detection is employed at the BS. Then, we formulate an optimization problem to determine, for a given power budget at the BS, what is the optimal distribution of one-bit and high-resolution ADCs that maximizes the sum SE. Interestingly, it is shown that in most realistic scenarios, using only one-bit ADCs provides the best spectral efficiency for a given power budget constraint.

In this paper, we consider the achievable sum-rate/distortion tradeoff for the Gaussian central estimation officer (CEO) problem with a scalar source having arbitrary memory. We describe how the arbitrary memory problem can be fully characterized by using known results for the vector CEO problem, and then we formulate the variational problem of minimizing the sum-rate subject to a distortion constraint. To solve the problem, we extend the conventional Lagrange method and show that if the solution exists, it should consist of a zero part and a non-zero part, where the non-zero part is determined by solving a set of Euler equations. By calculating the second variation of the min-sum-rate problem, a sufficient condition is also found that can be used to determine if the necessary solution results in the minimal sum rate. The special case of two terminals is examined in detail, and it is shown that an analytical solution is possible in this case. Analysis and discussion with examples are provided to illustrate the theoretical results. The general solution obtained in this paper is shown to be compatible with the previous results for cases such as the problem of rate evaluation for sources without memory.

This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

In this paper, we study the energy efficiency of secure communication in an underlay cognitive radio network (CRN). We first formulate an optimization problem to maximize the secrecy energy efficiency (SEE) while meeting the quality-of-service (QoS) requirement for the primary user and the transmit power constraint at each base station. Since the problem is non-convex and very difficult to solve, we then convert the original fractional form into a subtractive one, and adopt the difference of two-convex functions (D.C.) approximation method to obtain an equivalent convex problem. Furthermore, a two-layer iterative algorithm is presented to solve the problem and obtain the optimal beamforming (BF) weight vectors. Finally, numerical results are provided to demonstrate the superiority of the proposed scheme.

Theoretical studies on physical layer security often adopt the secrecy outage probability as the performance metric for wireless communications over quasi-static fading channels. The secrecy outage probability has two limitations from a practical point of view: 1) it does not give any insight into the eavesdropper's decodability of confidential messages and 2) it cannot characterize the amount of information leakage to the eavesdropper when an outage occurs. Motivated by the limitations of the secrecy outage probability, we propose three new secrecy metrics for secure transmissions over quasi-static fading channels. The first metric establishes a link between the concept of secrecy outage and the decodability of messages at the eavesdropper. The second metric provides an error-probability-based secrecy metric which is typically used for the practical implementation of secure wireless systems. The third metric characterizes how much or how fast the confidential information is leaked to the eavesdropper. We show that the proposed secrecy metrics collectively give a more comprehensive understanding of physical layer security over fading channels and enable one to appropriately design secure communication systems with different views on how secrecy is measured.

Secure relay and jammer selection for physical-layer security is studied in a wireless network with multiple intermediate nodes and eavesdroppers, where each intermediate node either helps to forward messages as a relay, or broadcasts noise as a jammer. We derive a closed-form expression for the secrecy outage probability (SOP), and we develop two relay and jammer selection methods for SOP minimization. In both methods a selection vector and a corresponding threshold are designed and broadcast by the destination to ensure each intermediate node knows its own role while knowledge of the relay and jammer set is kept secret from all eavesdroppers. Simulation results show the SOP of the proposed methods are very close to that obtained by an exhaustive search, and that maintaining the privacy of the selection result greatly improves the SOP performance.

The integration of cooperative transmission into satellite networks is regarded as an effective strategy to increase the energy efficiency as well as the coverage of satellite communications. This paper investigates the performance of an amplify-and-forward (AF) hybrid satellite-terrestrial relay network (HSTRN), where the links of the two hops undergo Shadowed-Rician and Rayleigh fading distributions, respectively. By assuming that a single antenna relay is used to assist the signal transmission between the multi-antenna satellite and multi-antenna mobile terminal, and multiple interferers corrupt both the relay and destination, we first obtain the equivalent end-to-end signal-to-interference-plus-noise ratio (SINR) of the system. Then, an approximate yet very accurate closed-form expression for the ergodic capacity of the HSTRN is derived. The analytical lower bound expressions are also obtained to efficiently evaluate the outage probability (OP) and average symbol error rate (ASER) of the system. Furthermore, the asymptotic OP and ASER expressions are developed at high signal-to-noise ratio (SNR) to reveal the achievable diversity order and array gain of the considered HSTRN. Finally, simulation results are provided to validate of the analytical results, and show the impact of various parameters on the system performance.

We consider using a buffer-aided relay to enhance security for two-hop half-duplex relay networks with an external eavesdropper. We propose a link selection scheme that adapts reception and transmission time slots based on channel quality, while considering both the two-hop transmission efficiency and security. Closed-form expressions for the secrecy throughput and the secrecy outage probability (SOP) are derived, and the selection parameters are optimized to maximize the secrecy throughput or minimize the SOP. We then analyze two sub-optimal link selection schemes that in general only require a line search to solve the optimization problem, and we show that, under certain conditions, these approaches also admit closed-form solutions. All schemes are discussed in the context of two different scenarios where the relay either knows or does not know the channel to the legitimate receiver. In the former case, the relay adopts adaptive-rate transmission, whereas for the latter, it uses fixed-rate transmission. Numerical results show that buffer-aided relaying provides a significant improvement in security compared with conventional unbuffered relaying. Furthermore, the performance of the sub-optimal schemes is shown to approach the optimal one for certain ranges of signal-to-noise-ratio (SNR) or SOP constraints.

In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.

The full potential of Device-to-device (D2D) communication relies on efficient resource reuse strategies including power control and matching of D2D links and cellular users (CUs). This letter investigates downlink resource reuse between multiple D2D links and multiple CUs. Our goal is to achieve a network utility enhancement for D2D communication while ensuring the QoS of the CUs. Despite the combinatorial nature of the problem and the coupled power constraints, we characterize the optimal D2D-CU matching as well as their power coordination, and propose an efficient algorithm to jointly optimize all D2D links and CUs. The proposed downlink resource reuse strategy shows a superiority over existing D2D schemes.

In recent years, radio positioning has received increasing attention and found many applications in various areas. However, the existence of non-line-of-sight (NLOS) paths introduces considerable positioning errors. In this paper, we propose a two-step approach in order to deal with pure NLOS scenarios based on a simple assumption regarding the propagation environment. A nonlinear least squares (NLS) method is proposed for the initial estimation, followed by a Kalman filter-based method to track subsequent movements. Compared with previous studies, fewer measurements are required to be made. Simulation results are provided to show the performance of both methods.

In this paper, we consider a dynamic linear system in statespace form where the observation equation depends linearly on a set of parameters. We address the problem of how to dynamically calculate these parameters in order to minimize the mean-squared error (MSE) of the state estimate achieved by a Kalman filter. We formulate and solve two kinds of problems under a quadratic constraint on the observation parameters: minimizing the sum MSE (Min-Sum-MSE) or minimizing the maximum MSE (Min-Max-MSE). In each case, the optimization problem is divided into two sub-problems for which optimal solutions can be found: a semidefinite programming (SDP) problem followed by a constrained least-squares minimization. A more direct solution is shown to exist for the special case of a scalar observation; in particular, the Min-Sum-MSE problem is optimally solved utilizing Rayleigh quotient, and the Min-Max-MSE problemreduces to an SDP feasibility test that can be solved via the bisection method.

This paper addresses the problem of multipath mitigation with GNSS antenna arrays. A beamformer that is able to cancel the multipath components regardless of their relative delay and directions of arrival is proposed. The weights are obtained from a set of spatial correlation matrices that allows us to estimate the multipath subspace. These matrices are generated after a FIR filter that reduces the correlation between the multipath components and the line-of-sight signal, and it is only used for spatial processing. Some representative simulation results show the multipath attenuation provided by the proposed method under different conditions.

The design of pilot and data power allocations for multicarrier OFDM signals is a key aspect in the development of combined positioning and high-data-rate communications systems. In this paper, we investigate capacity-maximizing pilot and data power allocations when a certain positioning accuracy is required. We consider a formulation based on the Expected Crámer-Rao Bound of the joint time-delay and channel estimation and the ergodic capacity, modeling the channel impulse response as a random vector. We compare the performance of capacity-maximizing pilot and data power distributions with respect to distributions that use equi-spaced and equi-powered pilot structures, shown by previous work to be optimal in terms of channel estimation. Numerical results show that the restriction to the use of equi-spaced and equi-powered pilot structures has an important impact on both the achievable capacity and the positioning capabilities of the designed signals.

We study the optimality of linear precoding for the two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel (BC) with confidential messages. Secret dirty-paper coding (S-DPC) is optimal under an input covariance constraint, but there is no computable secrecy capacity expression for the general MIMO case under an average power constraint. In principle, for this case, the secrecy capacity region could be found through an exhaustive search over the set of all possible matrix power constraints. Clearly, this search, coupled with the complexity of dirty-paper encoding and decoding, motivates the consideration of low complexity linear precoding as an alternative. We prove that for a two-user MIMO Gaussian BC under an input covariance constraint, linear precoding is optimal and achieves the same secrecy rate region as S-DPC if the input covariance constraint satisfies a specific condition, and we characterize the corresponding optimal linear precoders. We then use this result to derive a closed-form sub-optimal algorithm based on linear precoding for an average power constraint. Numerical results indicate that the secrecy rate region achieved by this algorithm is close to that obtained by the optimal S-DPC approach with a search over all suitable input covariance matrices.

The main goal of this special issue is to gather state-of-the art-contributions that address such challenges as they pertain to the design, analysis, and optimization of physical layer security in next-generation networks.

This work examines a novel heterogeneous dynamic spectrum access network where the primary users (PUs) coexist with both underlay and interweave cognitive transmitters (UCTs and ICTs); all terminals being potentially equipped with multiple antennas. UCTs are allowed to transmit concurrently with PUs subject to interference constraints, while the ICTs employ spectrum sensing and are permitted to access the shared spectrum only when both PUs and UCTs are absent. We investigate the design of MIMO precoding algorithms for the UCT that increase the detection probability at the ICTs, while simultaneously meeting a desired Quality-of-Service target to the underlay cognitive receivers (UCRs) and constraining interference leaked to PUs. The objective of such a proactive approach, referred to as prescient precoding, is to minimize the probability of interference from ICTs to the UCRs and primary receivers due to imperfect spectrum sensing. We begin with downlink prescient precoding algorithms for multiple single-antenna UCRs and multi-antenna PUs/ICTs. We then present prescient block-diagonalization algorithms for the MIMO underlay downlink where spatial multiplexing is performed for a plurality of multi-antenna UCRs. Numerical experiments demonstrate that prescient precoding by UCTs provides a pronounced performance gain compared to conventional underlay precoding strategies.

This paper studies a novel underlay MIMO cognitive radio (CR) network where the instantaneous or statistical channel state information (CSI) of the interfering channels to the primary receivers (PRs) is completely unknown to the CR. We first show that low-rank CR interference is preferable for improving the throughput of the PRs compared with spreading less power over more transmit dimensions. Based on this observation, we then propose a rank minimization CR transmission strategy assuming a minimum information rate must be guaranteed on the CR main channel. We propose a simple solution referred to as frugal waterfilling (FWF) that uses the least amount of power required to achieve the rate constraint with a minimum-rank transmit covariance matrix. We also present two heuristic approaches that have been used in prior work to transform rank minimization problems into convex optimization problems. We demonstrate that the direct FWF solution leads to higher PR throughput even though it has higher interference “temperature” (IT) compared with the heuristic methods. This calls into question the use of IT as a metric for CR interference.

In wireless sensor networks, many monitoring problems can be cast in the form of distributed estimation. If the data links from the sensor nodes to the fusion center have limited capacity, there is a tradeoff between estimation precision and transmission rate. This kind of decentralized estimation system is equivalent to the so-called indirect multiterminal source coding problem, and the Berger-Tung inner bound is the best known achievable rate region boundary. In this paper, we attempt to evaluate the Berger-Tung sum rate for a vector source with correlated components. First we formulate the sum rate as a nonconvex optimization problem with a distortion constraint. Then we propose a method to find an approximate solution. Numerical experiments show the approximate solution is accurate if the required distortion level is relatively small. Its appropriateness is also verified by simulation results based on practical quantizer design.

We investigate the secrecy outage performance for a cooperative unauthenticated relay network where antenna selection is employed at the multi-antenna relay. Both traditional amplify-and-forward (AF) relaying and a cooperative jamming (CJ) protocol are studied. We characterize the exact secrecy outage probability (SOP) for the AF scheme, and analyze the asymptotic behaviour in terms of SOP for the CJ scheme. Although both the unauthenticated relay and the destination perceive diversity gain from antenna selection, we show that as the number of antennas grows, the SOP approaches one for AF, while it approaches zero for CJ. For a fixed number of antennas, we demonstrate that the CJ scheme is better than AF relaying for high SNR, and its SOP approaches zero when the SNR goes to infinity. The theoretical analysis is also validated via several numerical examples.

We consider a distributed sensor network in which the single antenna sensor nodes observe a deterministic unknown parameter and after encoding the observed signal with a phase parameter, the sensor nodes transmit it simultaneously to a multi-antenna fusion center (FC). The FC optimizes the phase encoding parameter and feeds it back to the sensor nodes such that the variance of estimation error can be minimized. We relax the phase optimization problem to a semidefinite programming (SDP) problem and the numerical results show that the performance of the proposed method is close to the theoretical bound. Also, asymptotic results show that when the number of sensors is very large and the variance of the distance between the sensor nodes and FC is small, multiple antennas do not provide a benefit compared with a single antenna system; when the number of antennas M is large and the measurement noise at the sensor nodes is small compared with the additive noise at the FC, the estimation error variance can be reduced by a factor of M.

The MIMO wiretap channel comprises a passive eavesdropper that attempts to intercept communications between an authorized transmitter-receiver pair, with each node being equipped with multiple antennas. In a dynamic network, it is imperative that the presence of a passive eavesdropper be determined before the transmitter can deploy robust secrecy-encoding schemes as a countermeasure. This is a difficult task in general, since by definition the eavesdropper is passive and never transmits. In this work we adopt a method that allows the legitimate nodes to detect the passive eavesdropper from the local oscillator power that is inadvertently leaked from its RF front end. We examine the performance of non-coherent energy detection as well as optimal coherent detection schemes. We then show how the proposed detectors allow the legitimate nodes to increase the MIMO secrecy rate of the channel.

We investigate a relay network where the source can potentially utilize an unauthenticated amplify-and-forward (AF) relay to augment its direct transmission of a confidential message to the destination. Since the relay is unauthenticated, it is desirable to protect the confidential data from it while simultaneously making use of it to increase the reliability of the transmission. We study the likelihood of achieving simultaneously secure and reliable message transmission via the secrecy outage probability (SOP) of the relay network. We first characterize the SOP for three different schemes: direct transmission, conventional AF relaying, and cooperative jamming. Subsequently, an asymptotic analysis is conducted to determine the optimal policies for different power budgets and channel gains. Numerical results are presented to verify the theoretical predictions of the preferred transmission policies from a secrecy outage perspective.

This paper considers a multiple-input multiple-output (MIMO) Gaussian wiretap channel model, where there exists a transmitter, a legitimate receiver and an eavesdropper, each equipped with multiple antennas. Perfect secrecy is achieved when the transmitter and the legitimate receiver can communicate at some positive rate, while ensuring that the eavesdropper gets zero bits of information. In this paper, the perfect secrecy rate of the multiple antenna MIMO wiretap channel is maximized for arbitrary numbers of antennas under the assumption that the transmitter performs beamforming based on the generalized singular value decomposition (GSVD). More precisely, the optimal allocation of power for the GSVD-based precoder that maximizes the achievable secrecy rate is derived. Numerical results are presented to illustrate that the achievable secrecy rate of the GSVD-based precoding approach is nearly identical to that of the optimal scheme.

We consider a collection of single-antenna ground nodes communicating with a multi-antenna unmanned aerial vehicle (UAV) over a multiple-access ground-to-air communications link. The UAV uses beamforming to mitigate inter-user interference and achieve spatial division multiple access (SDMA). First, we consider a simple scenario with two static ground nodes and analytically investigate the effect of the UAV's heading on the system sum rate. We then study a more general setting with multiple mobile ground-based terminals, and develop an algorithm for dynamically adjusting the UAV heading to maximize the approximate ergodic sum rate of the uplink channel, using a prediction filter to track the positions of the mobile ground nodes. For the common scenario where a strong line-of-sight (LOS) channel exists between the ground nodes and UAV, we use an asymptotic analysis to find simplified versions of the algorithm for low and high SNR. We present simulation results that demonstrate the benefits of adapting the UAV heading in order to optimize the uplink communications performance. The simulation results also show that the simplified algorithms provide near-optimal performance.

This paper studies linear beamforming based on the generalized singular value decomposition (GSVD) for the two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel with confidential messages. The transmitter has two independent messages, each of which is intended for one of the receivers but needs to be kept as secret as possible from the other. Recently, it has been proved that, under an input power-covariance constraint, the secret dirty paper coding (S-DPC) scheme is optimal, but under the average power constraint, there is not a computable secrecy capacity expression for the general MIMO case. In fact, for this case, the secrecy capacity region must in general be found through an exhaustive search over the set of all possible matrix power constraints. Clearly, this exhaustive search, as well as the complexity of dirty-paper encoding and decoding, motivates us to consider low complexity linear beamforming technique whose performance is close to the optimal S-DPC scheme. In this paper, we propose a GSVD-based beamforming scheme for the general MIMO broadcast channel with confidential messages. Moreover, an optimal power allocation is obtained to maximize the sum-secrecy rates for the GSVD-based beamforming technique, under the average power constraint. Numerical results are presented to illustrate that the secrecy rate region of the linear precoding approach is nearly identical to that of the optimal S-DPC scheme.

This paper studies robust transmission schemes for MISO wiretap channels with imperfect channel state information (CSI) for the eavesdropper link. Both the cases of direct transmission and cooperative jamming with a helper are investigated. The error in the eavesdropper's CSI is assumed to be norm-bounded, and robust transmit covariance matrices are obtained based on worst-case secrecy rate maximization, under both individual and global power constraints. Numerical results show the advantage of the proposed robust design. In particular, under a global power constraint, although cooperative jamming is not necessary for optimal transmission with perfect eavesdropper's CSI, we show that robust jamming support can increase the secrecy rate in the presence of channel mismatch.

We investigate secure communications for a four-node relay-eavesdropper channel with multiple data stream transmission, assuming that the eavesdropper's channel state information (ECSI) is unavailable. Our approach is to allocate part of the global transmit power to satisfy a fixed target rate for the relay link, and then use the remaining power for jamming the eavesdropper. Two cooperative jamming strategies are investigated. In the first, jamming signals are only broadcast by the terminals that are also transmitting data. In the second scheme, the normally inactive nodes in the relay network are used as cooperative jamming sources. Algorithms are proposed for allocating the transmit power and the number of signal dimensions that carry data. Simulation results show that, under a global transmit power constraint, the secrecy rate is dramatically increased when the normally inactive nodes in the relay network provide jamming support for the relayed signals.

Orthogonal frequency division multiplexing (OFDM) communication systems require accurate estimation of timing offset and channel impulse response in order to achieve desirable performance. In this paper, we consider the optimal placement of pilot symbols over the OFDM subcarriers in order to minimize a function of the Cramer-Rao bound on these parameters. Previous work has investigated this problem for channel estimation only, and found that equi-spaced, equi-powered pilots are optimal. We show that when the time-delay must be simultaneously estimated, the optimal pilot distribution is often quite different, with more pilot energy distributed to the edges of the signal bandwidth. Upper and lower bounds for the required number of optimal pilots are also presented for the case where the variance on the time-delay estimate is minimized.

This work studies distributed linear transmission strategies for the multiple-input multiple-output (MIMO) interference channel with multiple concurrent links. We introduce the notion of interference self-mitigating beamforming (ISM-BF), where each transmitter minimizes the interference it causes to other users while satisfying its own signal-to-inter-ferenceplus-noise ratio requirement. The computation of the optimal transmit beamformers is shown to decouple into a generalized eigenvector problem. In addition, we analyze the optimality and uniqueness of the proposed distributed beamforming solution. Numerical simulations of the sum rate performance of ISM-BF show it to be a viable alternative to interference alignment-based techniques.

We study the achievable rate regions of the multiple-input multiple-output (MIMO) interference channel with confidential messages sent to two receivers, assuming the transmitters use linear precoding. Each receiver is assumed to be an eavesdropper for the other link, and the transmitters employ various techniques to increase the secrecy rate of their own link or that of the network. We describe both cooperative and noncooperative transmission schemes for Gaussian interference channels, and derive their achievable secrecy rate regions. Cooperation is made possible if the transmitters share a portion of their channel state information, allowing them to appropriately adjust their precoders and improve the overall secrecy performance of the network. A game-theoretic formulation of the problem is adopted to allow the transmitters to find an operating point that balances network performance and fairness. The benefit of cooperation is demonstrated via several numerical examples.

This paper proposes a cooperative jamming strategy for two-hop relay networks where the eavesdropper can wiretap the relay channels in both hops. The problems of jamming beamformer design and power allocation are investigated jointly for two scenarios where the eavesdropper has either a single or multiple antennas, with the assumption that the global channel state information (CSI) is available. Under a constraint that the jamming signal lies in a subspaces orthogonal to the channels to legitimate nodes, we derive closed-form solutions for the jamming beamformers. Based on these results, we find the optimal solution for power allocation via geometric programming.

In narrowband multiple-input multiple-output (MIMO) communication systems with perfectly known channel state information (CSI), the singular value decomposition (SVD) is commonly used to decompose the MIMO channel into independent single-input single-output subchannels. In theory, optimal interference-free data multiplexing may then be carried out using the subchannel power levels provided by the well-known waterfilling solution. In practice, however, when finite codebooks are used and perfect CSI is unavailable, adaptations to power levels and bit-loading schemes are often needed to maintain reasonable performance. In this paper, we use expressions for the per-subchannel signal-to-interference- and-noise ratio for the imperfect CSI case to derive approximately optimal subchannel power levels and thresholds on the amount of CSI imperfections and noise tolerable in SVD-based multiplexing systems using M-ary quadrature amplitude modulation. Numerical simulations demonstrate the usefulness of the derived expressions.

Accurate channel state information (CSI) at the transmitter is critical for maximizing spectral efficiency on the downlink of multi-antenna networks. In this work we analyze a novel form of physical layer attacks on such closed-loop wireless networks. Specifically, this paper considers the impact of deliberately inaccurate feedback by malicious users in a multiuser multicast system. Numerical results demonstrate the significant degradation in performance of closed-loop transmission schemes due to intentional feedback of false CSI by adversarial users.

An interference suppression algorithm is proposed for canceling the spatially correlated background noise and interference in EEG/MEG source localization applications. Rather than using the standard prewhitening approach, the proposed algorithm attempts to directly null interference using a projection operator obtained from a set of secondary, control-state data. The use of direct interference cancellation is shown to be significantly more robust than prewhitening, due to the elimination of the need for computing covariance matrices, and the relaxation of the assumption of temporal stationarity. Simulation examples are presented to demonstrate the robust performance of the algorithm.

In this paper, we investigate transmit beamforming schemes based on artificial interference for physical layer security in a multi-antenna wiretap channel. We consider the case where no information regarding the eavesdropper is present, and we use the signal-to-interference-plus-noise-ratio of a single transmitted data stream as our performance metric. Using a second-order perturbation analysis, we quantify the degradation in performance due to imperfect CSI of the main channel at the transmitter. We then present a robust beamforming scheme that approaches the performance obtained in the perfect CSI case. Numerical simulations verify our analytical performance predictions, and illustrate the benefits of the robust beamforming scheme.

This paper investigates transmission strategies in a MIMO wiretap channel with a transmitter, receiver and wiretapper, each equipped with multiple antennas. The secrecy rate between the transmitter and the legitimate receiver is chosen as the performance metric. In a departure from existing work, the wiretapper is able to act either as a passive eavesdropper or as an active jammer, under a half-duplex constraint. The transmitter therefore faces a choice between allocating all of its power for data; or broadcasting artificial noise along with the information signal in order to selectively degrade the eavesdropper's channel. We model the network as a dynamic zero-sum game in extensive form with the secrecy rate as the payoff function. We first examine subgame-perfect equilibrium strategies in the extensive form of the game with perfect information. We then discuss sequential and trembling hand perfect equilibria for the case of imperfect information. Finally, numerical simulations are presented to corroborate the analytical results.

In this paper, we present a novel definition of connectivity for ad hoc networks, and based on this definition, we propose a scheduling algorithm that can optimize the connectivity of the network through scheduling the active links into different time slots. We also propose an adaptive beamforming algorithm that reduces the impact of the co-channel interference on the links that transmit simultaneously. Simulation results demonstrate that the choice of the number of time slots is critical in optimizing the connectivity of the network.

In narrowband multiple-input multiple-output (MIMO) communication systems, when the channel state information (CSI) is known perfectly at the transmitter and the receiver, techniques such as waterfilling may use the singular value decomposition to separate the MIMO channel into independent single-input single-output subchannels. The signal-to-noise ratios of these subchannels are easily found, and, therefore, so are the subchannel bit allocations. In practice, perfect CSI is difficult to obtain. Imperfect CSI results in subchannel coupling and co-channel interference. In this paper, simple first-order expressions are presented for the signal and interference/noise powers for each subchannel for the imperfect CSI case. These expressions may be used to obtain more realistic subchannel bit allocations, allowing for fewer channel outages. Numerical simulations demonstrate the applicability and usefulness of the derived expressions.

This paper studies the use of artificial interference in reducing the likelihood that a message transmitted between two multi-antenna nodes is intercepted by an undetected eavesdropper. Unlike previous work that assumes some prior knowledge of the eavesdropper's channel and focuses on the information theoretic concept of secrecy capacity, we also consider the case where no information regarding the eavesdropper is present, and we use the relative signal-to-interference-plus-noise-ratio (SINR) of a single transmitted data stream as our performance metric. A portion of the transmit power is used to broadcast the information signal with just enough power to guarantee a certain SINR at the desired receiver, and the remainder of the power is used to broadcast artificial noise in order to mask the desired signal from a potential eavesdropper. The interference is designed to be orthogonal to the information signal when it reaches the desired receiver, and we study the resulting relative SINR of the desired receiver and the eavesdropper assuming both employ optimal beamformers.

This paper deals with the joint estimation of temporal (time- delay, Doppler frequency) and spatial (direction-of-arrival, DOA) parameters of several replicas of a known signal in an unknown spatially correlated field. Unstructured and structured models have been proposed in the literature. The former suffers from a severe performance degradation in some scenarios, whereas the latter involves huge complexity. It is shown how the extended invariance principle (EXIP) can be applied to obtain estimates with the quality of those of the structured model, but with the complexity of the unstructured one. We present a method to improve the quality of the time- delay and Doppler estimates obtained with an unstructured spatial model when an estimate of the DOAs is available. Exemplarily, simulation results for time-delay estimation for GPS (global positioning system) are included and confirm that our proposal approaches the Cramer-Rao lower bound (CRLB) of the structured model even when suboptimal DOA estimates obtained by ESPRIT are introduced.

It is well-known that opportunistic transmission schemes are sum-capacity optimal, in the Shannon sense, for symmetric cellular networks with single-antenna transceivers. However, for a practical system operating at non-negligible error probability, the correctly delivered throughput (or spectral efficiency) is a more useful figure-of-merit. In this paper, we present a mathematical model for the average throughput of a practical wireless system with channel-aware user scheduling in a symmetric Rayleigh fading cellular system. By employing an accurate model for the block error probability with turbo coding, and accounting for channel estimation errors, channel feedback quantization, and feedback delay, we present closed-form expressions for the average spectral efficiency of the downlink scheduler.

We investigate the use of multi-user detection to improve performance in MIMO interference networks. Unfortunately, while multi-user detection often allows higher data rates, it greatly complicates the problem: in addition to choosing a transmit covariance for each transmitter, we must decide which signals each receiver will detect and which data rates make such detection feasible. We discuss methods to optimize the data rates in two ways: maximizing the sum throughput of the network, and choosing rates based on the Kalai-Smorodinsky bargaining solution from cooperative game theory. Simulation results suggest that, while sum-rate maximization yields higher average throughput, the Kalai-Smorodinsky solution provides a superior solution in terms of fairness. The simulations also suggest that multi-user detection significantly improves network performance.

We examine signaling strategies for wireless MIMO networks with interference. Previous approaches have focused on maximizing either individual or total throughput, resulting in an inefficient or potentially unfair allocation of resources. We propose two methods motivated by game-theoretic results. First, we extend the non-cooperative Nash equilibrium proposed in previous literature. Second, we present a cooperative method based on the Nash bargaining solution which provides an axiomatic arbitration scheme. Simulation results show that the Nash bargaining solution provides a fair allocation of resources without significantly sacrificing total throughput.

A space-time optimal power schedule for multiple distributed MIMO links without the knowledge of channel state information at transmitting nodes is proposed. This new approach exploits both the spatial and temporal freedoms of distributed MIMO links. A readily computable expression for the ergodic sum capacity of the MIMO links is derived. Based on this expression, a projected gradient algorithm is developed to optimize the power allocation. For a symmetric set of MIMO links, it is observed that the space-time optimal power schedule reduces to a uniform isotropic power schedule when nominal interference is low, or to an orthogonal isotropic power schedule when nominal interference is high. Furthermore, the transition region between the latter two schedules is seen to be very small in terms of nominal interference-to-noise ratio.

A lower bound on the capacity of trained space-time modulation is presented for the case of a time-varying channel. We consider the case where the channel consists of two components of variable strength: a specular component from line-of-sight or strong coherent multipath, and a time-varying diffuse component due to a mobile receiver or scatterers. The training period and the data period are assumed quasi-static, with the time-autocorrelation function of the channel used to model the variation between each sub-block. The training signal, training signal length, power allocation and training frequency that optimize the capacity bound are derived. We compare the effective SNR of this bound to that of differential modulation at high SNR, and find it to be lower. We find the best number of antennas to use at high SNR with differential modulation, and compare it with the optimal value for trained modulation. For our time-varying channel model, trained modulation often has a higher achievable rate than differential modulation. Our results are supported by several numerical examples.

We consider a team of miniature unmanned air vehicles (MUAVs) in a multi-static radar scenario. Time delay and Doppler measurements made at the UAVs are transmitted to a base station which is tracking a target. The base then transmits heading commands to the MUAVs to reduce the tracking error. Optimal solutions that attempt to minimize a function of the error covariance or maximize the observability of the system are computationally difficult to implement. We present a simpler approximate method that yields a closed-form solution and performs comparably to the optimal approaches.

Recent results on the throughput achievable with wireless networks have not fully considered multiple antennas and multi-user links. We introduce these topics by giving the transport capacity of the multiple-antenna multiple-access and broadcast channels. We use these topologies at the physical layer of an ad-hoc network to obtain achievable distance-weighted rates for a multi-antenna wireless network. These values are obtained by maximizing the distance-weighted rate over all combinations of point-to-point, uplink, and downlink topologies, respectively. A distributed algorithm which seeks a Nash equilibrium is used to optimize the transmit covariance matrices for the centralized topology search. Numerical examples with a uniform per-node power constraint show the benefit of uplink topologies over downlink and point-to-point topologies, especially at high transmit power, high numbers of antennas, and a large number of nodes

Unmanned air vehicles (UAVs) can provide important communication advantages to ground-based wireless ad hoc networks. In this paper, the location and movement of UAVs are optimized such that the network connectivity can be improved. Two types of network connectivity are quantified: global message connectivity and worst-case connectivity. The problems of UAV deployment and movement are formulated for these applications. The optimization problems are NP hard and some heuristic adaptive schemes are proposed in order to yield simple solutions. From the simulation results, by deploying only a single UAV, the global message network connectivity and worst-case network connectivity can be improved by up to 109% and 60%, respectively.

Recent theoretical results describing the sum-capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum-rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the energy of the transmitted signal. The paper is comprised of two parts. In this second part, we show that, after the regularization of the channel inverse introduced in the first part, a certain perturbation of the data using a "sphere encoder" can be chosen to further reduce the energy of the transmitted signal. The performance difference with and without this perturbation is shown to be dramatic. With the perturbation, we achieve excellent performance at all signal-to-noise ratios. The results of both uncoded and turbo-coded simulations are presented.

Recent theoretical results describing the sum capacity when using multiple antennas to communicate with multiple users in a known rich scattering environment have not yet been followed with practical transmission schemes that achieve this capacity. We introduce a simple encoding algorithm that achieves near-capacity at sum rates of tens of bits/channel use. The algorithm is a variation on channel inversion that regularizes the inverse and uses a "sphere encoder" to perturb the data to reduce the power of the transmitted signal. This work is comprised of two parts. In this first part, we show that while the sum capacity grows linearly with the minimum of the number of antennas and users, the sum rate of channel inversion does not. This poor performance is due to the large spread in the singular values of the channel matrix. We introduce regularization to improve the condition of the inverse and maximize the signal-to-interference-plus-noise ratio at the receivers. Regularization enables linear growth and works especially well at low signal-to-noise ratios (SNRs), but as we show in the second part, an additional step is needed to achieve near-capacity performance at all SNRs.

A modification to the algebraic constant modulus (ACM) algorithm is proposed that exploits the presence of known pilot symbols within the transmitted data. The pilot symbols are used to place soft constraints on the subspace in which the ACM solution should lie, and relax some of the identifiability conditions required by ACM. The primary advantage of the proposed method is that sources transmitting linearly independent pilot data can be separated without resorting to a joint diagonalization procedure, and hence without computing the beamformers of signals that are not of interest.

The use of multi-user multiple input multiple output (MIMO) processing algorithms in wireless communication systems requires new channel allocation algorithms that can intelligently assign users to channels that can best take advantage of the spatial processing available at both transmitter and receiver. The availability of spatial processing at the receiver adds yet another variable to the classic channel allocation problem, making it very difficult to find the optimal solution for a particular set of users at a reasonable computational cost. We propose a two-step heuristic solution. The first step is the computation of a metric that quantifies the spatial compatibility of two users. The second step is to group the users into shared channels based on optimizing the sum of the compatibility metrics over all groups. we also propose a modified version of the algorithm in which the sub-channels (from different multipath components) of a single user are not required to share the same time-domain channel. In simulations, these algorithms come reasonably close to the optimal solution at a moderate computational cost.

Rapid temporal variations in wireless channels pose a significant challenge for space-time modulation and coding algorithms. This letter examines the performance degradation that results when time-varying flat fading is encountered when using trained and unitary space-time modulation. Performance is characterized for a channel having a constant specular component plus a time-varying diffuse component. A first-order autoregressive (AR) model is used to characterize diffuse channel coefficients that vary from symbol to symbol, and is shown to lead to an effective signal-to-noise ratio (SNR) that decreases with time. Differential modulation is shown to have an advantage in effective SNR over trained unitary modulation at high power. Simulation results are provided to support our analysis.

We analyze the performance of trained and differential space-time modulation for channels with a constant specular component and time-varying diffuse fading. We examine the case where the channel varies from sample to sample within a space-time symbol matrix according to a first-order time-varying model. We show that the effect of the time-varying diffuse channel can be described by an effective signal-to-noise ratio (SNR) that decreases with time. We derive pairwise probability of error expressions based on these effective SNR values that accurately describe performance for unitary modulation. We quantify the significant advantage that differential modulation provides at high SNR where the effect of the time-varying channel dominates. At low SNR where additive noise dominates, we note that trained modulation with perfect channel state information provides a 3-dB advantage over differential modulation, but decoding based on a maximum likelihood channel estimate yields worse performance than differential modulation at all SNR values. Simulation results are provided to support our analysis.

Abstract Several algorithms have recently been proposed for multiplexing multiple users in multiple input multiple output (MIMO) downlinks. The ability of a transmitter to accomplish this using spatial methods is generally dependent on whether the users' channels are correlated. Up to this point, most of the multiplexing algorithms have been tested on uncorrelated Gaussian channels, a best‐case scenario. In this paper, we examine the performance of multiplexing multiple users under more realistic channel conditions by using indoor channel measurements and a statistical model for test cases. We use a block zero‐forcing algorithm to test performance at various user separation distances, optimizing for both maximum throughput under a power constraint and minimum power under a rate constraint. The results show that for the measured indoor environment (rich scattering, non‐line‐of‐sight), a separation of five wavelengths is enough to achieve close to the maximum available performance for two users. Since many spatial multiplexing algorithms require channel state information (CSI) at the transmitter, we also examine the performance loss due to CSI error. The results show that a user can move up to one‐half wavelength before the original channel measurement becomes unusable. Copyright © 2004 John Wiley & Sons, Ltd.

The pairwise probability of error for trained unitary space-time modulation over channels with a constant specular component and time-varying diffuse fading is derived. We consider the case where the diffuse channel varies from sample to sample within a symbol according to a first-order AR model. Our previous results are reviewed which show that the effect of the time-varying diffuse channel can be described by an effective SNR that decreases with time. We derive pairwise probability of error expressions using these effective SNR values, which are shown by simulation to describe the performance accurately.

In the downlink of a multi-user MIMO (multiple input multiple output) communication system where each user has an arbitrary QoS requirement, intelligent algorithms are needed to choose transmit vectors. Here we present a new method of choosing transmit vectors that minimizes total transmitted power. The approach is based on previous iterative interference balancing algorithms, but it is initialized by applying a "block-diagonalization" algorithm that helps improve convergence speed. When the channel supports multiple data streams per user, power is distributed among the data streams by bit-loading using the channel gains derived from the block-diagonalization step. The result is a solution which is not guaranteed to converge to the global optimum, but will reach a solution that is either optimal or near-optimal with high probability and at minimal computational cost.

The pairwise probability of error for differential unitary space-time modulation for channels with a constant specular component and time-varying diffuse fading is derived in this paper. We consider the case where the channel varies from sample to sample within a symbol according to a first-order Gauss-innovations model. Our previous results are reviewed which show that the effect of the time-varying diffuse channel can be described by an effective SNR that decreases with time. We derive pairwise probability of error expressions using these effective SNR values, which are shown by simulation to accurately describe performance.

Using a model based on the time-autocorrelation function for time-varying MIMO channels, we find a lower bound on capacity for trained modulation, and find the training signal, training power, and length of training signal which maximize this bound. An approximation for the training frequency that maximizes the bound at high SNR is given for Jakes' fading model. The capacity for differential unitary space-time modulation is discussed and compared with that for trained modulation. We present several numerical examples to illustrate our results.

Detailed performance assessment of space-time coding algorithms in realistic channels is critically dependent upon accurate knowledge of the wireless channel spatial characteristics. This paper presents an experimental measurement platform capable of providing the narrowband channel transfer matrix for wireless communications scenarios. The system is used to directly measure key multiple-input-multiple-output parameters in an indoor environment at 2.45 GHz. Linear antenna arrays of different sizes and construction with up to ten elements at transmit and receive are utilized in the measurement campaign. This data is analyzed to reveal channel properties such as transfer matrix element statistical distributions and temporal and spatial correlation. Additionally, the impact of parameters such as antenna element polarization, directivity, and array size on channel capacity are highlighted. The paper concludes with a discussion of the relationship between multipath richness and path loss, as well as their joint role in determining channel capacity.

This paper presents a general framework for space-time codes that encompasses a number of recently proposed schemes as special cases. The space-time codes considered are block codes that employ arbitrary redundant linear precoding of a given data sequence together with embedded training symbols, if any. The redundancy introduced by the linear precoding imposes structure on the received data that under certain conditions can be exploited for blind or semi-blind estimation of the transmitted sequence (direct approach) or a linear equalizer that recovers the sequence in a second step (indirect approach). Algorithms based on this observation are developed for the flat-fading case, and then extended to handle frequency-selective fading.

This paper exploits a special class of space-time codes in which linearly transformed versions of a given data sequence are transmitted from multiple antennas. Several recently proposed codes, including space-time block codes, are members of this class. The redundancy introduced by the transformations imposes structure on the received data that under certain conditions can be exploited for direct blind (and semi-blind) estimation of a linear zero-forcing receiver that recovers the original data sequence. If the transmitted symbols are constant modulus, the space-time code structure can be exploited by the analytic constant modulus (ACM) algorithm to simplify the separation of multiple co-channel users. If each user employs a different code or only one user is present, the ACM joint diagonalization step can be eliminated even though multiple constant modulus data streams are received.

A statistical space-time model for indoor wireless propagation based on empirical measurements is compared with results from the deterministic ray-tracing simulation tool WiSE for the same environment. Excellent agreement is found in terms of the distributions of arrival times and angular spread for both modeling approaches. The WiSE package is also use to synthesize MIMO channel matrices and determine the theoretical capacity, available in the tested environments. It is found that, for narrowband channels, the spatial clustering of the multipaths limits the capacity gains associated with increased array size.

Many reduced dimension STAP algorithms have been developed for airborne radar applications which rely on a stationary Doppler component of the interference in order to maintain acceptable performance. Two cases where this assumption is violated are when the ground clutter contains intrinsic clutter motion (ICM) and when hot clutter is present. In addition to the non-stationary Doppler component, hot clutter contains non-zero correlations in fast-time (across range bins) as well. This paper presents an algorithm designed to mitigate both ground clutter and hot clutter in the same step using a two dimensional vector autoregressive model to whiten the data in space, fast-time, and slow-time. This is an extension of the space-time autoregressive (STAR) filter that we have previously proposed. Using a simulated data set for circular array STAP augmented with synthetic hot clutter, we demonstrate that the extensions we present do result in a significant performance increase over the standard STAR filter. In addition we also show that the STAR filters have a narrower clutter notch than the optimized pre-Doppler filter when a finite sample support is used to train the filters.

The fading channel is a significant problem in many communications environments. We examine the performance of unitary space-time modulation in a time-varying channel. We use a Gauss-Markov model of the continuously varying channel to characterize performance of differential and trained modulation. We find a performance ceiling at high SNR where the effect of the changing channel dominates. We show that while trained modulation provides an advantage at low SNR, above a certain SNR differential modulation gives better performance. We conclude with simulation results that support our analysis.

Recent research has demonstrated the existence of transmission schemes and codes which provide diversity gain by using multiple transmitting antennas. This paper applies the concepts of multicarrier DS-CDMA systems to the multiple transmitter case. The multicarrier CDMA system discussed here was originally based on the assumption that the frequency-selective fading was uncorrelated from one subcarrier to the next. We derive the performance for a system whose subcarriers are subject to correlated fading, and the performance of a system partially or completely decorrelated by distributing the subcarriers across transmit antennas.

The fading channel is a significant problem in many communications environments. We examine the performance of unitary space-time modulation in a time-varying channel. We use a Gauss-Markov model of the continuously varying channel to characterize performance of differential and trained modulation. We find a performance ceiling at high SNR where the effect of the changing channel dominates. We show that while trained modulation provides an advantage at low SNR, above a certain SNR differential modulation gives better performance. We conclude with simulation results that support our analysis.

This paper considers the problem of estimating the time delays of multiple replicas of a known signal received by an array of antennas. Under the assumptions that the noise and co-channel interference (CCI) are spatially colored Gaussian processes and that the spatial signatures are arbitrary, the maximum likelihood (ML) solution to the general time delay estimation problem is derived. The resulting criterion for the delays yields consistent and asymptotically efficient estimates. However, the criterion is highly non-linear, and not conducive to simple minimization procedures. We propose a new cost function that is shown to provide asymptotically efficient delay estimates. We also outline a heuristic way of deriving this cost function. The form of this new estimator lends itself to minimization by the computationally attractive iterative quadratic maximum likelihood (IQML) algorithm. The existence of simple yet accurate initialization schemes based on ESPRIT and identity weightings makes the approach viable for practical implementation.

This paper describes generalizations of the MUSIC and root-MUSIC algorithms for direction of arrival (DOA) estimation to arrays composed of multiple translated subarrays. The advantage of these new approaches is that the DOAs can be estimated using either a one-dimensional search or by rooting a polynomial, as opposed to a multidimensional search as required by the multiple invariance (MI)-ESPRIT algorithm. While MI-MUSIC and root-MI-MUSIC are not statistically efficient like MI-ESPRIT, they do perform better than a single invariance implementation of ESPRIT, and are thus better suited for finding the initial conditions required by the MI-ESPRIT search.

This paper presents a subspace method for blind channel estimation based on A a special output correlation matrix. The approach relies on the i.i.d. assumption of the data sequence and uses the cyclic prefix redundancy present in OFDM systems or single-carrier systems with frequency domain equalization. This method has an important feature that allows channels to be longer than the cyclic prefix. In addition, unknown frame synchronization can be accommodated. There is no constraint on the zero locations of the channel and the performance is asymptotically independent of white noise. The method is compared with a simple correlation approach and a deterministic subspace method.

Most previously proposed statistical models for the indoor multipath channel include only time of arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full spare-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.

This work considers the problem of jointly estimating symbol frame boundaries and carrier frequency offsets for orthogonal frequency division multiplexed (OFDM) communications in frequency selective fading environments. Orthogonality between the modulated and virtual carriers over an interference free window of the received signal is used to develop an algorithm for estimating the carrier frequency offset and detecting the beginning of a symbol frame. By using a cyclic prefix to remove interference from neighboring frames, the the method is applicable in the presence of dispersive channels. The main contribution of this work is the joint estimation of the frequency offset and the frame boundary.

We propose two batch versions of the constant modulus algorithm in which a fixed block of samples is iteratively reused. The convergence rate of the algorithms is shown to be very fast. The delay to which the algorithms converge can be determined if the peak position of the initialized global channel/equalizer response is known. These fixed window CM algorithms are data efficient, computationally inexpensive and no step-size tuning is required. The effect of noise, and the relationship between the converging delay and noise enhancement are analyzed as well.

The mathematical theory of kernel (null space) structure of Hankel and Hankel-like matrices is applied to the problem of blind equalization of co-channel signals. This work builds on recently introduced ideas in blind equalization where the symbols are treated as deterministic parameters and estimated directly without estimating the channel first. The main contribution of the new approach is that it allows the simultaneous exploitation of shift structure in the data model and the finite alphabet property of the signals.

This paper considers the problem of estimating the time delays and Doppler shifts of a known waveform received via several distinct paths by an array of antennas. The general maximum likelihood estimator is presented, and is shown to require a 2d-dimensional non-linear minimization, where d is the number of received signal reflections. Two alternative solutions based on signal and noise subspace fitting are proposed, requiring only a d-dimensional minimization. In particular, we show how to decouple the required search into a two-step procedure, where the delays are estimated and the Dopplers solved for explicitly. Initial conditions for the time delay search can be obtained by applying generalizations of the MUSIC and ESPRIT algorithms.

Model error sensitivity is an issue common to all high resolution direction of arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to array calibration errors have also appeared in the literature. Herein, a weighted subspace fitting method for a wide class of array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly enough, the method reduces to the WSF (MODE) estimator if no model errors are present. On the other hand, when model errors dominate, the proposed method turns out to be equivalent to the "model-errors-only subspace fitting method". Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated.

We consider robust and computationally efficient maximum likelihood algorithms for estimating the parameters of a radar target whose signal is observed by an array of sensors in interference with unknown second-order statistics. Two data models are described: one that uses the target direction of arrival and signal amplitude as parameters and one that is a simpler, unstructured model that uses a generic target "spatial signature". An extended invariance principle is invoked to show how the less accurate maximum likelihood estimates obtained from the simple model may be refined to achieve asymptotically the performance available using the structured model. The resulting algorithm requires two one-dimensional (1-D) searches rather than a two-dimensional search, as with previous approaches for the structured case. If a uniform linear array is used, only a single 1-D search is needed. A generalized likelihood ratio test for target detection is also derived under the unstructured model. The principal advantage of this approach is that it is computationally simple and robust to errors in the model (calibration) of the array response.

It has been observed that with appropriate stepsize normalization, the convergence speed of the constant modulus (CM) algorithm can be dramatically improved. It is shown that if a different normalization strategy is used, one that takes into account the finite alphabet structure of the signals, a standard normalized version of the decision directed equalizer (DDE) is achieved. A simulation example is included to demonstrate the faster convergence of the normalized DDE compared with its constant stepsize implementation and the normalized CM.

In wireless communication scenarios, local scatterers in the vicinity of the mobile sources cause angular spreading. As a result, the spatial signatures will not belong to the conventional array manifold parameterized by direction of arrival (DOA) alone. A parameterized model for spatial signatures applicable in scenarios with local scattering is presented. Several algorithms that exploit this model are proposed, and the performance of signal waveform estimators using the model is investigated via simulations. It is demonstrated that considerable gain may result as compared with using the conventional plane wave model.

This paper considers the problem of recovering an unknown signal transmitted over an unknown (but stationary) multipath channel, and received by a narrowband array with unknown calibration. Unlike previously proposed multichannel blind equalization techniques, the methods described herein employ a model based on physical channel parameters rather than unstructured multiple output FIR filters. The algorithms exploit the structure of the signal and noise subspaces of the array output data when transformed to the frequency domain. Two approaches are presented. The first is an ESPRIT-like solution that provides a closed-form, but suboptimal, blind signal estimate. The second is based on maximum likelihood and, though requiring a search, is easily initialized with the ESPRIT solution. A mathematical development of the two algorithms is given, and their advantages and disadvantages relative to other currently available techniques are discussed.

This paper is concerned with the problem of optimal (maximum likelihood) direction of arrival (DOA) estimation in situations where the sensor array is calibrated over only a portion of the DOA space. Situations such as this often arise in airborne direction finding when skywave multipath is present. A parameterization is proposed for partially calibrated arrays (PCAs), and the identifiability of the model is discussed for both uncorrelated and correlated signals. It is shown how the signal and noise subspace fitting algorithms are generalized to handle PCAs, and a detection scheme is proposed for individually determining the number of signals arriving from calibrated and uncalibrated directions. The results of several simulation examples are included to validate the analysis.

We consider the signal-to-interference plus noise ratio (SINR) performance of several beamforming algorithms, taking particular account of the contribution of sources correlated with the desired signal. In addition, we derive an optimal method that maximizes SINR by combining with the desired signal estimate any components of the interference/multipaths that are correlated with it. To facilitate performance comparisons, we only consider the case where the signal directions of arrival (DOAs) are precisely known. The extension to unknown DOAs is straight-forward. Our analysis includes the first order effects of array calibration errors, and is verified by numerical simulation.

With few exceptions, high-resolution source localization algorithms require an exact characterization of the array, including knowledge of the sensor positions, sensor gain/phase response, mutual coupling, and receiver equipment effects. In practice, all such information is inevitably subject to errors. Recently, several different methods have been proposed for alleviating the inherent sensitivity of parametric methods to such modeling errors. The technique proposed herein is related to the class of so-called auto-calibration procedures, but it is assumed that certain prior knowledge of the array response errors is available. The optimal maximum a posteriori (MAP) estimator for the problem at hand is formulated, and a more computationally attractive large-sample approximation is derived. In addition, the performance advantage of the algorithm is illustrated by an example involving a linear array mounted on a flexible structure.>

Blind adaptive algorithms extract signals that overlap in time and frequency by exploiting their temporal structure, but ignore any available spatial (array response) data. On the other hand, direction-finding based methods compute the signal copy weights using estimates of the signal directions, but ignore information about signal structure. In this paper, we present two simple iterative techniques that attempt to incorporate both temporal and spatial information in estimating the signal waveforms received by an array of sensors. The first technique assumes an initial blind signal estimate is available, and uses least-squares to approximate the array response and refine the signal estimate. The second method is applicable to digitally modulated signals, and uses bit decisions made on an initial signal estimate to recompute the signal copy weight vectors. A theoretical performance analysis of both algorithms is conducted for the high SNR case, and some representative simulation results are included.>

Conventional methods for signal copy require one to estimate the directions of arrival (DOA's) of the signals prior to computing the weight vectors. Blind copy algorithms alleviate the need for DOA estimation (and hence the need for array calibration data) by exploiting the temporal rather than spatial structure of the signals, but they converge slowly in some cases. In this letter, we present a simple technique that uses both spatial and temporal information to improve signal copy performance. Specifically, the algorithm uses an initial blind estimate of the signals to compute a least-squares estimate of the array response, which in turn is used to update the signal estimates.>

A unified approach is presented to the related problems of recovering signal parameters from noisy observations and identifying linear system model parameters from observed input/output signals, both using singular value decomposition (SVD) techniques. Both known and new SVD-based identification methods are classified in a subspace-oriented scheme. The SVD of a matrix constructed from the observed signal data provides the key step in a robust discrimination between desired signals and disturbing signals in terms of signal and noise subspaces. The methods that are presented are distinguished by the way in which the subspaces are determined and how the signal or system model parameters are extracted from these subspaces. Typical examples, such as the direction-of-arrival problem and system identification from input/output measurements, are elaborated upon, and some extensions to time-varying systems are given.>

Most popular techniques for the narrowband direction of arrival (DOA) problem rely on an eigenvalue decomposition (EVD) computation to determine both the number of signals and their respective DOAs. An alternative algorithm is presented that solves both the DOA detection and estimation problems without resorting to an EVD. The algorithm is shown to be asymptotically equivalent to the (stochastic) maximum likelihood method, and hence yields asymptotically minimum variance DOA estimates. In addition, the asymptotic distribution of the algorithm's cost function is derived and is shown to be composed of the sum of two differently scaled chi-squared random variables. A hypothesis test for determining the number of signals based on this result is presented.>

The recently introduced class of subspace fitting algorithms for sensor array signal processing (e.g. direction-of-arrival (DOA) estimation) includes deterministic maximum likelihood, ESPRIT, weighted subspace fitting, and both one- and multidimensional MUSIC as special cases. The performance of this class of algorithms is examined for situations where the sensor array response is perturbed from its nominal value. Theoretical expressions for the error in the DOA estimates are derived and compared with several simulation examples. It is shown that in difficult cases the algorithms are especially sensitive to the choice of subspace weighting. For a particular perturbation model, and optimal subspace weighting is proposed which minimizes the DOA estimate error variance over all possible weightings when finite sample effects are neglected.>

The third moment matrix R/sub 3/ is proposed as a tool for use in the detection and estimation of phase-coupled sine waves. The advantages of such an approach are that the structure of R/sub 3/ can be exploited by means of the singular value decomposition (SVD) to estimate directly the number of coupled sine waves and their frequencies. After a brief introduction to the bispectrum and the phase coupling problem, the following key results are presented: (1) The rank of R/sub 3/ is (asymptotically) equal to the number of coupled sine waves. (2) A pseudobispectrum can be generated using a MUSIC-like algorithm with vectors orthogonal to the (column) signal subspace. (3) The coupled frequencies can be directly estimated from the (row) signal subspace using techniques such as Root-MUSIC or ESPRIT. Examples of simulations using this approach are given.>

A decision-directed learning strategy is presented to recursively estimate (i.e., track) the time-varying a priori distribution for a multivariate empirical Bayes adaptive classification rule. The problem is formulated by modeling the prior distribution as a finite-state vector Markov chain and using past decisions to estimate the time evolution of the state of this chain. The solution is obtained by implementing an exact recursive nonlinear estimator for the rate vector of a multivariate discrete-time point process representing the decisions. This estimator obtains the Doob decomposition of the decision process with respect to the a-field generated by all past decisions and corresponds to the nonlinear least squares estimate of the prior distribution. Monte Carlo simulation results are provided to assess the performance of the estimator.