Orthogonal time frequency space (OTFS) offers significant advantages in managing mobility for both wireless sensing and communication systems, making it a promising candidate for dual-functional radar-communication (DFRC). However, the optimal signal design that fully exploits OTFS's potential in DFRC has not been sufficiently explored. This paper addresses this gap by formulating an optimization problem for signal design in DFRC-OTFS, incorporating both pilot-symbol design for channel estimation and data-power allocation. Specifically, we employ the integrated sidelobe level (ISL) of the ambiguity function as a radar metric, accounting for the randomness of the data symbols alongside the deterministic pilot symbols. For communication, we derive a channel capacity lower bound metric that considers channel estimation errors in OTFS. We maximize the weighted sum of sensing and communication metrics and solve the optimization problem via an alternating optimization framework. Simulations indicate that the proposed signal significantly improves the sensing-communication performance region compared with conventional signal schemes, achieving at least a 9.44 dB gain in ISL suppression for sensing, and a 4.82 dB gain in the signal-to-interference-plus-noise ratio (SINR) for communication.

Due to the availability of wide bandwidths in the frequency range 2 (FR2) band, sixth-generation (6G) networks can provide both radar and communication services using shared spectrum and hardware, reducing costs and enhancing efficiency via integrated sensing and communication (ISAC) systems. Motivated by the advantages of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios, this paper proposes an iterative hybrid precoder design method and a radar tracking algorithm for OTFS-ISAC systems with aerial targets, i.e., drones. Information is sent to the ground user through communication links using the subarray hybrid precoder to balance radar and communication performance through a weighted summinimization framework. The cubature Kalman filter (CKF) is employed for radar prediction and tracking. Simulation results indicate that the proposed hybrid precoder design algorithm effectively balances radar and communication performance, while the CKF-based radar tracking method outperforms other nonlinear Kalman filters.

Orthogonal frequency division multiplexing (OFDM) is one of the most widely adopted waveforms for integrated sensing and communication (ISAC) systems, owing to its high spectral efficiency and compatibility with modern communication standards. This paper investigates the sensing performance of OFDM-based ISAC for multi-target delay (range) estimation under specific radar receiver processing schemes. An estimation-theoretic framework is developed to characterize sensing performance with random communication payloads. We establish the fundamental limit of delay estimation accuracy by deriving the closed-form expression of the mean-square error (MSE) achieved using matched filtering (MF) and reciprocal filtering (RF) receivers. The results show that, in multi-target scenarios, the impact of signal constellations on the delay estimation MSE differs across receivers: MF performance depends on the fourth-order moment of the zero-mean, unit-power constellation in the presence of multiple targets, whereas RF performance depends on its inverse second-order moment, irrespective of the number of targets. Building on this analysis, we present a ISAC constellation design under specific receiver architecture that brings a receiver-dependent flexible trade-off between sensing and communication in OFDM-ISAC systems. The theoretical findings are validated through simulations and proof-of-concept experiments, and also the sensing and communication performance trade-off is experimentally shown with the proposed constellation design.

Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propose the Sensing Dataset Protocol (SDP), a protocol-level specification and benchmark framework for large-scale wireless sensing. SDP defines how heterogeneous wireless signals are mapped into a unified perception data-block schema through lightweight synchronization, frequency-time alignment, and resampling, while a Canonical Polyadic-Alternating Least Squares (CP-ALS) pooling stage provides a task-agnostic representation that preserves multipath, spectral, and temporal structures. Built upon this protocol, a unified benchmark is established for detection, recognition, and vital-sign estimation with consistent preprocessing, training, and evaluation. Experiments under the cross-user split demonstrate that SDP significantly reduces variance (approximately 88%) across seeds while maintaining competitive accuracy and latency, confirming its value as a reproducible foundation for multi-modal and multitask sensing research.

Integrated sensing and communications (ISAC), which are recognized as a key enabler for sixth generation (6G), have brought new opportunities for intelligent, sustainable, and connected wireless networks. Multiple-input–multiple-output (MIMO) transceiver technology lies at the core of this paradigm, providing the degrees of freedom required for simultaneous data transmission and accurate radar sensing. The tight integration of sensing and communication (S&C) introduces unique security vulnerabilities that extend beyond conventional physical-layer security (PLS). In particular, high-power transmissions directed at sensing targets may empower adversarial eavesdroppers, whereas passive interception of ISAC echoes can reveal sensitive information such as target locations and mobility patterns. This article presents an overview of recent advances in MIMO ISAC transceiver design, considering transmitter perspectives, receiver architectures, and full-duplex implementations. We examine MIMO transceiver designs under unique security threats specific to ISAC and highlight emerging countermeasures, including secure signaling design, interference exploitation, and transceiver optimization under adversarial conditions. Finally, we discuss challenges and research opportunities for developing secure ISAC systems in next-generation wireless networks.

This study proposes a radar-centric integrated sensing and communication (ISAC) system that utilizes a two-layer modulation scheme for vehicular networks. Frequencymodulated continuous wave (FMCW) chirps are jointly modulated via phase modulation (PM) and index modulation (IM) to transmit data while maintaining sensing as the primary function. Moreover, a novel radar signal processing technique is developed to mitigate the impacts of IM and PM on sensing accuracy, alongside a communication receiver architecture designed to demodulate IM and PM data within FMCW chirps successfully. System performance is evaluated through simulations in the 2.4 GHz and 24 GHz bands under Doppler effects, achieving communication throughputs of 25 Mbps and 50 Mbps, respectively. Furthermore, a proof-of-concept hardware implementation is realized, and experimental measurements are performed via a loopback cable to verify the feasibility of the architecture. Finally, it evaluates the fundamental trade-off between communication throughput, sensing accuracy, and out-of-band emission, demonstrating the system’s flexibility to dynamically adjust waveform parameters to meet various operational requirements.

In this paper, we study the potential of interference exploitation symbol-level precoding (SLP), focusing on maximizing mutual information (MI) in multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. As traditional MI expressions based on Gaussian signalling do not apply to SLP, we firstly derive the expression of the MI for SLP based on finite alphabet inputs, which is shown to be a nonlinear and non-concave function of the precoder. Subsequently, we formulate an optimization problem aimed at maximizing the MI without investing additional transmit signal power. Due to the original problem being non-convex, we design an iterative gradient-projection (GP)-based algorithm by deriving the gradient of the MI with respect to the SLP signal matrix. To alleviate the heavy reliance on Monte Carlo evaluations, we further propose an effective variant that employs the channel cut-off rate (CR) as a tractable optimizaiton metric within the GP framework. Moreover, we develop a low-complexity algorithm by deriving a closed-form approximation of the MI and addressing the resulting problem through successive convex approximation (SCA), thereby obtaining a near-optimal solution to the original formulation. Numerical results verify that the proposed MI-optimization SLP (MIO-SLP) exhibits a noticeable gain over traditional SLP schemes in terms of MI, and achieves close-to-optimal MI performance, while ensuring satisfactory error rate.

Integrated Sensing and Communication (ISAC) has emerged as a promising solution in addressing the challenges of high-mobility scenarios in 5 G NR Vehicle-to-Infrastructure (V2I) communications. This paper proposes a novel sensing-assisted handover framework that leverages ISAC capabilities to enable precise beamforming and proactive handover decisions. Two sensing-enabled handover triggering algorithms are developed: a distance-based scheme that utilizes estimated spatial positioning, and a probability-based approach that predicts vehicle maneuvers using interacting multiple model extended Kalman filter (IMM-EKF) tracking. The proposed methods eliminate the need for uplink feedback and beam sweeping, thus significantly reducing signaling overhead and handover interruption time. A sensing-assisted NR frame structure and corresponding protocol design are also introduced to support rapid synchronization and access under vehicular mobility. Extensive link-level simulations using real-world map data demonstrate that the proposed framework reduces the average handover interruption time by over 50%, achieves lower handover rates, and enhances overall communication performance.

Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement. This paper explores the integration of a transmit FAS array for hybrid beamforming (HBF) into a reconfigurable intelligent surface (RIS)-assisted communication architecture for multiuser communications in the downlink, corresponding to the downlink RIS-assisted multiuser multiple-input single-output (MISO) FAS model (Tx RIS-assisted-MISO-FAS). By considering Rician channel fading, we formulate a sum-rate maximization optimization problem to alternately optimize the HBF matrix, the RIS phase-shift matrix, and the FAS position. Due to the strong coupling of multiple optimization variables, the multi-fractional summation in the sum-rate expression, the modulus-1 limitation of analog phase shifters and RIS, and the antenna position variables appearing in the exponent, this problem is highly non-convex, which is addressed through the block coordinate descent (BCD) framework in conjunction with semidefinite relaxation (SDR) and majorization-minimization (MM) methods. To reduce the computational complexity, we then propose a low-complexity grating-lobe (GL)-based telescopic-FA (TFA) system with multiple delicately deployed RISs under the sub-connected HBF architecture and the line-of-sight (LoS)-dominant channel condition, to allow closed-form solutions for the HBF and TFA position. Our simulation results illustrate that the former optimization scheme significantly enhances the achievable rate of the proposed system, while the GL-based TFA scheme also provides a considerable gain over conventional fixed-position antenna (FPA) systems, requiring statistical channel state information (CSI) only and with low computational complexity.

With the emerging privacy sensitive applications, anonymity is recognized as an important attribute in privacy-preserving communications. Existing anonymous precoding approaches at the physical (PHY) layer aim to mask sender’s channel characteristic while overlooking the fact that other PHY characteristics contain information that can be traced back to the sender. In this paper, we first reveal that the sender’s in-phase and quadrature-phase imbalance (IQI) characteristic of its radio frequency (RF) front end can also be exploited as unique signature of the sender. Since the signal is transmitted through the RF front end and then is propagated through the channel, the received signal carries the composite characteristics of the IQI and channel which can be exploited to identify the sender. To prevent the sender detection enhanced by IQI, an IQI aware anonymous alias sender construction scheme is proposed with the concept of Kullback-Leibler divergence (KLD). It manipulates the signaling of transmitted signal, so that from the perspective of detector, the detection statistic of the real sender is close to that of the alias. To mitigate the communication performance loss caused by the IQI, we exploit the IQI interference as a constructive element. While the constructive IQI design increases the degree of freedom of precoding design, it does not violate the sender anonymity requirement. Finally, an IQI aware anonymous precoder (IAA) is proposed. Simulation demonstrates that the anonymity and communication performance of the proposed IAA precoder is maintained at a high level and is robust to the IQI parameters, where existing approaches fail.

Integrated Sensing and Communication (ISAC) has the potential to enhance both energy and spectral efficiency in modern communication systems. Although Probability Hypothesis Density (PHD)-based Simultaneous Localization and Mapping (SLAM) is a key algorithm for positioning and environmental mapping in ISAC, the advantages of multi-base-station (multi-BS) fusion remain underexplored, despite the considerable attention given to multi-sensor and multi-user data fusion in existing research. This paper leverages the distinct roles of Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) channel parameters, employing LoS for agent localization and NLoS for environment mapping. An Extended Kalman Filter (EKF) framework is proposed to fuse LoS path angle parameters for localization, for which the corresponding Cramér-Rao Lower Bound (CRLB) is derived. To facilitate landmark mapping, a virtual reference point (VRP) is introduced to model reflecting surfaces consistently across base stations (BSs), replacing the conventional approach of using multiple virtual anchors for multiple BSs. Furthermore, map fusion algorithms are developed to address the challenges of merging PHD-SLAM maps with varying observation quality and overlapping fields of view. To reduce the computational complexity of particle-based PHD-SLAM, agent location estimates derived from EKF fusion are used as priors, significantly improving particle efficiency and enabling the unified exploitation of LoS and NLoS data for comprehensive situational awareness. Simulation and experimental results confirm that the proposed EKF-based LoS fusion strategy significantly improves sensing performance while maintaining low computational overhead.

The deployment of integrated sensing and communication (ISAC) in wireless networks brings along unprecedented vulnerabilities to authorized passive sensing, necessitating the development of secure sensing solutions. Unlike traditional wireless communication, where data security can be enhanced through data encryption, sensing security is more challenging to achieve. This is because sensing parameters are embedded within the target-reflected signal leaked to unauthorized passive radar sensing eavesdroppers (Eve), implying that they can silently extract sensory information without prior knowledge of the information data. To overcome this limitation, we propose a novel sensing-secure ISAC framework that ensures secure target detection and estimation for the legitimate system, while obfuscating unauthorized sensing without requiring any prior knowledge of Eve. Specifically, by introducing artificial imperfections into the ambiguity function (AF) of ISAC signals, we introduce artificial ghost targets into Eve’s range profile which increase its range estimation ambiguity. In contrast, the legitimate sensing receiver (Alice) can suppress these AF artifacts using mismatched filtering, albeit at the expense of signal-to-noise ratio (SNR) loss. Specifically, employing an OFDM signal, a structured subcarrier power allocation scheme is designed to shape the secure autocorrelation function (ACF), inserting periodic peaks to mislead Eve’s range estimation and degrade target detection performance. To quantify the sensing security level, we introduce peak sidelobe level (PSL) and integrated sidelobe level (ISL) as key performance metrics. Additionally, we analyze the three-way trade-offs between communication, legitimate sensing, and sensing security, highlighting the impact of the proposed sensing-secure ISAC signaling on system performance. Furthermore, we formulate a convex optimization problem to maximize ISAC performance while guaranteeing a certain sensing security level. Numerical results validate the effectiveness of the proposed sensing-secure ISAC signaling, demonstrating its ability to degrade Eve’s target estimation while preserving Alice’s performance.

The ultimate goal of enabling sensing through the cellular network is to obtain coordinated sensing of an unprecedented scale, through distributed integrated sensing and communication (D-ISAC). This, however, introduces challenges related to synchronization and demands new transmission methodologies. In this paper, we propose a transmit signal design framework for noncoherent D-ISAC systems, where multiple ISAC nodes cooperatively perform sensing and communication without requiring phase-level synchronization. The proposed framework employing orthogonal frequency division multiplexing (OFDM) jointly designs downlink coordinated multi-point (CoMP) communication and multi-input multi-output (MIMO) radar waveforms. This leverages both collocated and distributed MIMO radars to estimate angle-of-arrival (AOA) and time-of-flight (TOF) from all possible multi-static measurements for target localization. To this end, we use the target localization Cramér-Rao bound (CRB) as the sensing performance metric and the signal-to-interference-plus-noise ratio (SINR) as the communication performance metric. Then, an optimization problem is formulated to minimize the localization CRB while maintaining a minimum SINR requirement for each communication user. Particularly, we present three distinct transmit signal design approaches, including unconstrained, orthogonal, and beamforming designs, which reveal trade-offs between ISAC performance and computational complexity. Unlike single-node ISAC systems, the proposed D-ISAC designs involve per-subcarrier sensing signal optimization to enable accurate TOF estimation, which contributes to the target localization performance. Numerical simulations demonstrate the effectiveness of the proposed designs in achieving flexible ISAC trade-offs and efficient D-ISAC signal transmission.

As a foundational technology for the sixth generation networks that integrates communication, sensing, computing, and intelligence, integrated sensing and communication (ISAC) surpasses conventional isolated system designs across multiple performance dimensions. Given its superior interference management capability, non-orthogonal multiple access (NOMA) presents significant potential for integration into ISAC systems. To this end, this paper establishes a unified NOMA-ISAC framework that concurrently incorporates residual hardware impairments, channel estimation errors, and imperfect successive interference cancellation (SIC). Within this framework, we propose two distinct SIC designs tailored for different operational priorities: a communication-centric design (CCD) and a sensing-centric design (SCD), thereby introducing SIC ordering as a new dimension for managing the sensing-communication trade-off. For both proposed designs, we derive analytical expressions encompassing exact and asymptotic lower bounds of outage probabilities, ergodic communication rates for users, as well as probability of detection (PoD), probability of false alarm, and sensing sum rate for the base station. These analytical results provide a theoretical foundation for optimizing critical system parameters, such as power allocation and beamforming design. Our analysis reveals the synergistic effect of these impairments, leading to a simultaneous error floor in both communication and sensing performance. Notably, the proposed SIC ordering enables substantial performance gains in the prioritized domain: the CCD scheme improves ECRs by over 40%, whereas the SCD scheme enhances target PoD by more than 25%, under practical impairment conditions.

Integrated sensing and communication (ISAC) technology presents promising prospects for improving spectral efficiency, enabling hardware resource sharing, and facilitating novel application scenarios. Nevertheless, the mutual interference between communication and sensing remains a critical barrier to overcome. As an effective interference management solution, both non-orthogonal multiple access (NOMA) and rate splitting multiple access (RSMA) demonstrate unique advantages in interference suppression, which are expected to substantially enhance the overall performance of ISAC systems. To this end, the design options of NOMA versus RSMA for the ISAC systems are investigated in this paper. Furthermore, due to the inherent complexities in both signal propagation characteristics and receiver processing architectures, channel estimation errors (CEEs) and imperfect successive interference cancellation (ipSIC) are incorporated during system modeling. Against the above background, the communication and sensing performance of the NOMA and RSMA ISAC systems is analyzed by respectively deriving the exact and asymptotic outage probabilities (OPs) and ergodic rates (ERs) for the users, the probability of detection (PoD) for the base station and Cramér-Rao bound (CRB). The numerical results demonstrate that RSMA outperforms NOMA in terms of OPs, ERs, PoD, and CRB.

Integrated sensing and communication (ISAC) networks strive to deliver both high-precision target localization and high-throughput data services across the entire coverage area. In this work, we examine the fundamental trade-off between sensing and communication from the perspective of base station (BS) deployment. Furthermore, we conceive a design that simultaneously maximizes the target localization coverage, while guaranteeing the desired communication performance. In contrast to existing schemes optimized for a single target, an effective network-level approach has to ensure consistent localization accuracy throughout the entire service area. While employing time-of-flight (ToF) based localization, we first analyze the deployment problem from a localization-performance coverage perspective, aiming for minimizing the area Cramér-Rao Lower Bound (A-CRLB) to ensure uniformly high positioning accuracy across the service area. We prove that for a fixed number of BSs, uniformly scaling the service area by a factor$\kappa $increases the optimal A-CRLB in proportion to$\kappa ^{2 \beta }$, where$\beta $is the BS-to-target pathloss exponent. Based on this, we derive an approximate scaling law that links the achievable A-CRLB across the area of interest to the dimensionality of the sensing area. We also show that cooperative BSs extend the coverage but yield marginal A-CRLB improvement as the dimensionality of the sensing area grows. By exploiting the invariance properties discovered with respect to the displacement, rotation, and symmetric projection deformation, we derive a deployment-invariant structure for conceiving a low-complexity framework for ISAC network deployment. We then formulate the joint sensing-communication optimization problem and present a Majorization-Minimization algorithm for designing high-quality deployment solutions. Extensive simulations demonstrate that our framework significantly enhances sensing coverage, while maintaining the desired communication throughput.

This paper explores the use of data modulated by a spectrally efficient frequency division multiplexing (SEFDM) waveform for sensing. We first show that, if the presence of a cyclic prefix is assumed in the transmitted signal, the problem of multiple target sensing is tantamount to the detection and estimation of an unknown number of complex two-dimensional complex tones. Then, a novel iterative estimation method, based on a maximum likelihood approach, is developed to solve the last problem. Our simulation results evidence that SEFDM represents a valid technical option over static or slowly varying channels, and that the proposed method achieves a better accuracy-complexity trade-off than other estimation techniques available in the technical literature. In particular, our numerical results show that, in various scenarios, the proposed method achieves a 15% – 50% improvement in estimation accuracy with respect to other techniques with a limited computational complexity.

This paper introduces the spectrally efficient time-frequency space (SETFS) modulation, a two-dimensional (2D) modulation extending the concepts of spectrally efficient frequency division multiplexing (SEFDM) and orthogonal time–frequency space (OTFS) modulation by increasing the spectral overlap at two levels, i.e., in both frequency and Doppler domains. This double compression improves the use of the available spectrum. Moreover, in SETFS, the periodicity in both time and frequency is maintained through the use of a double cyclic prefix (DCP) in order to simplify channel equalization and avoid 2D inter-symbol interference (ISI). However, the presence of a spectral overlap unavoidably induces 2D inter-carrier interference (ICI), that makes symbol detection more complicated. To mitigate channel estimation errors and also support high-resolution sensing, a novel channel estimator, dubbed Newton-based estimation and spectral cancellation algorithm (NESCA), is proposed. Numerical results show that SETFS achieves up to a 24% gain in the achievable communication rate with respect to OTFS at the cost of higher receiver complexity. Combined with NESCA, SETFS provides near optimal delay and Doppler estimation, outperforming other three algorithms available in the technical literature. In terms of communication performance, SETFS employing the NESCA, followed by a minimum mean square error equalizer and iterative detector with soft symbol mapping, reaches quasi optimal capacity, while throughput saturation occurs with an SNR gap of about 10 dB in favor of OTFS-DCP and increasing with less advanced estimators. Nevertheless, SETFS combined with NESCA achieves a better communication-sensing tradeoff, improving spectral efficiency up to 25% with respect to OTFS at the price of a higher detection complexity. Overall, SETFS offers a relevant tradeoff between spectral efficiency, complexity and sensing accuracy, making it a promising candidate for future wireless systems.

In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum overall latency to guarantee the stringent sensing requirements. We consider a two-tier offloading framework, where each multi-antenna terminal can optionally offload its local tasks to either multiple mobile-edge servers for distributed computation or the cloud server for centralized computation. The above offloading problem is formulated as a mixed-integer programming and non-convex problem, which can be decomposed into three sub-problems, namely, distributed offloading decision, beamforming design, and execution scheduling mechanism. First, the continuous relaxation and penalty-based techniques are applied to tackle the distributed offloading strategy. Then, the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA)-based lower bound are utilized to design the integrated communication and sensing (ISAC) beamforming. Finally, the other resources can be judiciously scheduled to minimize the maximum latency. A rigorous convergence analysis and numerical results substantiate the effectiveness of our method. Furthermore, simulation results demonstrate the benefits of multi-point cooperation in cell-free massive MIMO-enabled ICCS and reveal the trade-off between the number of involved APs and the resulting latency, highlighting the inherent interplay among communication, sensing, and computation.

Affine Frequency Division Multiplexing (AFDM) has attracted considerable attention for its robustness to Doppler effects. However, its high receiver-side computational complexity remains a major barrier to practical deployment. To address this, we propose a novel symbol-level precoding (SLP)-based AFDM transmission framework, which shifts the signal processing burden in downlink communications from user side to the base station (BS), enabling direct symbol detection without requiring channel estimation or equalization at the receiver. Specifically, in the uplink phase, we propose a Sparse Bayesian Learning (SBL) based channel estimation algorithm by exploiting the inherent sparsity of affine frequency (AF) domain channels. In particular, the sparse prior is modeled via a hierarchical Laplace distribution, and parameters are iteratively updated using the Expectation-Maximization (EM) algorithm. We also derive the Bayesian Cramér-Rao Bound (BCRB) to characterize the theoretical performance limit. In the downlink phase, the BS employs the SLP technology to design the transmitted waveform based on the estimated uplink channel state information (CSI) and channel reciprocity. The resulting optimization problem is formulated as a second-order cone programming (SOCP) problem, and its dual problem is investigated by Lagrangian function and Karush–Kuhn–Tucker conditions. Simulation results demonstrate that the proposed SBL estimator outperforms traditional orthogonal matching pursuit (OMP) in accuracy and robustness to off-grid effects, while the SLP-based waveform design scheme achieves performance comparable to conventional AFDM receivers while significantly reducing the computational complexity at receiver, validating the practicality of our approach.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems based on the concept of exploiting constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Moreover, an “indirect power scaling” method is proposed for the precoding design based on DAC quantization to enhance the ISAC performance. From the simulation results, we can observe that the proposed precoding schemes can achieve satisfactory ISAC performance with low complexity. In the considered ISAC systems, increasing the quantization bits for DAC and PS quantizations can improve the ISAC performance, and the gain for DAC quantization is more pronounced.

This work explores signal transmission for serving multiple communication users (CUs) while simultaneously estimating multiple targets. In this context, the multi-stream signals intended for downlink CUs equipped with multiple antennas are also employed as probing signals for target estimation. We consider precoding design to ensure quality of service, quantified by the CUs’ individual rates and the mean squared error in target estimation. We develop path-following computational procedures that generate a sequence of improved feasible points by iterating closed-form expressions, ensuring convergence. As a byproduct, a computational solution for estimating the targets’ response vectors or their reflection coefficients and angles manifests, addressing long-standing open problems in estimation theory. Computational experiments not only demonstrate their consistency but also reveal that the obtained precoders produce highly directionally selective beampatterns toward the targets, even though this is not a primary objective.

This work investigates the integration of multi-target inference and multiuser communication in an active reconfigurable intelligent surface (aRIS)-assisted network. To infer the targets’ elevation and azimuth pairs and reflection coefficients from signals transmitted by a base station and reflected by the aRIS, which form computationally intractable nonlinear models, we develop a constructive minimum mean square error (MMSE) estimator based on their probability distribution functions. The resulting MSE is expressed analytically as a deterministic function of the probing signal, enabling its optimization. We then formulate the problem of jointly designing a beamformer and the aRISs power-amplified reconfigurable elements to ensure both accurate target inference and fair user rates. A computational program using closed-form updates is developed. Numerical results demonstrate a flexible trade-off between inference accuracy and achieved user rates.

In this paper, we introduce a novel framework for multiple access code design under the finite blocklength regime in multi-input and multi-output (MIMO) systems, termed coded pattern multiple access (CPMA). CPMA involves a series of multiple access code designs facilitated by a sparse pattern mapper/demapper, enabling independent information projection onto transmission patterns. Unlike existing approaches, the mapping and demapping of patterns are completely isolated components, ensuring energy-efficient transmission. In this work, we establish and analyze practical CPMA models, thoroughly investigating the performance limits of a potential non-bijective demapper. Closed-form and integral-form solutions are provided to describe these performance limits. Additionally, we present a practical application of CPMA: the coded pattern unsourced random access (CPURA) scheme. This scheme is designed for finite blocklength transmission under a quasi-static fading channel. The proposed CPURA achieves bound-approaching performance for large user groups, outperforming existing state-of-the-art methods in the context of massive machine-type communications (mMTC). Notably, the minimum required energy-per-bit to support 1,200 active users exhibits only a 1.3 dB gap from the achievable bound, validating the potential of the proposed CPMA framework.

Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in underlying environments for performance enhancement. Specifically, the additional reflective signal paths provided by these ambient devices are exploited to enhance sensing accuracy and communication reliability, respectively. In this system, we define the Pareto boundary characterizing the trade-off between sensing mutual information (SMI) and communication rates to provide fundamental insights for its design. To derive the boundary, we formulate a performance optimization problem within an orthogonal frequency division multiplexing (OFDM) framework, by jointly optimizing time-frequency resource element (RE) allocation, transmit power management, and BD modulation decisions. To tackle the non-convexity of the problem, we decompose it into three subproblems, solved iteratively through a block coordinate descent (BCD) algorithm. Specifically, the RE subproblem is addressed using the successive convex approximation (SCA) method, the power subproblem is solved using an augmented Lagrangian combined water-filling method, and the BD modulation subproblem is tackled using semidefinite relaxation (SDR) methods. Additionally, we demonstrate the generality of the proposed system by showing its adaptability to bistatic ISAC scenarios and MIMO settings. Finally, extensive simulation results validate the effectiveness of the proposed system and its superior performance compared to existing state-of-the-art ISAC schemes.

In recent years, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. Unfortunately, the high mobility, as well as perturbation, of UAV poses a great challenge in aligning narrow high-gain beams between the UAV and base station (BS). To tackle this challenging issue, we propose efficient beam prediction and tracking solutions from the perspective of control in this paper. First of all, for an important and typical flight mode in practice (i.e., the mission flight mode - to assign a series of targets in advance and fly from one target to the next one in turn), we study in depth the underlying control principle and reveal important properties and relationships between beam direction and controlled variables. Then, to exploit the properties and relationships revealed, we propose an efficient learning-based beam prediction and tracking solution. Specifically, we develop an efficient learning model, together with offline training and online inference algorithms. To further reduce the computational complexity, we distinguish two kinds of beam offsets and prove an important property of the mission flight mode, i.e., a multicopter almost keeps fixed attitude and velocity in most part of a flight process, based on which an efficient algorithm is designed. Comprehensive experiment results from open-source software, hardware and real UAV confirm the effectiveness of our control-assisted approach.

This paper investigates intelligent predictive beamforming design for simultaneous wireless information and power transfer-integrated sensing and communication (SWIPT-ISAC) systems for low-altitude economy wireless networks. Considering the downlink scenario where the base station aims to localize the moving targets/communication users and also transfer power to them, we formulate a weighted sum optimization problem to balance the trade-off between achievable communication rate and harvested energy, subject to sensing accuracy constraints defined by the Cramér–Rao lower bound. To address the non-convexity of the problem, we propose the Time-Spatial Fusion Network (TSFusionNet), an unsupervised deep learning (DL) framework that leverages multi-step historical channel state information for predictive beamforming design. TSFusionNet integrates convolutional and recurrent layers with a differential attention mechanism to capture spatial-temporal dependencies and mitigate non-stationary channel dynamics. We introduce a dynamic penalty-based loss function to enforce sensing constraints during training. Simulation results show that by adjusting the weight factor, the proposed method achieves a trade-off between rate and energy while meeting sensing accuracy requirements. Moreover, it significantly reduces computational complexity by up to approximately 96.8% in parameters and 81.5% in FLOPs, compared to existing DL frameworks.

In this paper, we propose leveraging rotatable antennas (RAs) to enhance near-field communication and sensing performance by exploiting a new spatial degree-of-freedom (DoF) offered by array rotation. Specifically, we investigate an RA-aided near-field integrated sensing and communication (ISAC) system, where the transmit beamformers and the array rotation angle at the base station (BS) are jointly optimized to minimize the Cramér-Rao bounds (CRBs) for angle and range estimation, while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for communication users. To gain important insights into the impact of RAs on near-field ISAC, we analyze two special cases:communication-onlyandsensing-onlytransmission. For the communication-only case, we derive therotation-awarechannel path correlation using the Fresnel integrals and analytically demonstrate that RAs provide an additional rotation gain, thereby improving communication performance. For the sensing-only case, we derive closed-formrotation-awareCRBs for near-field angle and range estimation under bothisotropicanddirectionalbeamformers. It is theoretically unveiled that array rotation improves sensing performance by concurrently reducing both CRBs. Interestingly, the optimal rotation angles that minimize these CRBs are identical. Subsequently, to address the resultant non-convex optimization problem, we propose adouble-layeralgorithm to obtain a high-quality solution, where the inner layer optimizes the transmit beamformers using semidefinite relaxation (SDR), while the outer layer determines the array rotation through a one-dimensional exhaustive search. Finally, numerical results highlight the significant performance gains of the developed RA-aided near-field ISAC system over conventional fixed-antenna ISAC systems.

This paper investigates fluid antenna systems (FASs) subject to finite-blocklength (FBL) constraints, motivated by the strict reliability-latency and ultra-massive connectivity requirements of future wireless networks. While FAS performance has been widely studied in the asymptotic regime, its behavior under FBL remains largely unexplored. Our objective is to develop a unified set of analytical tools for evaluating FASs under FBL that remains applicable across different spatial-correlation models. First, to establish accurate benchmarks for non-orthogonal finite-length user signature design, we characterize both the average and the worst-case correlation coefficients via extreme value theory (EVT) and derive closed-form predictions of the achievable correlation levels. Second, taking block error rate (BLER) as the fundamental FBL metric, we study joint detection and decoding in FAS-assisted links and derive a closed-form BLER expression that is universally applicable across channel models. Additionally, we revisit outage probability (OP) in the FBL regime and obtain tractable OP characterizations for both FASs and conventional multiple fixed-position antenna (FPA) systems. In order to reduce the computational burden for multi-fold integrals in correlated fading models, we further propose a Taylor-expansion-assisted mean value theorem for integrals (MVTI), thus enabling efficient performance evaluation with marginal accuracy loss. Numerical results validate the analysis and reveal that even single-antenna FASs can have superior spatial diversity relative to conventional multi-FPA systems. Moreover, under both FBL and interference-limited environments, FASs provide improved energy, spectral, and hardware efficiencies, hence highlighting FAS as a promising enabler for next-generation wireless networks.

Integrated sensing and communication enables simultaneous communication and sensing tasks, including precise radio positioning and mapping, essential for future 6G networks. Current methods typically model environmental landmarks as isolated incidence points or small reflection areas, lacking detailed attributes essential for advanced environmental interpretation. This paper addresses these limitations by developing an end-to-end cooperative uplink framework involving multiple base stations and users. Our method uniquely estimates extended landmark objects and incorporates obstruction-based outlier removal to mitigate multi-bounce signal effects. Validation using realistic ray-tracing data demonstrates substantial improvements in the richness of the estimated environmental map.

This paper exploits the integrated sensing and communication (ISAC) technology in unmanned aerial vehicle (UAV) networks, where multiple UAVs collaboratively form a virtual antenna array (VAA) within a pre-determined area, to effectively operate as a multi-antenna system for communication and sensing (C&S) services. Since the VAA is an extremely-large antenna array, the near-field characteristics must be considered in C&S channels. By optimizing UAV positions, we construct an enhanced VAA configuration and subsequently design the corresponding beamforming, thereby improving sensing performance while guaranteeing communication requirements. A penalty-based iterative algorithm is exploited to address the resulting optimization problem. Simulation results demonstrate that the optimized VAA with beamforming significantly enhances target localization accuracy compared to conventional schemes, validating the effectiveness of the ISAC implementation in UAV networks.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

Given the challenges of high mobility and frequent handovers in 5G NR's Vehicle-to-Infrastructure (V2I) communications, the Integrated Sensing and Communications (ISAC) technique, now recognized in IMT-2030 as a usage scenario, is introduced to boost mobile network efficiency. Leveraging this emerging paradigm, our study delves into a V2I network within the 5G NR context, proposing a sensing-assisted handover mechanism and protocol that integrates ISAC to refine the intercell handover process. This mechanism uses precise beamforming and kinematic parameter estimation to reduce signaling and interruption time in inter-cell handover process. This approach enables a distance-based handover triggering mechanism that is both proactive and information-rich, allowing the serving gNB to inform the target gNB of the vehicle's location preemptively. Numerical results from link-level simulations at a crossroad scenario demonstrate that this sensing-assisted handover mechanism enables faster triggering and reduces the interruption time by 76.46%, while enhancing communication performance.

In this work, we explore symbol-level precoding (SLP) and efficient decoding strategies for downlink transmission in multi-user multiple-input multiple-output (MU-MIMO) systems. We specifically study scenarios where the base station (BS) sends multiple multi-level modulated data streams to users for decoding. We formulate an optimization problem for joint symbollevel transmit precoding and receive combining. However, the receive combining matrix is dependent on the transmit symbols in the joint design scheme, thus, we employ maximum likelihood detection (MLD) method at the receiver side. we demonstrate that the smallest singular value of the precoding matrix significantly affects the MLD performance, while traditional SLP scheme returns a rank-one precoding matrix, which results in inferior error-rate performance to users. To overcome this challenge, we propose a novel channel state information (CSI)-Free SLP scheme that employs semidefinite programming (SDP) method to enable SLP technique in systems utilizing MLD decoding, where the design of the precoding matrix depends only on the modulated data symbols. Numerical simulations confirm that our proposed scheme substantially outperforms the traditional block diagonalization methods.

Because of the flexible scheduling, improved reliability, enhanced capacity over much wider range, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. However, the high mobility and perturbation of UAV impose a challenge on aligning narrow beams between the UAV and another node, such as the base station (BS). Although the position and attitude of UAV have been exploited to develop beam tracking algorithms, they belong to current or past information, which often provide limited performance improvement in the high-mobility scenario. To tackle this challenging issue, we, for the first time, identify a kind of important but ready-made information - the command or control sequence (CCS) provided by the flight control system (FCS). We explain in detail that CCS provides real and direct (rather than estimated) future information for beam prediction. Then, we propose an efficient learning-based algorithm to exploit the information. In particular, we prove theoretically that the convolutional neural network (CNN) is an appropriate choice of the network structure within the nonlinear prediction model. Experiment results from practical real UAVs confirm the effectiveness and superiority of our proposal.

In this work, we discuss the problem of unsourced random access (URA) over a Gaussian multiple access channel (GMAC). To address the challenges posed by emerging massive machine-type connectivity, URA reframes multiple access as a coding-theoretic problem. The sparse code-oriented schemes are highly valued because they are widely used in existing protocols, making their implementation require only minimal changes to current networks. However, drawbacks such as the heavy reliance on extrinsic feedback from powerful channel codes and the lack of transmission robustness pose obstacles to the development of sparse codes. To address these drawbacks, a novel sparse code structure based on a universally applicable power division strategy is proposed. Comprehensive numerical results validate the effectiveness of the proposed scheme. Specifically, by employing the proposed power division method, which is derived analytically and does not require extensive simulations, a performance improvement of approximately 2.8 dB is achieved compared to schemes with identical channel code setups.

This paper addresses the unsourced/uncoordinated random access problem in an integrated sensing and communications (ISAC) system, with a focus on uplink multiple access code design. Recent theoretical advancements highlight that an ISAC system will be overwhelmed by the increasing number of active devices, driven by the growth of massive machine-type communication (mMTC). To meet the demands of future mMTC network, fundamental solutions are required that ensure robust capacity while maintaining favorable energy and spectral efficiency. One promising approach to support emerging massive connectivity is the development of systems based on the unsourced ISAC (UNISAC) framework. This paper proposes a spectrum-sharing compressive sensing-based UNISAC (SSCS-UNISAC) and offers insights into the practical design of UNISAC multiple access codes. In this framework, both communication signals (data transmission) and sensing signals (e.g., radar echoes) overlap within finite channel uses and are transmitted via the proposed UNISAC protocol. The proposed decoder exhibits robust performance, providing 20-30 dB capacity gains compared to conventional protocols such as TDMA and ALOHA. Numerical results validate the promising performance of the proposed scheme.

This work studies a cooperative architecture for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate the allocation of antennas-to-base stations (BSs) as a means to optimize antenna densities and explore the range between massive MIMO and cell-free typologies, and their effects on cooperative sensing and cooperative communication performance. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)-based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, to comprehensively assess their effects on ISAC network performance. In networks with multiple ISAC nodes following a Poisson point process, the Cramér-Rao lower bound (CRLB) for time of flight (TOF)-based methods decreases with the square of the logarithm of the number of nodes, for angle of arrival (AOA)-based methods with the logarithm, and for hybrid methods as a mix of both. In terms of communication performance, we derive a tractable expression for the communication data rate under various cooperative region sizes. The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

We propose a cooperative integrated sensing and communication (ISAC) architecture that integrates coordinated multi-point (CoMP) communication with multi-static sensing. This study investigates how the allocation of a fixed total number of antennas among base stations (BSs) affects sensing and communication performance, and optimizes both the antenna topology and the number of BSs. To this end, we formulate an antenna topology optimization problem to balance the advantages of centralized and distributed antennas. Specifically, centralized antennas enhance beamforming and coherent processing in massive multiple-input and multiple-output (MIMO) systems, while distributed antennas improve spatial diversity and reduce access distances in cell-free setups. For sensing, we evaluate two localization methods, angle-of-arrival (AOA)-based and time-of-flight (TOF)-based localizations, to analyze how their scaling laws affect overall network performance. We analyze and demonstrate that the synchronization errors in our proposed architecture are negligible, thereby providing theoretical support for system implementation. In terms of communication, our results indicate that higher path loss exponents favour distributed configurations, while lower exponents benefit centralized setups. Simulations confirm that our cooperative scheme outperforms non-cooperative approaches, surpassing purely centralized or distributed strategies.

Indoor localization is a critical component of various applications, including assisted living, personnel monitoring, and asset tracking. Traditional localization methods relying on specialized sensors such as LiDAR, ultrasound, and 3D cameras offer high precision but suffer from high costs and limited interoperability. To address these challenges, this paper explores the Integrated Sensing and Communication (ISAC) paradigm, leveraging signal-based modalities focusing on received signal strength indication (RSSI). These measurements, inherently embedded in wireless communication packets, enable cost-effective and vendor-agnostic localization without the need for additional hardware. However, practical deployment remains challenging due to signal degradation from obstacles, multipath effects, and reflections. This paper presents an end-to-end localization framework utilizing COTS IoT devices and advanced RSSI processing techniques to enhance measurement reliability. By integrating filtering mechanisms and machine learning models, the proposed solution improves distance estimation, categorizes line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and enhances localization accuracy. Additionally, an open and extendable edge-to-cloud infrastructure supports scalability and real-time processing. Experimental evaluation demonstrate the effectiveness of this approach in various aspects such as increase of RSSI reliability (increase of up to 82 % regarding standard deviation, drastic reduction of outliers' detection and fluctuation of more than 90 % to distances up to 2 m), accurate LoS-NLoS classification up to 99 % and overall localization increased accuracy more than 83 %.

Orthogonal time sequency multiplexing (OTSM) has arisen as a promising single-carrier waveform, providing reliable performance akin to orthogonal time frequency space (OTFS) while surpassing orthogonal frequency division multiplexing (OFDM) in high-mobility doubly spread channels (DSCs), yet with significantly lower complexity. To alleviate both time and frequency dispersions in DSCs, delay-Doppler alignment modulation (DDAM) has been recently proposed for the systems operating at millimeter-wave (mmWave) and higher frequency bands. Building on the promising combination of OTSM with DDAM, this paper presents for the first time, waveform optimization in mmWave massive multiple-input multiple-output (mMIMO) integrated sensing, communication, and wireless power transfer (ISCAP) systems. We propose an OTSM-DDAM-based ISCAP system and derive the system’s input-output relations in time, delay-time, and delay-sequency (DS) domains based on delay-Doppler (DD) bin alignment in the presence of fractional DD shifts and by incorporating transceiver hardware impairments. This facilitates the derivation of key performance metrics including bit error rate (BER), spectral efficiency (SE), Cramér-Rao bound (CRB) for sensing, and energy harvested via wireless power transfer (WPT). By harnessing the advantage actor-critic method combined with a greedy strategy, an on-policy deep reinforcement learning algorithm is introduced to solve a newly defined optimization problem. This approach optimizes the ISCAP waveform while simultaneously recognizing the environment, providing the base station with the path state information needed for practical DDAM implementation. The optimization problem jointly considers precoding, power loading, time and power splitting ratios, beam alignment, and receive combining with the goal of minimizing the transmitted power coupled with CRB while maintaining constraints on signal-to-interference-plus-noise ratio, BER, SE, CRB, harvested energy, and total power budget to ensure high-quality ISCAP services. Simulation results, based on both real-world and deep learning datasets, validate the effectiveness of the proposed scheme, demonstrating improvements in peak-to-average power ratio, SE, detection complexity, CRB, and BER in high-mobility DSCs.

The increase in antenna apertures and transmission frequencies in next-generation wireless networks is catalyzing advancements in near-field communications (NFC). In this paper, we investigate secure transmission in near-field multi-user multiple-input single-output (MU-MISO) scenarios. Specifically, with the advent of extremely large-scale antenna arrays (ELAA) applied in the NFC regime, the spatial degrees of freedom in the channel matrix are significantly enhanced. This creates an expanded null space that can be exploited for designing secure communication schemes. Motivated by this observation, we propose a near-field dynamic hybrid beamforming architecture incorporating artificial noise, which effectively disrupts eavesdroppers at any undesired positions, even in the absence of their channel state information (CSI). Furthermore, we comprehensively analyze the dynamic precoder’s performance in terms of the average signal-to-interference-plus-noise ratio, achievable rate, secrecy capacity, secrecy outage probability, and the size of the secrecy zone. In contrast to far-field secure transmission techniques that only enhance security in the angular dimension, the proposed algorithm exploits the unique properties of spherical wave characteristics in NFC to achieve secure transmission in both the angular and distance dimensions. Remarkably, the proposed algorithm is applicable to arbitrary modulation types and array configurations. Numerical results demonstrate that the proposed method achieves approximately 20% higher rate capacity compared to zero-forcing and the weighted minimum mean squared error precoders.

Both dual-functional radar-communication (DFRC) and massive multiple-input multiple-output (MIMO) have been recognized as enabling technologies for 6G wireless networks. This paper considers the advanced waveform design for hardware-efficient massive MIMO DFRC systems. Specifically, the transmit waveform is imposed with the quantized constant-envelope (QCE) constraint, which facilitates the employment of low-resolution digital-to-analog converters (DACs) and power-efficient amplifiers. The waveform design problem is formulated as the minimization of the mean square error (MSE) between the designed and desired beampatterns subject to the constructive interference (CI)-based communication quality of service (QoS) constraints and the QCE constraint. To solve the formulated problem, we first utilize the penalty technique to transform the discrete problem into an equivalent continuous penalty model. Then, we propose an inexact augmented Lagrangian method (ALM) algorithm for solving the penalty model. In particular, the ALM subproblem at each iteration is solved by a custom-built block successive upper-bound minimization (BSUM) algorithm, which admits closed-form updates, making the proposed inexact ALM algorithm computationally efficient. Simulation results demonstrate the superiority of the proposed approach over existing state-of-the-art ones. In addition, extensive simulations are conducted to examine the impact of various system parameters on the trade-off between communication and radar performances.

Visible Light Positioning and Communication (VLPC) is a promising candidate for implementing Integrated Sensing And Communication (ISAC) in the unlicensed 400 THz to 800 THz band. The current Visible Light Positioning (VLP) systems mainly operate based on the Received Signal Strength (RSS) of the Line-of-Sight (LoS) path. However, its accuracy is degraded by interferences from Non-LoS (NLoS) paths. Furthermore, in Visible Light Communication (VLC) systems, the estimation of Channel State Information (CSI) also becomes challenging, when the optical channel becomes dispersive. Against this background, we propose a new VLPC scheme using Direct Current (DC) biased Optical Orthogonal Frequency-Division Multiplexing (VLPC-DCO-OFDM), where OFDM-based sensing is applied for the sake of improving the resolution of the estimated Channel Impulse Response (CIRs) exploited for positioning functionality. The CIRs estimated by sensing are further exploited to provide enhanced CSI for communication data detection. Moreover, we propose a hybrid Radar-RSS based solution, where the conventional RSS-aided VLP method is invoked for the sake of refining OFDM radar. Our simulation results demonstrate that the proposed VLPC-DCO-OFDM scheme – which simultaneously supports the triple functionalities of illumination, bi-static sensing and communication – is capable of achieving centimeter-level positioning accuracy and Giga-bits-per-second data rate.

In this paper, we investigate symbol-level precoding (SLP) and efficient decoding techniques for downlink transmission, where we focus on scenarios where the base station (BS) transmits multiple quadrature amplitude modulation (QAM) constellation streams to users equipped with multiple receive antennas. We begin by formulating a symbol-level joint design scheme aimed at collaboratively optimizing the transmit precoding and receive combining matrices. This coupled problem is addressed by employing the alternating optimization (AO) method, and closed-form solutions are derived by analyzing the obtained two subproblems. Furthermore, to address the dependence of the receive combining matrix on the transmit signals, we switch to maximum likelihood detection (MLD) method for decoding. Notably, we have demonstrated that the smallest singular value of the precoding matrix significantly impacts the performance of MLD method. Specifically, a lower value of the smallest singular value results in degraded detection performance. Additionally, we show that the traditional SLP matrix is rank-one, making it infeasible to directly apply MLD at the receiver end. To circumvent this limitation, we propose a novel symbol-level smallest singular value maximization problem, termed SSVMP, to enable SLP in systems where users employ the MLD decoding approach. Moreover, to reduce the number of variables to be optimized, we further derive a more generic semidefinite programming (SDP)-based optimization problem. Numerical results validate the effectiveness of our proposed schemes and demonstrate that they significantly outperform the traditional block diagonalization (BD)-based method.

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case when the number of symbol slots in a given transmission block is smaller than the number of users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain a closed-form structure of the optimal CI-BLP precoding matrix. Similar to the case when the number of symbol slots in a given transmission block is not smaller than the number of users, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can achieve a flexible trade-off between communication performance and execution time by modifying the maximum number of iterations. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the QP optimization, and further show that the proposed ADMM algorithm can provide satisfactory results in dozens of iterations, which motivates the use of CI-BLP in practical wireless systems.

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical “window function” by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.

This paper designs a parallel solution framework for constructive interference based symbol-level precoding (CI-SLP) in the downlink of a multi-user multiple-input single-output (MU-MISO) system. Most existing works on SLP have considered the sum-power constraint, while in practical systems each transmit antenna is equipped with its dedicated power amplifier. Therefore, it is more realistic to design SLP approaches that incorporate the per-antenna power constraint (PAPC). In this paper, we focus on two specific PAPC-based problems: the constructive interference per-antenna power constraint signal to interference plus noise ratio (SINR) balancing (CI-PASB) problem and the constructive interference per-antenna peak power minimization (CI-PAPM) problem. Similar to sum-power constraint, for the CI-PASB problem, we demonstrate that it is separable, allowing the existing parallel proximal Jacobian alternating direction method of multipliers (PJ-ADMM) algorithm to be directly used. As for the CI-PAPM problem, although it is unseparable, we can leverage the established duality to obtain its solution based on the solution of the corresponding CI-PASB problem. Numerical results verify our proposed parallel methods and show that they are more efficient than the existing centralized schemes, which showcases the advantages of parallel computing and promotes the implementation of CI precoding under practical PAPC scenarios.

In response to growing demands for both sensing and communication performance from future applications of wireless networks, simultaneous transmitting and reflecting intelligent reflecting surface (STAR-IRS), sparse code multiple access (SCMA), and integrated sensing and communication (ISAC) have been introduced. In this paper, we investigate the joint beamforming and resource allocation optimization problem (OP) for STAR-IRS-Aided SCMA for an ISAC System by considering ISAC, SCMA, beamforming, quality of service (QoS), and STAR-IRS constraints in the THz band. The investigated OP is a type of NP-hard OP that cannot be solved by conventional methods. Moreover, the possible solution must have the ability to adapt to different situations. Then to solve this problem efficiently, we employ a new meta deep reinforcement learning (MDRL). The simulation results demonstrate the effectiveness of the proposed solution for the resource allocation problem across different system model and MDRL parameters, as well as multiple access methods. Compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), the proposed system model improves energy efficiency (EE) by 15% and 20%, respectively. The suggested MDRL approach achieves a 123% enhancement over classic deep deterministic policy gradient (DDPG). Additionally, a scenario involving a coupled phase-shift model was conducted for the proposed system model. To investigate the effect of channel state information (CSI) estimation on our proposed system model, we considered a simulation with both imperfect and perfect CSI. Additionally, when solving the optimization problem using a convex optimization approach, MDRL shows a 10% improvement.

Constructive interference (CI) precoding, which converts the harmful multi-user interference into beneficial signals, is a promising and efficient interference management scheme in multi-antenna communication systems. However, CI-based symbol-level precoding (SLP) experiences high computational complexity as the number of symbol slots increases within a transmission block, rendering it unaffordable in practical communication systems. In this paper, we propose a symbol-level extrapolation (SLE) strategy to extrapolate the precoding matrix by leveraging the relationship between different symbol slots within in a transmission block, during which the channel state information (CSI) remains constant, where we design a closed-form iterative algorithm based on SLE for both PSK and QAM modulation. In order to further reduce the computational complexity, a sub-optimal closed-form solution based on SLE is further developed for PSK and QAM, respectively. Moreover, we design an unsupervised SLE-based neural network (SLE-Net) to unfold the proposed iterative algorithm, which helps enhance the interpretability of the neural network. By carefully designing the loss function of the SLE-Net, the time-complexity of the network can be reduced effectively. Extensive simulation results illustrate that the proposed algorithms can dramatically reduce the computational complexity and time complexity with only marginal performance loss, compared with the conventional SLP design methods.

Integrated sensing and communications (ISAC) has emerged as a pivotal enabling technology for next-generation wireless networks. Despite the distinct signal design requirements of sensing and communication (S&C) systems, shifting the symbol-wise pulse shaping (SWiPS) framework from communication-only systems to ISAC poses significant challenges in signal design and processing This paper addresses these challenges by examining the ambiguity function (AF) of the SWiPS ISAC signal and introducing a novel pulse shaping design for single-carrier ISAC transmission. We formulate optimization problems to minimize the average integrated sidelobe level (ISL) of the AF, as well as the weighted ISL (WISL) while satisfying inter-symbol interference (ISI), out-of-band emission (OOBE), and power constraints. Our contributions include establishing the relationship between the AFs of both the random data symbols and signaling pulses, analyzing the statistical characteristics of the AF, and developing algorithmic frameworks for pulse shaping optimization using successive convex approximation (SCA) and alternating direction method of multipliers (ADMM) approaches. Numerical results are provided to validate our theoretical analysis, which demonstrate significant performance improvements in the proposed SWiPS design compared to the root-raised cosine (RRC) pulse shaping for conventional communication systems.

A cooperative architecture is proposed for integrated sensing and communication (ISAC) networks, incorporating coordinated multi-point (CoMP) transmission along with multi-static sensing. We investigate how the allocation of antennas-to-base stations (BSs) affects cooperative sensing and cooperative communication performance. More explicitly, we balance the benefits of geographically concentrated antennas in the massive multiple input multiple output (MIMO) fashion, which enhance beamforming and coherent processing, against those of geographically distributed antennas towards cell-free transmission, which improve diversity and reduce service distances. Regarding sensing performance, we investigate three localization methods: angle-of-arrival (AOA)- based, time-of-flight (TOF)-based, and a hybrid approach combining both AOA and TOF measurements, for critically appraising their effects on ISAC network performance. Our analysis shows that in networks havingNISAC nodes following a Poisson point process, the localization accuracy of TOF-based methods follows a ln2Nscaling law (explicitly, the Cramér-Rao lower bound (CRLB) reduces with ln2N). The AOA-based methods follow a lnNscaling law, while the hybrid methods scale asaln2N+blnN, whereaandbrepresent parameters related to TOF and AOA measurements, respectively. The difference between these scaling laws arises from the distinct ways in which measurement results are converted into the target location. Specifically, when converting AOA measurements to the target location, the localization error introduced during this conversion is inversely proportional to the distance between the BS and the target, leading to a more significant reduction in accuracy as the number of transceivers increases. In contrast, TOF-based localization avoids such distance dependent errors in the conversion process. In terms of communication performance, we derive a tractable expression for the communication data rate, considering various cooperative region sizes and antenna-to-BS allocation strategy. It is proved that higher path loss exponents favor distributed antenna allocation to reduce access distances, while lower exponents favor centralized antenna allocation to maximize beamforming gain. Simulations confirm that cooperative transmission and sensing in ISAC networks can effectively improve non-cooperative sensing and communication performance The proposed cooperative scheme shows superior performance improvement compared to centralized or distributed antenna allocation strategies.

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the derived Cramér-Rao lower bound (CRLB) expression of the localization accuracy unveils a significant finding: DeployingNISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the$\ln ^{2}N$scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of$N^{2}$achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance. Simulation results demonstrate that compared to the conventional time-sharing scheme or a non-cooperative scheme, the proposed cooperative ISAC scheme can effectively improve the average data rate and reduce the CRLB, hence striking an improved S&C performance tradeoff at the network level.

In this paper, we investigate the constructive interference (CI) based symbol-level precoding (SLP) design for integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) serves multiple single-antenna communication users while simultaneously detecting targets of interest. Specifically, the minimum communication CI scaling factor among the users is maximized under radar performance constraint and power constraint. In order to solve the proposed optimization problem, two groups of approximate feasible domains are adopted to transform the optimization problem into convex. In order to improve the efficiency of the proposed precoding scheme, we adopt a modified Hooke-Jeeves pattern search algorithm for the convex subproblems. We further propose a weighted optimization scheme which considers the tradeoff between radar performance and communication performance as the objective function. By analyzing the Lagrangian function and Karush-Kuhn-Tucker (KKT) condition of the weighted optimization problem, we formulate the corresponding dual problem, which is a simple quadratic programming (QP) problem and can be easily solved. In addition, we further extend the proposed CI precoding scheme from symbol level to block level, in order to be more consistent with the currently used communication systems and achieve better ISAC performance. Extensive simulation results are provided to demonstrate the advantages and the effectiveness of the proposed symbol-scaling based CI-SLP design and CI-based block-level precoding (CI-BLP) design in ISAC systems.

Asynchronous radio transceivers often lead to significant range and velocity ambiguity, posing challenges for precise positioning and velocity estimation in passive-sensing perceptive mobile networks (PMNs). To address this issue, carrier frequency offset (CFO) and time offset (TO) synchronization algorithms have been studied in the literature. However, their performance can be significantly affected by the specific choice of the utilized window functions. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We first derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then develop a practical window selection criterion and test a special window generated by the super-resolution algorithm. Numerical simulation has verified our analysis.

Future wireless networks targeted for improving spectral and energy efficiency, are expected to simultaneously provide sensing functionality and support low-power communications. This paper proposes a novel net-zero integrated sensing and communication (ISAC) model for backscatter systems, including an access point (AP), a net-zero device, and a user receiver. We fully utilize the backscatter mechanism for sensing and communication without additional power consumption and signal processing in the hardware device, which reduces the system complexity and makes it feasible for practical applications. To further optimize the system performance, we design a novel signal frame structure for the ISAC model that effectively mitigates communication interference at the transmitter, tag, and receiver. Additionally, we employ distributed antennas for sensing which can be placed flexibly to capture a wider range of signals from diverse angles and distances, thereby improving the accuracy of sensing. We derive theoretical expressions for the symbol error rate (SER) and tag’s location detection probability, and provide a detailed analysis of how the system parameters, such as transmit power and tag’s reflection coefficient, affect the system performance.

n this paper, we analyze the performance tradeoff between integrated radar and communications (RadCom) systems using the Kullback-Leibler divergence (KLD) measure, also called the relative entropy (RE). Specifically, we derive the KLD measure for both subsystems for a base-station serving a number of communication users while detecting multiple targets simultaneously. We evaluate the trade-off between the radar and the communication systems using a weighted KLD that can enhance the flexibility of the allocations. The results demonstrate that the KLD is an effective mean for achieving the optimal tradeoff between both systems and that it provides a higher degree of controllablity and adaptability for RadCom systems.

While symbol-level (SL) precoders have been shown to improve transmission performance by treating multi-user interference (MUI) as a useful resource, the SL precoders only employ uniform modulation for all downlink users, and the incurred complexity increases linearly with the block length. In this letter, we investigate the possibility of mixed-modulations interference exploitation (IE) for satellite communications, at a sub-block level. By exploiting the specific detection regions of constellation points of different modulations, a novel sub-block level mixed-modulation (BL-MIE) design is proposed, guaranteeing that MUI is always constructive in each sub-block duration, regardless of the users’ heterogeneous modulation schemes. Compared to the classic SL design, it is proved that the BL-MIE provides complexity reduction on the order of square root of the sub-block length, i.e., ${\mathcal{O}}(\sqrt{n})$, with n denoting the number of symbols per sub-block. Hence, it well strikes the balance between the performance and complexity. Simulation demonstrates that the proposed designs significantly outperform the benchmarks in terms of power consumption and throughput performance.

This paper introduces a dual-function radar-communication (DFRC) system with cognitive radio capability to tackle the spectral scarcity problem in wireless communications. Particularly, a cognitive DFRC system operates on a spectrum owned by a primary system to simultaneously perform data communication and target tracking while maintaining its interference to the primary users (PUs) below a certain threshold. To achieve this, an optimization problem is formulated to jointly design the beamforming vectors for both the radar and communication functions in minimizing the mean square error (MSE) of the beam patterns between the designed and desired waveforms under three constraints: i) the signal-to-interference-plus-noise ratio (SINR) at each data communication user; ii) the perantenna transmit power; and iii) the interference imposed on each PU. The semidefinite relaxation technique is utilized to search for the optimal solution to the optimization problem. The simulation results indicate that our proposed cognitive DFRC approach can effectively protect the PUs while simultaneously perform its communication and radar functions.

In this study, we explore integrated sensing and communication (ISAC) networks to strike a more effective balance between sensing and communication (S&C) performance at the network scale. We leverage stochastic geometry to analyze the S&C performance, shedding light on critical cooperative dependencies of ISAC networks. According to the derived expres-sions of network performance, we optimize the user/target loads and the cooperative base station cluster sizes for S&C to achieve a flexible trade-off between network-scale S&C performance. It is observed that the optimal strategy emphasizes the full utilization of spatial resources to enhance multiplexing and diversity gain when maximizing communication ASE. In contrast, for sensing objectives, parts of spatial resources are allocated to cancel inter-cell sensing interference to maximize sensing ASE. Simulation results validate that the proposed ISAC scheme realizes a remarkable enhancement in overall S&C network performance.

We study constructive interference based block-level beamforming (CI-BLB) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. CI-BLB achieves im-proved performance over the traditional CI-based symbol-level beamforming (CI-SLB) method, because of its more intelligent power allocation scheme over the considered block of symbol slots. In this paper, we design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can efficiently solve QP problems. We analyze the convergence and complexity of the proposed algorithm. Nu-merical results validate the optimality of the proposed algorithm, and further show that the proposed algorithm offers a flexible performance-complexity tradeoff by limiting the maximum num-ber of iterations, which motivates the use of CI - BLB in practical wireless systems.

The success of the Internet of Things has recently spread to the industrial sector, commonly referred to as Industrial IoT (IIoT). IIoT, which has a far-reaching impact on the operation of industries around the world, is recognized as a key enabler for the fourth industrial revolution. It has the potential to prompt economic growth and global competitiveness, in terms of improving productivity, efficiency, and so on.

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user’s echo signals, and non-orthogonal power allocation is conducted to achieve a superior achievable rate. A 3D motion prediction topology is used to guide the NOMA transmission for multiple users, and a robust power allocation solution is proposed under perfect and imperfect channel estimation for max-min fairness (MMF) and maximum sum-rate (SR) problems. Simulation results demonstrate the superiority of the proposed NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate under both perfect and imperfect channel conditions with the aid of 3D motion prediction topology.

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.

Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.

Underwater covert communication (UCC) technology can prevent legitimate transmission from being intercepted upon by potential eavesdroppers while ensuring a certain rate at the receiver under the condition of underwater acoustic channels. Previous studies have focused on UCC designs that rely on fixed transmitters and receivers, with limited attention given to dynamic moving senders, such as the widely-used autonomous underwater vehicle (AUV). Therefore, the establishment of a secure link between the mobile AUV and the receiver remains unexplored. In this paper, we construct an AUV-aided UCC architecture. Specifically, leveraging the unique characteristics of the underwater environment i.e., time-variant channel, severe attenuation, and ambient noise, the AUV plans its trajectory from the settled start point to the destination, adjusting its transmission power for covert communications. Accounting for both green energy consumption and communication security, we develop a novel multi-objective deep deterministic policy gradient (MODDPG) framework for jointly optimizing AUV’s diving energy consumption as well as effective throughput under the covertness constraint. Moreover, we propose an active-trust mechanism at the receiving side to pose an extra safe guard. To handle this, an evolutionary game model between the receiver and eavesdropper is built. Simulations and numerical results demonstrate that our proposed method can achieve a Pareto-optimal solution for covert communications with rapid convergence speed. The evolutionary stable strategy (ESS) enables the receiver to attain superior benefits and security compared to other strategies.

The growth of frequency bandwidths and applications with the forthcoming generations of wireless networks will give rise to a multitude of wireless transmission scenarios, topologies and channel structures. In this work, we go beyond existing learning-based channel estimation methods tailored for specific scenarios, to develop an adaptive learning-based channel state information (CSI) estimation approach. We offer the adaptivity in the learning approach through extracting the scenario embeddings of CSI and adjusting the channel estimation method with the extracted information automatically in each scenario. Specifically, Learning-Based Scenario-Adaptive Channel Estimation Algorithm (LACE) is designed. LACE is based on a Scenario-Aware Hyper-Network (SAH-Net) that incorporates the embedding loss to make the Convolutional Neural Network (CNN) based encoder learn to extract the effective scenario embeddings from the time-space two dimensional features of the CSI. The extracted embeddings are utilized by a Multi-Layer Perceptron (MLP) based tuning module to tune the parameters of the channel estimation method. Our learning design is complemented with analysis to verify that the theoretical performance of LACE is strictly superior to that of the mix-training method, which involves conventionally training the deep network-based channel estimation method using samples from all scenarios. Our results show that the performance of LACE trained in finite scenarios is comparable to that of the deep network-based channel estimation method trained in each scenario, while having lower complexity. Further more, the performance of LACE trained in infinite scenarios is demonstrated to be superior to that of the mix-training method in all test scenarios.

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramér-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.

The emergence of the fifth-generation (5G) New Radio (NR) technology has provided unprecedented opportunities for vehicle-to-everything (V2X) networks, enabling enhanced quality of services. However, high-mobility V2X networks require frequent handovers and acquiring accurate channel state information (CSI) necessitates the utilization of pilot signals, leading to increased overhead and reduced communication throughput. To address this challenge, integrated sensing and communications (ISAC) techniques have been employed at the base station (gNB) within vehicle-to-infrastructure (V2I) networks, aiming to minimize overhead and improve spectral efficiency. In this study, we propose novel frame structures that incorporate ISAC signals for three crucial stages in the NR-V2X system: initial access, connected mode, and beam failure and recovery. These new frame structures employ 75% fewer pilots and reduce reference signals by 43.24%, capitalizing on the sensing capability of ISAC signals. Through extensive link-level simulations, we demonstrate that our proposed approach enables faster beam establishment during initial access, higher throughput and more precise beam tracking in connected mode with reduced overhead, and expedited detection and recovery from beam failures. Furthermore, the numerical results obtained from our simulations showcase enhanced spectrum efficiency, improved communication performance and minimal overhead, validating the effectiveness of the proposed ISAC-based techniques in NR V2I networks.

This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated multi-point (CoMP). In the CBF scheme, a BS shares channel state information (CSI) and estimates target parameters using monostatic sensing. In contrast, a BS in the CoMP scheme shares the CSI and data, allowing bistatic sensing through inter-cell reflection. We consider both block-level (BL) and symbol-level (SL) precoding schemes for both the multi-cell scenarios that are robust to channel state estimation errors. The formulated optimization problems to minimize the CRB in estimating the parameters of a target and maximize the minimum communication signal-to-interference-plus-noise-ratio (SINR) while satisfying a given total transmit power budget are non-convex. We tackle the non-convexity using a combination of semidefinite relaxation (SDR) and alternating optimization (AO) techniques. Simulations suggest that neglecting the inter-cell reflection and communication links degrades the performance of an ISAC system. The CoMP scenario employing SL precoding performs the best, whereas the BL precoding applied in the CBF scenario produces relatively high estimation error for a given minimum SINR value.

Integrated sensing and communication (ISAC) is studied in the airborne domain, where Unmanned Aerial Vehicles (UAVs) act as communication base stations and radars simultaneously. The UAV transmits signals to users while leveraging these signals to localize targets. This research focuses on jointly improving communication and sensing (C&S) performances by designing the UAV trajectory and allocating user’s bandwidth. Since UAV’s sustainability is determined by its onboard battery, energy supply is considered as a constraint in the trajectory design. Communication performance is evaluated by total transmitted data, while sensing performance is assessed through Cramér-Rao bound (CRB). A tradeoff objective is formulated with normalization. To achieve a flexible tradeoff between C&S, the trajectory design is formulated as a weighted sum optimization problem. To improve the formulation accuracy of trajectory design, a multi-stage trajectory design (MSTD) is proposed. While the resultant design problem is difficult to solve directly, an iterative algorithm is developed to obtain a local optimal solution of UAV trajectory. Finally, numerical results are presented to show UAV trajectories determined by the tradeoff between C&S and the energy supply. Benefits of ISAC-based UAV scenario are highlighted by comparing the single-functional UAV scenarios.

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink integrated sensing and communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework can increase communication throughput by up to 20% while reducing sensing MMSE by about 1 dB.

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.

In this work, we study integrated sensing and communication (ISAC) networks with the aim of effectively balancing sensing and communication (S&C) performance at the network level. Focusing on monostatic sensing, the tool of stochastic geometry is exploited to capture the S&C performance, which facilitates us to illuminate key cooperative dependencies in the ISAC network and optimize key network-level parameters. Based on the derived tractable expression of area spectral efficiency (ASE), we formulate the optimization problem to maximize the network performance from the view point of two joint S&C metrics. Towards this end, we further jointly optimize the cooperative BS cluster sizes for S&C and the serving/probing numbers of users/targets to achieve a flexible tradeoff between S&C at the network level. It is verified that interference nulling can effectively improve the average data rate and radar information rate. Surprisingly, the optimal communication tradeoff for ASE maximization tends to use all spatial resources for multiplexing and diversity gain, without interference nulling. In contrast, for sensing objectives, resource allocation tends to eliminate interference, especially when there are sufficient antenna resources, because inter-cell interference becomes a more dominant factor affecting sensing performance. This work first reveals the insight into spatial resource allocation for ISAC networks. Furthermore, we prove that the ratio of the optimal number of users and the number of transmit antennas is a constant value when the communication performance is optimal. Simulation results demonstrate that the proposed cooperative ISAC scheme achieves a substantial gain in S&C performance at the network level.

In this paper, we study symbol-level precoding (SLP) design for time-domain peak-to-average power ratio (PAPR) reduction in a multi-user MISO-OFDM transmission through the idea of constructive interference (CI). Specifically, we design the precoded transmit signals that minimize the symbol-level transmit power subject to per-antenna time-domain PAPR constraint and CI condition, using the knowledge of both data information and channel state information (CSI), based on which a non-convex problem is established. This non-convex problem is transformed into a convex one by the vectorization and relaxation method. For the relaxed problem, we employ Lagrangian method and Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form expression on the precoded signals as a function of the Lagrangian multipliers. Subsequently, we study the dual problem and obtain the optimal Lagrangian multipliers via the proposed alternating iterative algorithm. We further consider the practical communication scenario with imperfect CSI, where the original CI constraint is transformed into a probabilistic constraint in order to achieve robustness against statistically CSI errors. Numerical results validate that the proposed low-complexity algorithm achieves an enhanced performance over existing methods in terms of transmit power, PAPR and computation complexity, both in ideal perfect CSI and practical imperfect CSI cases.

In this paper, we investigate the sensing-aided physical layer security (PLS) towards Integrated Sensing and Communication (ISAC) systems. A well-known limitation of PLS is the need to have information about potential eavesdroppers (Eves). The sensing functionality of ISAC offers an enabling role here, by estimating the directions of potential Eves to inform PLS. In our approach, the ISAC base station (BS) firstly emits an omnidirectional waveform to search for potential Eves’ directions by employing the combined Capon and approximate maximum likelihood (CAML) technique. Using the resulting information about potential Eves, we formulate secrecy rate expressions, which is a function of the Eves’ estimation accuracy. We then formulate a weighted optimization problem to simultaneously maximize the secrecy rate with the aid of the artificial noise (AN), and minimize the Cramér-Rao Bound (CRB) of targets’/Eves’ estimation. By taking the possible estimation errors into account, we enforce a beampattern constraint with a wide main beam covering all possible directions of Eves. This implicates that security needs to be enforced in all these directions. By improving estimation accuracy, the sensing and security functionalities provide mutual benefits, resulting in improvement of the mutual performances with every iteration of the optimization, until convergence. Our results avail of these mutual benefits and reveal the usefulness of sensing as an enabler for practical PLS.

In this paper, we study the interference exploitation precoding in the presence of distortion from nonlinear power amplifiers (PAs) in multi-user multiple-input single-output (MU-MISO) downlink communication systems. We consider the memoryless polynomial model of nonlinear PAs, which is incorporated into the symbol-level precoding (SLP) design to allow the PA nonlinearities in the constructive interference (CI) exploitation. The optimization problem that aims to enhance the signal-to-interference-plus-noise ratio (SINR) without investing additional transmit signal power is formulated for both PSK and QAM signaling. Since the original optimization problem is nonconvex, we first introduce auxiliary variables to transform the optimization problem and adopt the alternating optimization framework for the new optimization problem. For non-convex subproblems, additional auxiliary variables are introduced and several approximations are employed to transform the problem into a semidefinite programming (SDP) form, where the semidefinite relaxation (SDR) method is adopted to obtain feasible solutions. In order to reduce the computational cost of the iterative algorithm, we further propose a low-complexity algorithm for the original PA-aware SLP optimization problem. Numerical results verify the superiority of our proposed PA-aware SLP approach in the presence of nonlinear PAs in the MU-MISO downlink in terms of the error-rate performance over the state-of-the-art.

Departing from traditional data security-oriented designs, the aim of anonymity is to conceal the transmitters’ identities during communications to all possible receivers. In this work, joint anonymous transceiver design at the physical (PHY) layer is investigated. We first present sender detection error rate (DER) performance analysis, where closed-form expression of DER is derived for a generic precoding scheme applied at the transmitter side. Based on the tight DER expression, a fully DER-tunable anonymous transceiver design is demonstrated. An alias channel-based combiner is first proposed, which helps the receiver find a Euclidean space that is close to the propagation channel of the received signal for high quality reception, but does not rely on the recognition of the real sender’s channel. Then, two novel anonymous precoders are proposed under a given DER requirement, one being able to provide full multiplexing performance, and the other flexibly adjusting the number of multiplexing streams with further consideration of the receive-reliability. Simulation demonstrates that the proposed joint transceiver design can always guarantee the subscribed DER performance, while well striking the trade-off among the multiplexing, diversity and anonymity performance.

This paper addresses the constructive interference (CI) precoding problem under per-antenna power constraint (PAPC) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. In this setup, we extend the phase rotation metric and symbol scaling metric of CI precoding under sum power constraint (SPC) to the scenario of PAPC. Against SPC, the scenario of PAPC represents a practical constraint acknowledging that each antenna would have its dedicated power amplifier. Nevertheless, the optimization problem of CI-PAPC precoding becomes more challenging than that under SPC. By analyzing the KKT conditions and leveraging the generalized matrix inverse theory, we obtain a closed-form structure of the CI-PAPC precoder as a function of introduced variables, which facilitates a low-complexity solver. Since the power constraint of each antenna is not always active under PAPC, existing iterative schemes under CI-SPC are no longer applicable. Therefore, the primal-dual interior point method (IPM) is employed to solve the simplified problem with reduced complexity and fast convergence. Simulation results verify our mathematical derivations and demonstrate that our proposed method can reduce the complexity of solving CI-PAPC problem while preserving the error-rate performance, which promotes the practical implementation of CI precoding in real PAPC scenarios.

We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. We explore and reveal the separability of the SLP model. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.

Beam prediction and tracking (BPT) are key technology for millimeter wave communications. Typical techniques include Kalman filtering (KF) and Gaussian process (GP) regression. However, KF requires explicit system dynamics, which is difficult to obtain for complicated scenarios. In contrast, thanks to the data-driven manner, GP regression circumvents this challenging, which, however, suffers from prohibitive computational complexity. To tackle this issue, we propose a novel hybrid model and data driven approach, referred to as data-induced intelligent Kalman filtering (DIIKF). DIIKF learns the system dynamics via the data-driven manner, which can enjoy the advantages of both KF and GP while overcoming their drawbacks. In view that the system dynamics is available, we further propose long-term prediction and design an efficient algorithm. Simulation results show that our method approaches the optimal oracle solution (in terms of effective achievable rate), with the linear complexity order.

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) based integrated sensing and communication (ISAC) framework has been envisioned as a key technology for future wireless networks. Most DFRC beamforming works focus on block-level precoding, which fails to exploit constructive interference. To tackle this issue, we propose symbol-level joint radar sensing and communication beamforming algorithms in this paper. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference into SLP, so as to improve the energy efficiency. To address the formulated problem, we tailor a highly parallelizable iterative algorithm, which is shown to converge to stationary points. To achieve better performance, we further propose an efficient recursive optimization algorithm. In particular, the recursive algorithm monotonously improves the performance of interest as the recursive procedure proceeds.

In this paper, we consider the energy-efficient waveform design for integrated sensing and communications systems, simultaneously performing multi-user communications and point-like/extended target sensing. We propose a performance metric to measure sensing-centric energy efficiency (EE) for the first time, namely sensing-centric EE. We formulate a problem to optimize sensing-centric EE with power budget, signal-to-interference-and-noise ratio (SINR) constraints for communication and a Cramér-Rao bound (CRB) constraint for sensing. For the point-like target case, we give the first-order approximations for the non-convex formulations and develop an effective iterative algorithm to handle the nonconvexity. For the extended target case, we show that the considered problem can be relaxed into semidefinite programming and the optimum can be reconstructed. Simulation results demonstrate significant performance gains on sensing-centric EE over the benchmarks.

We investigate constructive interference (CI)-based symbol-level precoding (SLP) in large-scale systems with massive connectivity of users to minimize the transmit power subject to the instantaneous signal-to-interference-plus-noise-ratio (SINR) and CI constraints. By converting the considered problem into a novel separable formulation, we reveal the existence of separability in SLP, which is therefore well-suited for decomposition. The proximal Jacobian alternating direction method of multipliers (PJ-ADMM) framework is adopted to decompose the reformulated problem into multiple subproblems, which can be solved in parallel with closed-form solutions. We further linearize the second-order terms by approximation, which leads to a parallelizable first-order fast solution to SLP. Our derivations are validated by simulation results, which also show that our algorithm can provide optimal performance with substantially lower computational complexity than state-of-the-art algorithms.

This paper reveals the latent relation inherent in two typical problems in constructive interference (CI)-based symbol-level precoding (SLP). One is the power minimization (PM) problem subject to instantaneous signal-to-interference-plus-noise ratio (SINR) constraints, and the other is the weighted max-min SINR balancing (SB) problem with the symbol-level transmit power budget. In particular, we establish an explicit duality between the PM-SLP and SB-SLP problems, where we prove that one of the two problems can be uniquely mapped to the other. The proposed duality not only provides insights into the intrinsic structure of the problems and solutions but also facilitates obtaining the solution to the SB-SLP given the solution to the PM-SLP without the need for one-dimension search, and vice versa. We further propose a closed-form power scaling algorithm to solve the SB-SLP via PM-SLP, by which the separability of the PM-SLP can be leveraged to solve the two problems simultaneously. Numerical results demonstrate our derivations on the duality as well as the efficiency of the proposed power scaling algorithm.

In this paper, we provide performance analysis for an integrated radar-communication (RadCom) system based on the relative information (RE), also called the Kullback-Leibler divergence (KLD) theorem. The considered system model consists of a multiple-input-multiple-output (MIMO) base-station (BS) which aims at providing RadCom services to multiple communication user equipments (UEs) and detecting a target. The separated deployment, in which the base-station antennas are distributed among radar and communication subsystems, is considered with Zero forcing (ZF) and maximum ratio transmission (MRT) precoders are applied to precode the communication signal. Results show that the derived formulas in this paper are accurate and imply that MRT suffers from bad performance compared to ZF.

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.

Symbol-level precoding (SLP) can fully exploit the multi-user interference in the downlink. This paper investigates fast SLP algorithms for phase-shift keying (PSK) and quadrature amplitude modulation (QAM). In particular, we prove that the weighted max-min signal-to-interference-plus-noise ratio (SINR) balancing (SB) SLP problem with PSK signaling is not separable, which is contrary to the power minimization (PM) SLP problem, and accordingly, existing decomposition methods are not applicable. To tackle this issue, we establish an explicit duality between the SB-SLP and PM-SLP problems with PSK modulation. The proposed duality enables simultaneously obtaining the solutions to the SB-SLP and PM-SLP problems. We concurrently propose a closed-form power scaling algorithm to address the SB-SLP problem by the solution to the PM-SLP problem, via which the separability can be leveraged to decompose the problem. In terms of QAM signaling, a succinct model is used to formulate the PM-SLP problem and convert it into a separable equivalent. The new problem is decomposed into several simple parallel subproblems with closed-form solutions, employing the proximal Jacobian alternating direction method of multipliers (PJ-ADMM). We further prove that the proposed duality can be generalized to the multi-level modulation case, based on which a power scaling parallel inverse-free algorithm is proposed to solve the SB-SLP problem with QAM signaling. Numerical results show that the proposed algorithms offer optimal performance with lower complexity than the state-of-the-art.

Beam training and tracking (BTT) are key technologies for millimeter wave communications. However, since the effectiveness of BTT methods heavily depends on wireless environments, complexity and randomness of practical environments severely limit the application scope of many BTT algorithms and even invalidate them. To tackle this issue, from the perspective of stochastic process (SP), in this paper we propose to model beam directions as a SP and address the problem of BTT via process inference. The benefit of the SP design methodology is that environment priors and uncertainties can be naturally taken into account (e.g., to encode them into SP distribution) to improve prediction efficiencies (e.g., accuracy and robustness). We take the Gaussian process (GP) as an example to elaborate on the design methodology and propose novel learning methods to optimize the prediction models. In particular, beam training subset is optimized based on derived posterior distribution. The GP-based SP methodology enjoys two advantages. First, good performance can be achieved even for small data, which is very appealing in dynamic communication scenarios. Second, in contrast to most BTT algorithms that only predict a single beam, our algorithms output an optimizable beam subset, which enables a flexible tradeoff between training overhead and desired performance. Simulation results show the superiority of our approach.

The need for integrated sensing and communication (ISAC) services has significantly increased in the last few years. This integration imposes serious challenges such as joint system design, resource allocation, optimization, and analysis. Since sensing and telecommunication systems have different approaches for performance evaluation, introducing a unified performance measure which provides a perception about the quality of sensing and telecommunication is very beneficial. To this end, this paper provides performance analysis for ISAC systems based on the information theoretical framework of the Kullback-Leibler divergence (KLD). The considered system model consists of a multiple-input-multiple-output (MIMO) base-station (BS) providing ISAC services to multiple communication user equipments (CUEs) and targets (or sensing-served users). The KLD framework allows for a unified evaluation of the error rate performance of CUEs, and the detection performance of the targets. The relation between the detection capability for the targets and error rate of CUEs on one hand, and the proposed KLD on the other hand is illustrated analytically. Theoretical results corroborated by simulations show that the derived KLD is very accurate and can perfectly characterize both subsystems, namely the communication and radar subsystems.

We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

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.

In this paper, we propose sensing-assisted beamforming designs for vehicles on arbitrarily shaped roads by relying on integrated sensing and communication (ISAC) signalling. Specifically, we aim to address the limitations of conventional ISAC beam-tracking schemes that do not apply to complex road geometries. To improve the tracking accuracy and communication quality of service (QoS) in vehicle to infrastructure (V2I) networks, it is essential to model the complicated roadway geometry. To that end, we impose the curvilinear coordinate system (CCS) in an interacting multiple model extended Kalman filter (IMM-EKF) framework. By doing so, both the position and the motion of the vehicle on a complicated road can be explicitly modeled and precisely tracked attributing to the benefits from the CCS. Furthermore, an optimization problem is formulated to maximize the array gain by dynamically adjusting the array size and thereby controlling the beamwidth, which takes the performance loss caused by beam misalignment into account. Numerical simulations demonstrate that the roadway geometry-aware ISAC beamforming approach outperforms the communication-only-based and ISAC kinematic-only-based technique in tracking performance. Moreover, the effectiveness of the dynamic beamwidth design is also verified by our numerical results.

Rate Splitting Multiple Access (RSMA) relies on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receiver. In RS the users’ messages are split into a common message and private messages, where the common part is first decoded by the all users, while the private part is decoded only by the intended user using SIC technique. This split of the users ’ signals into common and private parts raises some interesting tradeoffs between maximizing sum rate versus secrecy rate. In this work we consider the secrecy performance of RSMA in multi-user multiple-input single-output (MU-MISO) systems, where secrecy is defined by the ability of any user to decode the signal intended for user$k$in the system. To that end, new analytical expressions for the ergodic sum-rate and ergodic secrecy rate are derived for two closed-form precoding techniques of the private messages, namely, 1) zero-forcing (ZF) precoding approach, 2) minimum mean square error (MMSE) approach. Then, based on the analytical expressions of the ergodic rates, novel power allocation strategies that maximize the sum-rate subject to a target secrecy rate for the two precoding schemes are presented and investigated. Our Monte Carlo simulations show a close match with our theoretical derivations. They also reveal that, by tuning the split of the messages, our power allocation approaches provide a scalable tradeoff between rate benefits and secrecy.

A recently proposed unified precoding and pilot design optimization (UPPiDO) framework offers a reduction in both training and feedback overhead of acquiring channel state information (CSI) and an enhancement in robustness (to CSI uncertainties) at the expense of a more computationally demanding precoding optimization. To address this increased complexity, in this paper we first propose an unfolding-friendly iterative algorithm, which can efficiently address a family of non-convex and non-smooth problems. Then, we develop an efficient approach to unfold the iterative algorithm designed. Besides being applicable to important and typical iterative optimization algorithms, a pivotal advantage of the proposed unfolding approach is that the trainable parameters are scalars (rather than matrices). This, in turn, reduces the number of training samples required and makes it suitable for rapidly fluctuating wireless environments. We apply the algorithm unfolding (AU) techniques developed to our UPPiDO-based symbol-level precoding and block-level precoding. Our complexity analysis indicates that the computational complexity is scalable both with the numbers of served users and antennas. Our simulation results demonstrate that the number of outer iterations (or layers) required is about 1/3 of that of the original iterative algorithms.

This paper develops an efficient index modulation (IM) approach for the joint radar-communication (JRC) system based on a multi-carrier multiple-input multiple-output (MIMO) radar. The communication information is embedded into the transmitted radar pulses by selecting the corresponding indices of the carrier frequencies and antenna allocations, providing two degrees of freedom. Our contribution involves the development of a novel codebook based minimum Euclidean distance (MED) maximization and a constellation randomization pre-scaling (CRPS) scheme for efficient IM-JRC transmission. It can be inferred that the IM approach integrating the CRPS scheme followed by the codebook design maximizes the signal-to-noise ratio gain. The reuse of hardware and spectral resources in JRC system, and efficient index modulation for JRC leads to greener approach than existing method. The numerical results support the effectiveness of the proposed approach and show enhanced bit error rate performance when compared to existing baseline.

Different from traditional security-oriented designs, the aim of anonymizing techniques is to mask users' identities during communication, thereby providing users with unidentifiability and unlinkability. The existing anonymizing techniques are only designated at upper layers of networks, ignoring the risk of anonymity leakage at physical layer (PHY). In this paper, we address the PHY anonymity design with focus on a typical uplink scenario where the receiver is equipped with more antennas than the sender. With the increased degrees-of-freedom at the receiver side, we first propose a maximum likelihood estimation (MLE) signal trace-back detector, which only analyzes the signaling pattern of the received signal to disclose the sender's identity. Accordingly, an anonymity entropy anonymous (AEA) precoder is proposed, which manipulates the transmitted signalling pattern to counteract the receiver's trace-back detector and meanwhile to guarantee high receive signal-to-interference-plus-noise ratio for communication. More importantly, more data streams can be multiplexed than the number of transmit antennas, which is particularly suitable for the strong receiver configuration. Simulation demonstrates that the proposed AEA precoder can simultaneously provide high anonymity and communication performance.

Optimal pilot design to acquire channel state information (CSI) is of critical importance for FDD downlink massive MIMO systems, and is still an open problem. To tackle this issue, in this paper we propose a two-stage precoding approach based on reduced CSI (rCSI-TSP) design framework and an efficient algorithm, whose core is to obtain an optimal precoder while also sparsifying physical CSI (pCSI), so as to save on CSI estimation. The advantages of the rCSI-TSP framework are three-fold. First, the framework enables to simultaneously extract and exploit statistical and instantaneous CSI. Second, it guarantees the most needed rCSI can be obtained and thus avoids performance loss due to heuristic pilot design. Third, we tailor an efficient online deep-learning based method for the TSP framework, which paves the way for practical applications. As an example, we apply the framework to the multi-user symbol-level precoding (SLP) and verify performance improvements.

In this paper, we explore a multiple-input multiple- output (MIMO) system with orthogonal frequency division multiplexing (OFDM) transmissions and study the low peak- to-average power ratio (PAPR) MIMO-OFDM waveform design for integrated sensing and communications (ISAC). This is done by leveraging a weighted objective function on both communication and radar performance metrics under power and PAPR constraints. The formulated optimization problem can be equivalently transformed into several sub-problems which can be parallelly solved by the semi-definite relaxation (SDR) method and the optimal rank-1 solution can be obtained in general. The feasibility, effectiveness, and flexibility of the proposed low-PAPR MIMO-OFDM waveform design method are demonstrated by a range of simulations on communication sum rate, symbol error rate as well as radar beampattern and detection probability.

This paper considers a multiple-input multiple-output (MIMO) joint radar-communication (JRC) transmission with hybrid precoding and low resolution digital to analog converters (DACs). An energy efficient radio frequency (RF) chain and DAC bit selection approach is presented for a sub-arrayed hybrid MIMO JRC system. We introduce a weighting formulation to represent the combined radar-communications information rate. The presented selection mechanism is incorporated with fractional programming to solve an energy efficiency maximization problem for JRC which selects the optimal number of RF chains and DAC bit resolution. Subsequently, a weighted minimization problem to compute the precoding matrices is formulated, which is solved using an alternating minimization approach. The numerical results show the effectiveness of the proposed method in terms of high energy efficiency whilst maintaining good rate and desirable radar beampattern performance.

This paper presents the design of two-stage beam alignment methods employing a hybrid analog-digital antenna array and exploiting the beam pattern in a point-to-point millimeter-wave (mmWave) radio for mmWave massive multiple-input multiple-output systems. We investigate an antenna deactivating approach that generates wider beams at the coarse alignment stage and exploit the theoretical beam pattern at the fine alignment stage. Our numerical results show that the proposed two-stage methods can achieve a better beam alignment than existing exhaustive methods and avail measurements/complexity reductions by tuning key parameters governing the alignment performance.

We consider an unmanned aerial vehicle (UAV) based joint radar localization and communication system, where a UAV transmits the downlink signal to a ground communication user and the transmitted signal is also exploited to localize a target coordinates. We aim to optimize the UAV path with energy constraints. We formulate the trajectory design into a weighted optimization problem, where a scalable performance trade-off between localization and communication can be achieved. An iterative algorithm is exploited then to address the trajectory design formulation. Numerical results are provided to validate the effectiveness of the proposed UAV trajectory design approaches.

This work studies resource allocation policies for a multi-antenna access point that is powered by a combination of harvested energy and the power grid, communicating with multiple single-antenna users. We propose a non-convex problem to directly solve the throughput maximization problem. Though the problem is challenging to solve, we first propose an iterative algorithm based on the first-order Taylor expansion and block coordinate descent for the scenario that full channel state information (CSI) and energy arrival information (EAI) are assumed to be known. Then, inspired by this scenario, we study a case in which statistical CSI and EAI are only required. Simulation results demonstrate that the energy-performance trade-off as well as the performance of the statistical case is comparable to the full CSI and EAI scenario, which supports the practical aspect of the proposed policies.

Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power. This paper proposes an unsupervised-learning based SLP that applies to quantized deep neural networks (DNNs). Rather than simply training a DNN in a supervised mode, our proposal unfolds a power minimization SLP formulation in an imperfect channel scenario using the interior point method (IPM) proximal ‘log’ barrier function. We use binary and ternary quantizations to compress the DNN’s weight values. The results show significant memory savings for our proposals compared to the existing full-precision SLP-DNet with significant model compression of ~ 21× and ~ 13× for both binary DNN-based SLP (RSLP-BDNet) and ternary DNN-based SLP (RSLP-TDNets), respectively.

As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Communications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks.

Driving a gradual integration of the physical and digital worlds is perceived to become a reality in the 6G era, from vehicles to drones, from surveillance facilities in cities to agricultural tools in the countryside. Jointly motivated by recent advances in communication and signal processing, radio sensing functionality can be integrated into a 6G radio access network (RAN) in a low-cost and fast manner. That is, future networks have the ability to “see” the physical world through imaging and measuring the surrounding environment, which enables advanced location-aware services, ranging from the physical to application layers. In essence, a radio emission could simultaneously convey communication data from the transmitter to the receiver and deliver environmental information from the scattered echoes. Therefore, sensing and communication (S&C) functionalities are possible to be co-designed to utilize resources efficiently and to assist each other for mutual benefits. This type of research is typically referred to as integrated sensing and communication (ISAC).

This is Part II of the double-part Special Issue (SI) on Integrated Sensing and Communication (ISAC). This SI aims at bringing together contributions from both academia and industry to highlight the recent progress of ISAC, where sensing and communication (S$\$ $C) functionalities are jointly designed to utilize wireless/hardware resources efficiently and to assist each other for mutual benefits. The 32 accepted articles of this SI are arranged into six groups, namely, 1) Fundamental Performance Bounds and Optimization, 2) Time-Frequency Signal Processing, 3) Spatial Signal Processing, 4) Networking and Resource Allocation, 5) ISAC With Emerging Communications Technologies, and 6) ISAC Applications. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team, which provides both a bird’s eye view and technical details regarding state-of-the-art ISAC innovations. The contributions made by the papers in Part II are summarized as follows, which correspond to paper groups 4), 5), and 6).

In the era of e-Health, privacy protection has become imperative in applications that carry personal and sensitive data. Departing from the data-perturbation based privacy-preserving techniques that reduce the fidelity of the disclosed data, in this paper we investigate anonymous communications, which mask the identity of the data sender while providing high data reliability. Focusing on the physical (PHY) layer, we first explore the break of privacy through a statistical attribute based sender detection (SD) from the receiver. Compared to the existing literature, this enables a much enhanced SD performance, especially when the users are equipped with different numbers of antennas. To counteract the advanced SD approach above, we formulate explicit anonymity constraints for the design of the anonymous precoder, which mask the sender’s PHY attributes that can be exploited by SD, while at the same time preserving the reliability of the data. Then, anonymity entropy-oriented precoders are proposed for different antenna configurations at the users, which adaptively construct a maximum number of aliases while obeying users’ signal-to-noise-ratio requirements for data accuracy. Simulation results demonstrate that the proposed anonymous precoders provide the highest level of anonymity entropy over the benchmarks, while achieving reasonable symbol error rate for the communication signal.

This paper studies resource allocation policies for energy harvesting (EH) multi-user multiple input single output (MU- MISO) communication systems. The multi-antenna EH base station (BS) is equipped with a limited-capacity battery. Though employing the multi-antenna at the BS provides the channel diversity, this leads to the co-channel interference which makes the resource allocation problem hard to solve. For this challenging scenario, we first consider off-line policies based on full channel state information (CSI) and energy arrival information (EAI) to obtain the best performance for any feasible resource allocation policies. We propose an iterative algorithm using generalized linear fractional programming to obtain an optimal policy. To achieve a low-complexity sub-optimal policy, we propose another iterative algorithm using the successive convex approximation. Based on the off-line policies, we develop on-line policies in which only statistical CSI and EAI are available. The complexity of the proposed policies is derived. Finally, simulation results evaluate the performance of the proposed approaches and show that the proposed polices outperform the benchmark.

This work proposes a framework to design a cost-efficient unmanned aerial vehicle (UAV)-based energy-neutral (EN) system deployed to harvest data from a set of internet-of-things (IoT) nodes. The energy-neutrality refers to the zero-sum balance between energy harvested, stored, and consumed during operation, which is a game-changer when a connection to the electricity grid is not available/feasible. This involves employing an off-grid charging station (CS) comprising of photovoltaic (PV) panels and batteries that provide enough energy to recharge the UAV-based aerial access points (AAPs). The investment cost is determined by the number of AAPs, PV panels, and ground battery units. Its minimization cannot be achieved using conventional optimization tools due to the non-tractable form of the CS load. Therefore, a novel wave-based method is proposed to represent the load profile as a proportional function of the required number of AAPs, so as to directly relate the CS design to the trajectory optimization. Compared to baseline scenarios, the proposed trajectory design can halve the time and energy consumption; the investment cost varies with the time and season of service; the off-grid CS is particularly advantageous in rural areas, while in urban areas its cost is comparable to that of a grid-connected alternative.

Symbol detection (SD) plays an important role in a digital communication system. However, most SD algorithms require channel state information (CSI), which is often difficult to estimate accurately. As a consequence, it is challenging for these SD algorithms to approach the performance of the maximum likelihood detection (MLD) algorithm. To address this issue, we employ both semi-supervised learning and ensemble learning to design a flexible parallelizable approach in this paper. First, we prove theoretically that the proposed algorithms can arbitrarily approach the performance of the MLD algorithm with perfect CSI. Second, to enable parallel implementation and also enhance design flexibility, we further propose a parallelizable approach for multi-output systems. Finally, comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the designed algorithms. In particular, the proposed algorithms approach the performance of the MLD algorithm with perfect CSI, and outperform it when the CSI is imperfect. Interestingly, a detector constructed with received signals from only two receiving antennas (less than the size of the whole receiving antenna array) can also provide good detection performance.

We study security solutions for dual-functional radar communication (DFRC) systems, which detect the radar target and communicate with downlink cellular users in millimeter-wave (mmWave) wireless networks simultaneously. Uniquely for such scenarios, the radar target is regarded as a potential eavesdropper which might surveil the information sent from the base station (BS) to communication users (CUs), that is carried by the radar probing signal. Transmit waveform and receive beamforming are jointly designed to maximize the signal-to-interference-plus-noise ratio (SINR) of the radar under the security and power budget constraints. We apply a Directional Modulation (DM) approach to exploit constructive interference (CI), where the known multiuser interference (MUI) can be exploited as a source of useful signal. Moreover, to further deteriorate the eavesdropping signal at the radar target, we utilize destructive interference (DI) by pushing the received symbols at the target towards the destructive region of the signal constellation. Our numerical results verify the effectiveness of the proposed design showing a secure transmission with enhanced performance against benchmark DFRC techniques.

In the era of big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication and routing designs are applied at higher layers of networks, ignoring the fact that physical layer (PHY) also contains privacy-critical information. In this paper, we introduce the concept of PHY anonymity, and reveal that the receiver can unmask the sender’s identity by only analyzing the PHY information, i.e., the signaling patterns and the characteristics of the channel. We investigate two scenarios, where the receiver has more antennas than the sender in the strong receiver case, and vice versa in the strong sender case. For each scenario, we first investigate sender detection strategies at the receiver, and then we develop anonymous precoding to address anonymity while guaranteeing high signal-to-interference-plus-noise-ratio (SINR) for communications. In particular, an interference suppression anonymous precoder is first proposed, assisted by a dedicated transmitter-side phase equalizer for removing phase ambiguity. Afterwards, a constructive interference anonymous precoder is investigated to utilize inter-antenna interference as a beneficial element without loss of the sender’s anonymity. Simulations demonstrate that the anonymous precoders are able to preserve the sender’s anonymity and simultaneously guarantee high SINR, opening a new dimension on PHY anonymous designs.

Large-scale antenna array techniques are key enablers for modern wireless communication systems. Channel state information (CSI) is indispensable for large-scale multi-antenna systems, but is challenging to obtain. To tackle this issue, in this paper we propose a unified precoding and pilot design framework, that allows minimal and precoding-sensitive modified CSI (mCSI) to be collected. This results in a significant reduction in the CSI overheads and complexity compared to classical physical CSI (pCSI) estimation. Based on this unified framework, we further propose an intelligent pilot (IP) approach that senses and selects the mCSI to be collected. The IP approach utilizes a compressive sensing formulation to attach sensing and selection of significant mCSI to precoding optimization. We apply the above techniques to the multi-user frequency division duplexing (FDD) downlink as an example. Our study shows that the advantages of the IP approach are three-fold. First, in contrast to the pCSI, precoding-sensitive information is only captured, which reduces the training and feedback overheads. Second, the precoders are optimized directly based on the mCSI, which avoids recovering the pCSI of high-dimension. Third, since the mCSI of reduced dimension is utilized, the scale of the problem to optimize the precoder is also reduced and thus it is much easier to solve.

The use of large-scale antenna arrays poses great difficulties in obtaining perfect channel state information (CSI) in multi-antenna communication systems, which is essential for precoding optimization. To tackle this issue, in this paper we propose a probabilistic-learning based approach (PLA), aiming at alleviating the requirement of perfect CSI. The rationale is that the existing precoding algorithms that output a single precoder are often overconfident in their abilities and the obtained CSI. To avoid overconfidence, we incorporate the idea of regularization in machine learning (ML) into precoding models, so as to limit representative abilities of the precoding models. Compared to the state-of-the-art robust precoding designs, an important advantage of PLA is that CSI uncertainty models are not required. As a specific application of PLA, we design an efficient robust symbol-level hybrid precoding algorithm for the millimeter wave system and confirm the effectiveness of PLA via simulations.

This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal ‘$log$’ barrier function. The proximal ‘$log$’ barrier function is derived from the strict power minimization formulation subject to signal-to-interference-plus-noise ratio (SINR) constraint. The proposed scheme exploits the known interference via symbol-level pre-coding (SLP) to minimize the transmit power and is named strict Symbol-Level-Precoding deep network (SLP-SDNet). The results show that SLP-SDNet out-performs the conventional block-level-precoding (Conventional BLP) scheme while achieving near-optimal performance faster than the SLP optimization-based approach.

This paper explores tailored power allocation (PA) for dual functional radar-communication (DFRC) in the vehicle-to-infrastructure (V2I) network, where a road side unit (RSU) provides both localization and communication services to multiple vehicles. Going beyond classical communications-optimal water-filling solutions, we formulate a PA optimization problem, which minimizes the summation of the Cramér-Rao bound (CRB) for multiple vehicles, subject to downlink sum-rate constraint. We prove that the problem can be solved in closed-form for given sum-rate requirement regions. Numerical results demonstrate that our approach achieves significantly lower estimation errors while improving the communication rate, as compared to the classical water-filling.

In this paper, we propose an energy-efficient radar beampattern design framework for Millimeter Wave (mmWave) massive multi-input multi-output (mMIMO) systems, equipped with a hybrid analog-digital (HAD) beamforming structure. Aiming to reduce the power consumption and hardware cost of the mMIMO system, we employ a learning approach to synthesize the probing beampattern based on a small number of RF chains and antennas. By leveraging a combination of softmax neural networks, the proposed solution is able to achieve a desirable beampattern with high accuracy while incurring low cost.

In this paper, we investigate a deep learning architecture for lightweight online implementation of a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the optimized performance can be achieved based on user equipment’s (UEs’) location-only information. Assuming that each UE is endowed with a limited energy budget, we aim at maximizing the total completed task-input bits (TCTB) of all UEs within a given time slot, through jointly optimizing the RIS reflecting coefficients, the receive beamforming vectors, and UEs’ energy partition strategies for local computing and computation offloading. Due to the coupled optimization variables, a three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the formulated TCTB maximization problem iteratively with guaranteed convergence. The location-only deep learning architecture is then constructed to emulate the proposed BCD optimization algorithm, through which the pilot channel estimation and feedback can be removed for online implementation with low complexity. The simulation results reveal a close match between the performance of the BCD optimization algorithm and the location-only data-driven architecture, all with superior performance to existing benchmarks.

In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication.

Over the past few years, there has been a growing interest in using unmanned aerial vehicles (UAVs) for high-rate wireless communication systems due to their highly flexible deployment and maneuverability. The aim of this article is to propose a 3-D multi-UAV deployment approach to provide Quality-of-Service (QoS) requirements for different types of user distributions in the presence of co-channel interference by maximizing the minimum achievable system throughput for all of the ground users. The proposed approach is divided into two separate algorithms. In the first algorithm, by using the mean-shift technique and prior knowledge of users' positions provided by the global positioning system (GPS), it has been shown that one can simultaneously find xy coordinates of UAVs, which are associated with the maximum of users' density and schedule users to UAVs. Once the xy-Cartesian coordinates of UAVs are determined, UAVs' altitudes and transmit powers are separately optimized. Since these problems are nonconvex optimizations, the successive convex optimization technique has been applied to approximate their nonconvex constraints. In the second algorithm, the block coordinate descent technique is leveraged to jointly optimize UAVs altitudes and transmit powers by tightening the bounds obtained for approximations. It is then proven that the suggested algorithm is guaranteed to converge. The computational complexity of the proposed placement approach is derived. Numerical experiments are carried out to evaluate the performance of our technique and show its superiority to conventional benchmarks.

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.

By fully exploiting the mobility of unmanned aerial vehicles (UAVs), UAV-based aerial base stations (BSs) can move closer to ground users to achieve better communication conditions. In this paper, we consider a scenario where an aerial BS is dispatched for satisfying the data request of a maximum number of ground users, weighted according to their data demand, before exhausting its on-board energy resources. The resulting trajectory optimization problem is a mixed integer non-linear problem (MINLP) which is challenging solve. Specifically, there are coupling constraints which cannot be solved directly. We exploit a penalty decomposition method to reformulate the optimization formulation into a new form and use block coordinate descent technique to decompose the problem into sub-problems. Then, successive convex approximation technique is applied to tackle non-convex constraints. Finally, we propose a double-loop iterative algorithm for the UAV trajectory design. In addition, to achieve a better coverage performance, the problem of designing the initial trajectory for the UAV trajectory is considered. In the results section, UAV trajectories with the proposed algorithm are shown. Numerical results show the coverage performance with the proposed schemes compared to the benchmarks.

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 paper, directional modulation (DM) is investigated to enhance physical layer security. Practical transmitter designs are exploited under imperfect channel state information (CSI) and hardware constraints, such as finite-resolution phase shifters (PSs) and per-antenna power budget. Tailored for the practical issues in realizing DM, a series of practical scenarios are investigated. Starting from the scenario where eavesdroppers (Eve)s' information is completely unknown, corresponding designs are proposed to optimize legitimate users (LU)s' receiving performance while randomizing the Eves' received signal. When the Eves' CSI is imperfectly known, in the second scenario, the Eves' receiving performance is further deteriorated by imposing destructive interference to the Eves. For each scenario, three algorithms are proposed under hardware constraints and imperfect CSI, i.e., one direct-mapping algorithm suitable for high/moderate number of bits in PSs, one heuristic algorithm with improved receiving performance at the cost of complexity, and one iterative-closed-form algorithm with enhanced practicality of symbol-level based DM. Simulation demonstrates that the algorithms achieve lower symbol error rate (SER) at the LUs while significantly deteriorating the Eves' SER, leading to an improved secrecy throughput over the benchmarks.

Millimeter wave (mmwave) communications, envisaged for the next generation wireless networks, rely on large antenna arrays and very narrow, high-gain beams. This poses significant challenges to beam alignment between transmitter and receiver, which has attracted considerable research attention. Even when alignment is achieved, the link is subject to beam drift (BD). BD, caused by non-ideal features inherent in practical beams and rapidly changing environments, is referred to as the phenomenon that the center of main-lobe of the used beam deviates from the real dominant channel direction, which further deteriorates the system’s performance. To mitigate the BD effect, in this paper we first theoretically analyze the BD effect on the performance of outage probability as well as effective achievable rate, which takes practical factors (e.g., the rate of change of the environment, beam width, transmit power) into account. Then, different from conventional practice, we propose a novel design philosophy where multi-resolution beams with varying beam widths are used for data transmission while narrow beams are employed for beam training. Finally, we design an efficient learning based algorithm which can adaptively choose an appropriate beam width according to the environment. Simulation results demonstrate the effectiveness and superiority of our proposals.

This paper considers the performance analysis of constructive interference (CI) precoding technique in multi-user multiple-input single-output (MU-MISO) systems with a finite constellation phase-shift keying (PSK) input alphabet. Firstly, analytical expressions for the moment generating function (MGF) and the average of the received signal-to-noise-ratio (SNR) are derived. Then, based on the derived MGF expression the average symbol error probability (SEP) for the CI precoder with PSK signaling is calculated. In this regard, new exact and very accurate asymptotic approximation for the average SEP are provided. Building on the new performance analysis, different power allocation schemes are considered to enhance the achieved SEP. In the first scheme, power allocation based on minimizing the sum symbol error probabilities (Min-Sum) is studied, while in the second scheme the power allocation based on minimizing the maximum SEP (Min-Max) is investigated. Furthermore, new analytical expressions of the throughput and power efficiency of the CI precoding in MU-MISO systems are also derived. The numerical results in this work demonstrate that, the CI precoding outperforms the conventional interference suppression precoding techniques with an up to 20 dB gain in the transmit SNR in terms of SEP, and up to 15 dB gain in the transmit SNR in terms of the throughput. In addition, the SEP-based power allocation schemes provide additional up to 13 dB gains in the transmit SNR compared to the conventional equal power allocation scheme.

Interference exploitation has recently been shown to provide significant security benefits in multi-user communication systems. In this technique, the known interference is designed to be constructive to the legitimate users and disruptive to the malicious receivers. Accordingly, this paper analyzes the secrecy performance of constructive interference (CI) precoding technique in multi-user multiple-input single-output (MU-MISO) systems with phase-shift-keying (PSK) signals and in the presence of multiple passive eavesdroppers. The secrecy performance of CI technique is comprehensively investigated in terms of symbol error probability (SEP), secrecy sum-rate, and intercept probability (IP). Firstly, new and exact analytical expressions for the average SEP of the legitimate users and the eavesdroppers are derived. In addition, for simplicity and in order to provide more insights, very accurate approximations of the average SEPs are presented in closed-form. Departing from classical Gaussian rate analysis, we employ finite constellation rate expressions to investigate the secrecy sum-rate. In this regard, closed-form analytical expression of the ergodic secrecy sum-rate is obtained. Then, based on the new secrecy sum-rate expression we propose adaptive modulation (AM) scheme with the aim to enhance the secrecy performance. Finally, we present analytical expressions of the IP with fixed and adaptive modulations. The analytical and simulation results demonstrate that, the interference exploitation technique can provide additional up to 17dB gain in the transmit SNR in terms of SEP, and up to 10dB gain in terms of the secrecy sum-rate and the IP, compared to the conventional interference suppression technique. Furthermore, significant performance improvement up to 66% can be achieved with the proposed AM scheme.

This article studies the physical layer security in a multiple-input-multiple-output (MIMO) dual-functional radar-communication (DFRC) system, which communicates with downlink cellular users and tracks radar targets simultaneously. Here, the radar targets are considered as potential eavesdroppers which might eavesdrop the information from the communication transmitter to legitimate users. To ensure the transmission secrecy, we employ artificial noise (AN) at the transmitter and formulate optimization problems by minimizing the signal-to-noise ratio (SNR) received at radar targets, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirement at legitimate users. We first consider the ideal case where both the target angle and the channel state information (CSI) are precisely known. The scenario is further extended to more general cases with target location uncertainty and CSI errors, where we propose robust optimization approaches to guarantee the worst-case performance. Accordingly, the computational complexity is analyzed for each proposed method. Our numerical results show the feasibility of the algorithms with the existence of instantaneous and statistical CSI error. In addition, the secrecy rate of secure DFRC system grows with the increasing angular interval of location uncertainty.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data rates by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint on the transmit signals, we mathematically prove that, when relaxing the 1-bit constraint, the majority of the obtained transmit signals already satisfy the 1-bit constraint. Based on this important observation, we propose a 1-bit precoding method via a partial branch-and-bound (P-BB) approach, where the BB procedure is only performed for the entries that do not comply with the 1-bit constraint. The proposed P-BB enables the use of the BB framework in large-scale antenna scenarios, which was not applicable due to its prohibitive complexity. Numerical results demonstrate a near-optimal error rate performance for the proposed 1-bit precoding algorithm.

In this paper, we introduce a framework for a systematic acceleration of deep neural network (DNN) design for MIMO detection. A monotonically non-increasing function is used to scale the values of the layer weights such that only a certain fraction of the inputs is used for feedforward computation. This enables a dynamic weight scaling across and within the network layers, and it is termed as weight-scaling neural network-based MIMO detector (WeSNet). To increase the robustness against the changes in the activation patterns and additional enhancement in the detection accuracy for the same inference complexity, we introduce trainable weight-scaling functions. Experimental results show the superiority of our proposed method over the benchmark model (DetNet) and classical approaches based on semi-definite relaxation in terms of detection accuracy and computational efficiency.

Unmanned Aerial Vehicle (UAV) networks have recently gained interest, owing to the mobility of UAVs that can be exploited to improve channel conditions and user coverage. In this paper, we consider a scenario where a rotary-wing UAV is dispatched for covering a maximum number of ground users by jointly optimizing the UAV trajectory and bandwidth allocation, under constraints of pre-determined maximal total flight time and on-board energy. The problem is difficult to solve since has nonconvex constraints and includes infinite variables over time. As such, we propose an iterative algorithm with guaranteed convergence by applying block coordinate descent and successive convex approximation techniques. We further exploit the path discretization to formulate the original problem into an optimization formulation with finite variables. We deploy a UAV circular trajectory as the benchmark. The numerical results show that the proposed algorithm significantly outperforms the benchmark scheme and the bandwidth allocation can improve UAV coverage compared with the UAV trajectory only with time partitioning.

Interference exploitation has recently been shown to provide significant security benefits in multiuser communication systems. In this technique, the known interference is designed to be constructive to the legitimate users and disruptive to the malicious receivers. Accordingly, this paper analyzes the secrecy performance of constructive interference (CI) precoding technique in multi-user multiple-input single-output (MU-MISO) systems with phase-shift-keying (PSK) signals and in the presence of multiple passive eavesdroppers. The secrecy performance of CI technique is comprehensively investigated in terms of symbol error probability (SEP), and secrecy sum-rate. Firstly, new and exact analytical expressions for the average SEP of the legitimate users and the eavesdroppers are derived. Departing from classical Gaussian rate analysis, we employ finite constellation rate expressions to investigate the secrecy sum-rate. In this regard, closed-form analytical expression of the ergodic secrecy sum-rate is obtained. Then, based on the new secrecy sum-rate expression we revisit adaptive modulation (AM) scheme with the aim to enhance the secrecy performance. The numerical results in this work demonstrate that, the interference exploitation technique achieves a significant performance gain over the interference suppression schemes. Furthermore, the proposed AM scheme provides significant improvement in terms of the secrecy sum-rate.

In this paper, we investigate power-efficient constructive interference (CI) exploitation in multi-cell coordination systems. By only sharing channel state information (CSI) among the coordinated base stations (BS)s, we propose a CI-based coordinated beamforming (CBF) scheme to judiciously exploit multiuser interference as a beneficial element rather than strictly mitigating it, while simultaneously suppressing inter-cell interference as a destructive element. Then taking imperfect channel state information (CSI) into consideration, we minimize the total transmission power consumption with multiple users' probabilistic signal-to-interference-and-noise ratio (SINR) requirements, where the users' SINR requirements are guaranteed in a statistical manner. Finally, under the presence of CSI error, simulation results demonstrate that the proposed CI-based CBF scheme consumes much lower transmission power compared to the classical CBF benchmarks, where both intra-cell multi-user and inter-cell interference need to be strictly cancelled as destructive elements. Last but not least, the incurred overhead and computational complexity of the proposed scheme are analytically analyzed, confirming its practicality as a new dimension on multi-cell coordination.

In this paper, we focus on the constructive interference (CI) precoding for the scenario when the number of streams simultaneously transmitted by the base station (BS) is larger than that of transmit antennas at the BS, and derive the optimal precoding structure by employing the pseudo inverse. We show that the optimal pre-scaling vector in IE precoding is equal to a linear combination of the right singular vectors that correspond to zero singular values of the coefficient matrix. By formulating the dual problem, we further show that the optimal precoding matrix can be expressed as a function of the dual variables in a closed form, and an equivalent quadratic programming (QP) formulation is derived for computational complexity reduction. Numerical results validate our analysis and demonstrate significant performance improvements for interference exploitation precoding in the considered scenario.

In this paper, we analyze the performance of constructive interference (CI) precoding in downlink multi-user multiple-input single-output (MU-MISO) systems with phase-shift-keying (PSK) signals. A new closed-form expression is derived for the moment generating function (MGF) of the received signal-to-noise-ratio (SNR). Then, the MGF is used to calculate the average symbol error probability (SEP) for the CI technique. In light of this, new exact analytical expression and very accurate asymptotic expression for the average SEP are presented. Based on the new SEP expressions, a power allocation scheme to minimize the sum SEPs (Min-Sum) is investigated, and analytical expression of the power allocation factors is derived. The numerical results show that, the CI precoding yields superior performance over conventional interference suppression precoding techniques in terms of SEP. Furthermore, the Min-Sum power allocation scheme provides additional up to 10dB gains in the transmit SNR compared to equal power allocation technique.

In this article, we address physical layer security in the distributed antenna (DA) systems, where eavesdroppers (Eves) can intercept the information transmitted for the intended receiver (IR). To realize a device-centric, power-efficient, and physical-layer security-aware system, we aim at minimizing power consumption by jointly designing DA selection and secure precoding. Different from the conventional artificial noise (AN)-aided secure transmission, where AN is treated as an undesired element for the IR, we design AN such that it is constructive to the IR while keeping destructive to the Eves. Importantly, we investigate two practical scenarios, where the IR and Eves' channel state information (CSI) are imperfectly obtained or the Eves' CSI is completely unknown. To handle the CSI uncertainties, we solve the problems in probabilistic and deterministic robust optimizations, respectively, both satisfying the IR' signal-to-interference-plus-noise ratio (SINR) requirement by the use of constructive AN and addressing security against the Eves. The simulation results demonstrate our algorithms consume much less power compared to the centralized antenna (CA) systems with/without antenna selection, as well as the DA systems with conventional AN processing. Last but not least, by the proposed algorithms, the activation of DAs closely relates to devices' locations and quality-of-service (QoS) requirements, featuring a device-centric and on-demand structure.

To enable user diversity and multiplexing gains, a fully digital precoding multiple-input-multiple-output (MIMO) architecture is typically applied. However, a large number of radio frequency (RF) chains make the system unrealistic to low-cost communications. Therefore, a practical three-stage hybrid analog-digital precoding architecture, occupying fewer RF chains, is proposed aiming for a nonorthogonal Internet of Things (IoT) signal in low-cost multiuser MIMO systems. The nonorthogonal waveform can flexibly save spectral resources for massive devices connections or improve data rate without consuming extra spectral resources. The hybrid precoding is divided into three stages, including analog domain, digital domain, and waveform domain. A codebook-based beam selection simplifies the analog-domain beamforming via phase-only tuning. Digital-domain precoding can fine-tune the codebook shaped beam and resolve multiuser interference in terms of both signal amplitude and phase. In the end, the waveform-domain precoding manages the self-created intercarrier interference (ICI) of the nonorthogonal signal. This article designs over-the-air signal transmission experiments for fully digital and hybrid precoding systems on software-defined radio (SDR) devices. Results reveal that waveform precoding accuracy can be enhanced by hybrid precoding. Compared to a transmitter with the same RF chain resources, hybrid precoding significantly outperforms fully digital precoding by up to 15.6 dB error vector magnitude (EVM) gain. A fully digital system with the same number of antennas clearly requires more RF chains and, therefore, is low power, space-efficient, and cost-efficient. Therefore, the proposed three-stage hybrid precoding is a quite suitable solution to nonorthogonal IoT applications.

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

This paper introduces a framework for systematic complexity scaling of deep neural network (DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. This allows for weight scaling across and within the different DNN layers in order to achieve accuracy-vs.-complexity scalability during inference. In order to further improve the performance of our proposal, we introduce a sparsity-inducing regularization constraint in conjunction with trainable weight-scaling functions. In this way, the network learns to balance detection accuracy versus complexity while also increasing robustness to changes in the activation patterns, leading to further improvement in the detection accuracy and BER performance at the same inference complexity. Numerical results show that our approach is 10-fold and 100-fold less complex than classical approaches based on semi-definite relaxation and ML detection, respectively.

In this paper, we study a multi-pair two-way half-duplex decode-and-forward (DF) massive multiple-input multiple-output (MIMO) relaying system, in which multiple single-antenna user pairs can exchange information through a massive MIMO relay. For low-complexity processing, zero-forcing reception/zero-forcing transmission (ZFR/ZFT) is employed at the relay. First, we analytically study the large-scale approximations of the sum spectral efficiency (SE). Furthermore, we focus on three specific power scaling laws to study the trade-off between the transmit powers of each pilot symbol, each user and the relay, and also focus on how the transmit powers scale with the number of relay antennas, M , to maintain a finite SE performance. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE), and illustrate the impact of M and the interplay between the power scaling laws and the EE performance. Finally, we consider the system fairness via maximizing the minimum achievable SE among all user pairs.

In this paper, we focus on 1-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where we exploit the concept of constructive interference (CI). For both PSK and QAM signaling, we firstly formulate the optimization problem that maximizes the CI effect subject to the requirement of the 1-bit transmit signals. We then mathematically prove that, when employing the CI formulation and relaxing the 1-bit constraint, the majority of the transmit signals already satisfy the 1-bit formulation. Building upon this important observation, we propose a 1-bit precoding approach that further improves the performance of the conventional 1-bit CI precoding via a partial branch-and-bound (P-BB) process, where the BB procedure is performed only for the entries that do not comply with the 1-bit requirement. This operation allows a significant complexity reduction compared to the fully-BB (F-BB) process, and enables the BB framework to be applicable to the complex massive MIMO scenarios. We further develop an alternative 1-bit scheme through an `Ordered Partial Sequential Update' (OPSU) process that allows an additional complexity reduction. Numerical results show that both proposed 1-bit precoding methods exhibit a significant signal-to-noise ratio (SNR) gain for the error rate performance, especially for higher-order modulations.

In vehicular networks of the future, sensing and communication functionalities will be intertwined. In this article, we investigate a radar-assisted predictive beamforming design for vehicle-to-infrastructure (V2I) communication by exploiting the dual-functional radar-communication (DFRC) technique. Aiming for realizing joint sensing and communication functionalities at road side units (RSUs), we present a novel extended Kalman filtering (EKF) framework to track and predict kinematic parameters of each vehicle. By exploiting the radar functionality of the RSU we show that the communication beam tracking overheads can be drastically reduced. To improve the sensing accuracy while guaranteeing the downlink communication sum-rate, we further propose a power allocation scheme for multiple vehicles. Numerical results have shown that the proposed DFRC based beam tracking approach significantly outperforms the communication-only feedback based technique in the tracking performance. Furthermore, the designed power allocation method is able to achieve a favorable performance trade-off between sensing and communication.

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users' signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

Dual-functional radar communication (DFRC) system has recently attracted significant academic attentions as an enabling solution for realizing radar-communication spectrum sharing. During the DFRC transmission, however, the critical information could be leaked to the targets, which might be potential eavesdroppers. Therefore, the physical layer security has to be taken into consideration. In this paper, fractional programming (FP) problems are formulated to minimize the signal-to-interference-plus-noise ratio (SINR) at targets under the constraints for the SINR of legitimate users. By doing so, the secrecy rate of communication can be guaranteed. We first assume that communication CSI and the angle of the target are precisely known. After that, problem is extended to the cases with uncertainty in the target's location, which indicates that the target might appear in a certain angular interval. Finally, numerical results have been provided to validate the effectiveness of the proposed method showing that it is viable to guarantee both radar and secrecy communication performances by using the techniques we propose.

In this paper, we propose a beamforming design for dual-functional radar-communication (DFRC) systems at the mil-limeter wave (mmWave) band, where hybrid beamforming and sub-arrayed MIMO radar techniques are jointly exploited. We assume that a base station (BS) is serving a multi-antenna user equipment (UE), which in the meantime actively detects multiple targets. Given the optimal communication beam-former and the desired radar beampattern, we propose to design the analog and digital beamformers under non-convex constant-modulus (CM) and power constraints, such that the weighted summation of the communication and radar beam-forming errors is minimized. The formulated optimization problem can be decomposed into three subproblems, and is solved by the alternating minimization approach. Numerical simulations verify the feasibility of the proposed beamforming design, and show that our approach offers a favorable performance tradeoff between sensing and communication.

In this paper, we investigate the interference exploitation precoding for multi-level modulations in the downlink multi-antenna systems. We mathematically derive the optimal precoding structures based on the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, by formulating the dual problem, the precoding problem for multi-level modulations can be transformed into a pre-scaling operation using quadratic programming (QP) optimization. Compared to the original second-order cone programming (SOCP) formulation, this transformation that finally leads to a QP optimization allows a considerable complexity reduction. Simulation results validate our derivations on the optimal precoding structure, and demonstrate significant performance improvements for interference exploitation precoding over traditional precoding methods for multi-level modulations.

In this paper, to realize a power-efficient, user-centric and physical layer security-addressing system, we investigate total power minimization by jointly designing antenna selection and secure precoding for distributed antenna (DA) systems. Different from the conventional artificial noise (AN)-aided secure transmission, where AN is treated as an undesired element for the intended receiver (IR), we design AN such that it is constructive to the IR while keeping destructive to the eavesdroppers (Eves). Importantly, we investigate a practical scenario, where the IR and Eves' channel state information (CSI) is imperfectly obtained. To handle the CSI uncertainties, we solve the problem in a probabilistic manner, which statistically satisfies the IR' signal-to-interference-and-ratio (SINR) requirement by use of constructive AN and addresses security against the Eves. Simulation demonstrates our algorithm consumes much less power compared to the centralized antenna (CA) systems, as well as the DA systems with conventional AN processing. Last but not least, a user-centric and on-demand structure is presented by the algorithm, thanks to the adaptive DAs activation/deactivation mechanism.

Distributed antenna (DA) is considered as a strong alternative to conventional centralized multiple-input multiple-output (MIMO), to provide a greener and user-centric network structure. However, physical layer (PHY) security becomes more challenging in DA systems because of the proximity to the transmitters. In this paper, we jointly optimize DA activation/deactivation and secure precoding to minimize the total power consumption, subjected to legitimate user's (LU) quality-of-service (QoS) and PHY security constraints against potential eavesdroppers (Eves). A practical scenario is considered, where channel state information (CSI) of all the nodes can only be imperfectly obtained. In the presence of infinite probabilities of CSI uncertainties, a deterministic robust based algorithm is designed to always satisfy LU's QoS requirement and address PHY security constraints against Eves. Moreover, essentially different from existing artificial noise (AN)-aided secure transmission schemes, where AN' leakage effect at LU needs to be suppressed, we utilize AN as a beneficial element at LU end while keeping it destructive at potential Eves. Simulation results verify that, the proposed algorithm incurs much lower power consumption compared to its benchmarks, thanks to the additional degrees of freedom of antenna selection and utilizing constructive AN. Last but not least, by adaptively facilitating DA activation/deactivation, the proposed algorithm addresses a user-centric network structure, which is more flexible over the conventional centralized MIMO systems.

In this paper, we consider a physically constrained multi-pair two-way massive multiple-input multiple-output (MIMO) decode-and-forward (DF) half-duplex relay system, where multiple single-antenna user pairs exchange information through a massive MIMO relay, and we employ zero-forcing reception/zero-forcing transmission (ZFR/ZFT) at the relay. When the number of relay antennas M becomes very large and tends to be infinite, we study the large-scale approximation of the sum spectral efficiency (SE) with the effect of spatial correlation generated by the constrained space. Furthermore, we investigate the energy efficiency (EE) with a practical power consumption model, and demonstrate the impact of the relay antenna number and the size of constrained space on the EE performance.

In this paper, we consider a multiuser single-cell transmission facilitated by a full duplex (FD) base station (BS). We formulate two multi-objective optimization problems (MOOPs) via the weighted Tchebycheff method to jointly minimize the two desirable system design objectives namely the total downlink and uplink transmit power. In the first MOOP, multiuser interference is suppressed while in the second MOOP, multiuser interference is rather exploited. In order to solve the non-convex MOOPs, we propose a two-step iterative algorithm to optimize jointly the receive beamformer, transmit beamformer and uplink transmit power, respectively. Simulation results show the proposed scheme achieves a scalable performance-complexity trade-off that allows performance improvements compared to existing solutions.

In this paper, we consider a multi-user full duplex (FD) mobile edge computing (MEC) system, where a FD base station (BS) integrated with a MEC server, simultaneously transmits information signals to downlink users through the downlink and receive computation tasks for execution by the MEC server from mobile devices in the uplink. We study the trade-off between the offloading energy and latency by minimize the total offloading energy and latency via a weighted multi-objective optimization problem. First, we propose a design strategy based on the traditional interference suppression in the downlink, and then, we proposed a second design based on downlink interference exploitation. We employ Lagrangian methods to solve the two non-convex designs via an iterative algorithm. Simulation results not only show the trade-off between the offloading energy and latency, but also show the substantial gains achieved by the proposed FD schemes compared with the baseline half duplex schemes.

This paper analyzes the performance of multi-user multiple-input multiple-output (MU-MIMO) systems, with a finite phase-shift keying (PSK) input alphabet. The achievable sum rate is investigated for two precoding techniques, namely: 1) zero forcing (ZF) precoding, 2) constructive interference (CI) precoding. In light of this, new analytical expressions for the average sum rate are derived in the two scenarios, and Monte Carlo simulations are provided throughout to confirm the analysis. Furthermore, based on the derived expressions, a power allocation scheme that can ensure fairness among the users is also investigated. The results in this paper demonstrate that, the CI strictly outperforms the ZF scheme, and the performance gap between the considered schemes depends essentially on the system parameters.

Rate-Splitting (RS) approach has been proposed recently to enhance the performance of multi-user multiple-input multiple-output (MU-MIMO) systems. In RS a user message is split into common and private parts, where the common message can be decoded by all users, while the private one can be decoded only by the intended user. In addition, constructive interference (CI) precoding technique has shown to provide significant performance benefits in different multi-user scenarios. In this paper we propose employing the CI concept to further enhance the sum-rate achieved by RS approach in MU-MIMO systems under a phase-shift keying (PSK) input alphabet. In light of this and in order to provide fair comparison, new analytical expressions for the average sum-rate are derived for two precoding techniques of the private messages, namely, 1) CI precoding technique, 2) zero forcing (ZF) precoding technique. In addition, the conventional transmission, without using RS (NoRS) is also studied in this paper. Monte-Carlo simulations are provided throughout to confirm the analysis. The results in this work validate the significant sum-rate gain of RS with CI over the conventional RS with ZF technique.

Narrowband Internet of Things (NB-IoT) was introduced by 3GPP in low power wide area network to support low power and wide coverage applications. Since it follows long term evolution standard, its signal quality is guaranteed and its deployment is straightforward via reusing existing infrastructures. Current NB-IoT supports low data rate services via using low order modulation formats for the purpose of power saving. However, with the increase of data rate driven applications, next generation NB-IoT would require data rate enhancement techniques without consuming extra battery power. In this paper, a downlink framework, using a nonorthogonal signal waveform for next generation enhanced NB-IoT (eNB-IoT), is proposed and experimentally tested in both single-antenna and multiantenna systems. In the single-antenna scenario, waveform precoding is used to pre-equalize the self-created inter carrier interference distorted signal waveform. For the multiantenna multiuser scenario, both waveform and antenna space precoding have to be used. Measured results show that in both single-antenna and multiantenna systems, the proposed signal waveform in eNB-IoT can increase data rate by ~11% compared with NB-IoT occupying the same spectral resource in similar receiver computational complexity.

In this paper, we investigate the offloading energy and latency trade-off in a multiuser full-duplex (FD) system. We consider a multi-user FD system where a FD base station (BS), equipped with a mobile-edge computing (MEC) server, carries out data transmission in the downlink while at the same time receiving computational tasks from mobile devices in the uplink. Our main aim is to study the trade-off between the offloading energy and latency, which are known to be very important and desirable system objectives for both the system operator and users. In practice, there always exists a trade-off between these two objectives. Toward this aim, we formulate two weighted multi-objective optimization problems (MOOPs), one where the multi-user interference (MUI) is suppressed and the other where MUI is rather exploited. As a result, our proposed MOOPs allow for a scalable trade-off between the two objectives. To tackle the non-convexity of the formulations, we design an iterative algorithm through Lagrangian method. We also address the scenario of imperfect channel state information (CSI) at the FD BS. For the imperfect CSI case, we apply convex relaxations and transformation using the S-procedure to tackle the non-convexity of the formulations. Simulation results show the effectiveness of the proposed FD schemes compared with the existing baseline half duplex schemes and the superiority of MUI exploitation over suppression.

This paper studies interference exploitation techniques for secure beamforming design in simultaneous wireless information and power transfer in multiple-input single-output systems. In particular, multiuser interference (MUI) and artificially generated noise (AN) signals are designed as constructive to the information receivers (IRs) yet kept disruptive to potential eavesdropping by the energy receivers. The objective is to improve the received signal-to-interference and noise ratio (SINR) at the IRs by exploiting the MUI and AN power in an attempt to minimize the total transmit power. We first propose second-order cone programming-based solutions for the perfect channel state information (CSI) case by defining strong upper and lower bounds on the energy harvesting (EH) constraints. We then provide semidefinite programming-based solutions for the problems. In addition, we also solve the worst case harvested energy maximization problem under the proposed bounds. Finally, robust beamforming approaches based on the above are derived for the case of imperfect CSI. Our results demonstrate that the proposed constructive interference precoding schemes yield huge saving in transmit power over conventional interference management schemes. Most importantly, they show that, while the statistical constraints of conventional approaches may lead to instantaneous SINR as well as EH outages, the instantaneous constraints of our approaches guarantee both constraints at every symbol period.

In this paper, we analyze the sum rate performance of multi-user multiple-input-multiple-output (MU-MIMO) systems, with a finite constellation phase-shift keying (PSK) input alphabet. We analytically calculate and compare the achievable sum rate in three downlink transmission scenarios: 1) without precoding; 2) with zero forcing (ZF) precoding; and 3) with closed form constructive interference (CI) precoding technique. In light of this, new analytical expressions for the average sum rate are derived in the three cases, and Monte Carlo simulations are provided throughout to validate the analysis. Furthermore, based on the derived expressions, a power allocation scheme that can ensure fairness among the users is also proposed. The results in this work demonstrate that the CI strictly outperforms the other two schemes, and the performance gap between the considered schemes increases with the increase in MIMO size. In addition, the CI provides higher fairness and the power allocation algorithm proposed in this paper can achieve maximum fairness index.

Unmanned aerial vehicle-based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional terrestrial BSs are available and the aim is deploying multiple aerial BSs to cover a maximum number of users within a certain target area. To this end, we first propose a naive successive deployment method, which converts the non-convex constraints in the involved optimization into a combination of linear constraints through geometrical relaxation. Then, we investigate a deployment method based on K-means clustering. The method divides the target area into K convex subareas, where within each subarea, a mixed integer non-linear problem is solved. An iterative power efficient technique is further proposed to improve coverage probability with reduced power. Finally, we propose a robust technique for compensating the loss of coverage probability in the existence of inaccurate user location information. Our simulation results show that the proposed techniques achieve an up to 30% higher coverage probability when users are not distributed uniformly. In addition, the proposed simultaneous deployment techniques, especially the one using iterative algorithm, improve power-efficiency by up to 15% compared with the benchmark circle packing theory.

Hybrid analog-digital precoding significantly reduces the hardware costs in massive multiple-input multiple-output (MIMO) transceivers when compared with fully digital precoding at the expense of increased transmit power. In order to mitigate the above-mentioned shortfall, we use the concept of constructive interference-based precoding, which has been shown to offer significant transmit power savings when compared with the conventional interference suppression-based precoding in fully digital multiuser MIMO systems. Moreover, in order to circumvent the potential quality-of-service degradation at the users due to the hardware impairments in the transmitters, we judiciously incorporate robustness against such vulnerabilities in the precoder design. Since the undertaken constructive interference-based robust hybrid precoding problem is nonconvex with infinite constraints and thus difficult to solve optimally, we decompose the problem into two subtasks, namely, analog precoding and digital precoding. In this paper, we propose an algorithm to compute the optimal constructive interference-based robust digital precoders. Furthermore, we devise a scheme to facilitate the implementation of the proposed algorithm in a low-complexity and distributed manner. We also discuss the block-level analog precoding techniques. The simulation results demonstrate the superiority of the proposed algorithm and its implementation scheme over the state-of-the-art methods.

In this paper, we focus on the coexistence between a MIMO radar and cellular base stations. We study the interfering channel estimation, where the radar is operated in the “search and track” mode, and the BS receives the interference from the radar. Unlike the conventional methods where the radar and the cellular systems fully cooperate with each other, in this paper, we consider that they are uncoordinated and the BS needs to acquire the interfering channel state information (ICSI) by exploiting the radar probing waveforms. For completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are considered in the coexistence scenario. By further assuming that the BS has limited a priori knowledge about the radar waveforms, we propose several hypothesis testing methods to identify the working mode of the radar, and then obtain the ICSI through a variety of channel estimation schemes. Based on the statistical theory, we analyze the theoretical performance of both the hypothesis testing and the channel estimation methods. Finally, the simulation results verify the effectiveness of our theoretical analysis and demonstrate that the BS can effectively estimate the interfering channel even with the limited information from the radar.

Hybrid beamforming with phase shifters and switches has been identified as a low-cost and energy-efficient approach to harness the benefits of massive multiple-input multiple-output (MIMO) systems. In this paper, three subconnected hybrid beamforming structures with different combinations of phase shifters and switches will be considered. Firstly we assume that perfect channel state information (CSI) is available and the wireless channel follows uncorrelated Rayleigh fading model. Then, we derive the closed-form expressions of the low-complexity beamformers and their asymptotic achievable sum-rates. Based on the proposed beamformers, we develop quantized hybrid beamforming and channel estimation techniques for correlated Rayleigh fading channels. These methods rely on designing novel RF codebooks and they can be used in both CSI acquisition and data transmission phases. The proposed methods benefit from low computational complexity, low signaling overhead and robustness to estimation errors. Moreover, they are applicable to both frequency and time division duplex systems.

Aerial base stations (BSs) based on unmanned aerial vehicles (UAVs) can provide rapid wireless services to users in areas without ground infrastructure. This paper aims to deploy multiple aerial BSs to cover a maximum number of ground users within a certain target area while avoiding inter-cell interference (ICI). Two techniques are proposed. The first method deploys multiple aerial BSs in a successive way and converts the non-convex constraints into various linear constraints which can be easily solved. The second method simultaneously deploys multiple aerial BSs by dividing the target area into K convex subareas with the help of K-means clustering. Simulation results show that both techniques achieve a performance gain compared to the benchmark circle packing theory (CPT).

In this paper, we consider the downlink transmission for a dual-hop amplify-and-forward (AF) multiple-antenna relay systems, where we propose beamforming techniques for interference exploitation on a symbol level. Based on the constructive interference (CI), we firstly propose a joint source/relay precoding, where the precoding matrices at the source and the output signals at the relay are jointly optimized. To alleviate the high computational costs and circumvent the difficulty of practical implementation of the joint design, we further propose a low-complexity decoupled approach, where a closed-form linear precoding method is first employed at the source, and we then optimize the beamforming matrix at the relay for interference exploitation. It is revealed by numerical results that the proposed approaches that exploit the instantaneous interference can achieve an improved performance over the conventional case with a linear approach.

This paper investigates a new beamforming approach for interference exploitation, which has recently attracted interest as an alternative to conventional interference-avoidance beamforming for the downlink of multiple-input multiple-output systems. Contrary to existing interference exploitation approaches that focus on signal-to-noise ratio performance, we adopt an approach based on the detection region of the signal constellation. Focusing on quality of service, we then formulate the optimization for minimizing the error probability (EP) for the worst user, subject to power constraints. We do this by employing the knowledge of channel state information at the transmitter, along with all downlink users’ data that are readily available at the base station during downlink transmission. In this context, we also show that the detection-region-based beamforming and the worst user EP downlink beamforming are equivalent problems. Finally, we further propose a sum EPs approach and provide an analytic bound of average symbol error rate performance. Our simulations verify that the proposed techniques provide significantly improved performance over conventional downlink beamforming techniques.

Conventionally, interference and noise are treated as catastrophic elements in wireless communications. However, it has been shown recently that exploiting known interference constructively can contribute to signal detection ability at the receiving end. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). The scenario considered here is a multiple-input single-output wiretap channel with multiple Eves. This paper starts from AN design without any knowledge of Eve's CSI, builds with solutions with statistical CSI up to full CSI. Both perfect and imperfect channel information have been considered, in particular, with different extent of Eves' channel responses. The main objective is to improve the receive signal-to-interference and noise ratio at IR through exploitation of AN power in an attempt to minimize the total transmit power, while hindering detection at the Eves. Numerical simulations demonstrate that the proposed constructive AN precoding approach yields superior performance over conventional AN schemes in terms of transmit power. Critically, they show that, while the statistical constraints of conventional approaches may lead to instantaneous IR outages and security breaches from the Eves, the instantaneous constraints of our approach guarantee both IR performance and secrecy at every symbol period.

This paper considers a multiuser full-duplex (FD) wireless communication system, where an FD radio base station (BS) serves multiple single-antenna half-duplex uplink and downlink users simultaneously. Unlike conventional interference mitigation approaches, we propose using knowledge of the data symbols and the channel state information (CSI) at the FD radio BS to exploit the multi-user interference constructively rather than to suppress it. We propose a multi-objective optimization problem (MOOP) via the weighted Tchebycheff method to study the tradeoff between the two desirable system design objectives, namely the total downlink transmit power and the total uplink transmit power, at the same time ensuring the required quality-of-service (QoS) for all users. In the proposed MOOP, we adapt the QoS constraints for the downlink users to accommodate constructive interference for both generic phase shift keying modulated signals and for quadrature amplitude modulated signals. We also extended our work to a robust design to study the system with imperfect uplink and downlink CSI. The simulation results and analysis show that significant power savings can be obtained. More importantly, however, the MOOP approach here allows for the power saved to be traded off for both uplink and downlink power savings, leading to an overall energy efficiency improvement in the wireless link.

In this paper, we propose closed-form precoding schemes with optimal performance for constructive interference (CI) exploitation in the multiuser multiple-input single-output downlink, where the cases of both strict and non-strict phase rotation are considered. For optimization with strict phase rotation, we mathematically derive the optimal precoding structure with Lagrangian and Karush-Kuhn-Tucker conditions. By formulating its dual problem, the optimization problem is further shown to be equivalent to a quadratic programming over a simplex, which can be solved more efficiently. We then extend our analysis to the case of non-strict phase rotation, where it is mathematically shown that a K -dimensional optimization for non-strict phase rotation is equivalent to a 2K -dimensional optimization for strict phase rotation in terms of the problem formulation. The connection with the conventional zero-forcing precoding is also discussed. Based on the above-mentioned analysis, we further propose an iterative closed-form scheme to obtain the optimal precoding matrix, where within each iteration a closed-form solution can be obtained. Numerical results validate our analysis and the optimality of the proposed iterative closed-form algorithm, and further show that the proposed iterative closed-form scheme offers a flexible performance-complexity tradeoff by limiting the maximum number of iterations, which motivates the use of CI precoding in practical wireless systems.

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.

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts as radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

In this paper, we consider the power minimization problem in a multi-user full-duplex communication system by employing a multi-objective optimization problem via the weighted Tchebycheff method. We propose to exploit the multi-user interference by using the knowledge of the data symbols and channel state information at the full-duplex base station. Simulation results show that significant power savings can be obtained, which leads to substantial reduction of the self- interference power in the full-duplex systems.

In this paper, we propose a novel approach for the spectrum sharing between Multi-Input-Multi-Output (MIMO) radar and downlink multi-user Multi-Input- Single-Output (MU-MISO) communication system. To obtain a power-efficient beamforming at the base station (BS), we utilize the constructive multi- user interference (MUI) as a source of green signal power. The proposed beamforming design mainly focuses on two optimization problems, i.e., transmit power minimization for BS and interference minimization for radar, subject to given performance requirements of the two systems. We further consider the impact of the proposed methods on radar, where the detection probability for MIMO radar in the presence of the interference from BS is analytically derived, and important trade-offs are revealed. Numerical results show that the proposed approach outperforms the conventional beamforming designs by achieving a significant performance gain under the discussed coexistence scenario.

In this work we analyze the benefits of low-complexity radio frequency (RF) switching matrices (SMs) for antenna selection (AS) in large scale antenna systems (LSAS). The reduced RF complexity and insertion losses (ILs) are attained by limiting the number of internal connections in the SM, at the expense of a limited flexibility in the AS. The results presented in this paper demonstrate that partially-connected (PC) SMs outperform conventional fully-flexible (FF) alternatives due to their reduced ILs, which are characterized in this work.

In this paper, we study a constructive interference based cognitive radio beamforming optimization problem under perfect channel state information at the transmitter and the knowledge of data information. The beamformers are designed to minimize the worst secondary user's symbol error probability under constraints on the instantaneous total transmit power, and the power of the instantaneous interference in the primary link. The problem is formulated as a bivariate probabilistic constrained programming problem and can be solved using the barrier method. Our simulations indicate that the proposed technique offers a significantly improved performance over the conventional technique, while guaranteeing the quality of service (QoS) of primary users on an instantaneous basis, in contrast to the average QoS guarantees of conventional beamformers.

In this paper, the mutual coupling effect among antenna elements for the downlink multiuser multiple-input-single-output (MU-MISO) is studied. Different from conventional knowledge that the mutual coupling effect usually degrades the system performance, a joint analog-digital precoding scheme is proposed so that the system can benefit from this effect. Linear precoding approaches are applied in the digital domain, while in the analog domain, convex optimization is applied to determine the value of each load impedance such that the resulting noise amplification factor for the precoder is minimized. Simulation results show that the proposed analog-digital precoding schemes can achieve a significant performance gain over conventional precoding approaches with fixed mutual coupling.

In this paper, we propose a joint analog-digital (A/D) beamforming scheme for the point-to-point (P2P) multiple-input-multiple-output (MIMO) systems, where we exploit the mutual coupling effect to further improve the system performance. By judiciously selecting the value of each load impedance for the antenna array, it will be shown that the mutual coupling effect can be beneficial. We firstly prove that the full elimination of mutual coupling is not achievable solely by changing the values of each load impedance. We further propose a joint A/D technique where the resulting interference aligns constructively to the useful signal vector with the concept of constructive interference. Numerical results show that the proposed schemes can achieve an improved performance compared to systems with fixed mutual coupling, especially when the antenna spacing is small.

In this paper, we focus on the millimeter-wave multi-user multiple-input-multiple-output (mmWave MU-MIMO) systems and propose a low-complexity hybrid precoding and combining design, which is applicable to both fully-connected structures and sub-connected structures. Based on the channel knowledge of each user, the analog combiner for each user is independently designed based on the singular value decomposition (SVD), while the analog precoder is obtained by the conjugate transposition to maximize the effective channel gain. Then, with the resulting effective analog channel, low-dimensional baseband precoders can be efficiently applied. The proposed scheme requires no optimization techniques or any complicated iterative algorithms, while the numerical results show that it can approach the performance of fully digital schemes and even achieve a better performance in some scenarios. It is also observed that sub-connected structures can achieve a much higher power efficiency compared to fully-connected structures and are therefore promising for the future green communication systems.

Recent advances in interference exploitation showed that exploiting knowledge of interference constructively can improve the receive signal-to-interference and noise ratio (SINR) at the destination. This paper exploits this concept to design artificial noise (AN) beamformers constructive to the intended receiver (IR) yet keeping AN disruptive to possible eavesdroppers (Eves). A multiple-input single-output (MISO) wiretap channel with multiple eavesdroppers scenario has been investigated taking both perfect and imperfect channel information into consideration. The main objective is to improve the receive SINR at the IR through exploitation of AN power in an attempt to minimize the total transmit power, while confusing the Eves.

In this letter, we propose a novel manifold-based algorithm to solve the constant envelope (CE) precoding problem with interference exploitation. For a given power budget, we design the precoded symbols subject to the CE constraints, such that the constructive effect of the multiuser interference is maximized. While the objective function for the original problem is not complex differentiable, we consider the smooth approximation of its real representation, and map it onto a Riemannian manifold. By using the Riemmanian conjugate gradient algorithm, a local minimizer can be efficiently found. The complexity of the algorithm is analytically derived in terms of floating-points operations (flops) per iteration. Simulations show that the proposed algorithm outperforms the conventional methods on both symbol error rate and computational complexity.

We propose several low-complexity transmit antenna selection (TAS) and precoding schemes for massive multi-input multi-output (M-MIMO). It is well established that large antenna arrays in M-MIMO lead to particularly high hardware overheads as they require an equally large number of radio-frequency chains, and antenna selection is envisaged as a solution to reducing this hardware complexity. Accordingly, in the proposed schemes, both hardware and computational complexity of M-MIMO systems are addressed by jointly optimizing TAS and precoding. We first introduce a mixed-integer programming approach that simultaneously identifies the transmitting antennas subset and solves the precoding problem, by employing a unified metric based on constructive interference (CI) concept. We then propose three sub-optimal techniques that allow a reduction of the computational complexity required to solve the joint optimization. Our analyses and results prove that the proposed joint TAS and precoding schemes based on CI exploitation are able to outperform the state-of-the-art, while providing a favorable performance-complexity tradeoff.

In this paper, we explore reduced-connectivity radio frequency (RF) switching networks for reducing the analog hardware complexity and switching power losses in antenna selection (AS) systems. In particular, we analyze different hardware architectures for implementing the RF switching matrices required in AS designs with a reduced number of RF chains. We explicitly show that fully-flexible switching matrices, which facilitate the selection of any possible subset of antennas and attain the maximum theoretical sum rates of AS, present numerous drawbacks such as the introduction of significant insertion losses, particularly pronounced in massive multiple-input multiple-output (MIMO) systems. Since these disadvantages make fully-flexible switching suboptimal in the energy efficiency sense, we further consider partially-connected switching networks as an alternative switching architecture with reduced hardware complexity, which we characterize in this work. In this context, we also analyze the impact of reduced switching connectivity on the analog hardware and digital signal processing of AS schemes that rely on received signal power information. Overall, the analytical and simulation results shown in this paper demonstrate that partially-connected switching maximizes the energy efficiency of massive MIMO systems for a reduced number of RF chains, while fully-flexible switching offers sub-optimal energy efficiency benefits due to its significant switching power losses.

Compact parasitic arrays in the form of electronically steerable parasitic antenna radiators (ESPARs) have emerged as a new antenna structure that achieves multiple-input-multiple-output (MIMO) transmission with a single RF chain. In this paper, we study the application of precoding on practical ESPARs, where the antennas are equipped with load impedances of quantized values. We analytically study the impact of the quantization on the system performance, where it is shown that while ideal ESPARs with ideal loads can achieve a similar performance to conventional MIMO, the performance of ESPARs will be degraded when only loads with quantized values are available. We further extend the performance analysis to imperfect channel state information. In order to alleviate the performance loss, we propose to approximate the ideal current vector by optimization, where a closed-form solution is further obtained. This enables the use of ESPARs in practice with quantized loads. Simulation results validate our analysis and show that a significant performance gain can be achieved with the proposed scheme over ESPARs with quantized loads. Finally, the tradeoff between performance and power consumption is shown to be favorable for the proposed ESPAR approaches compared with conventional MIMO, as evidenced by our energy efficiency results.

We introduce a new approach to constant-envelope precoding (CEP) based on an interference-driven optimization region for generic phase-shift-keying modulations in the multi-user (MU) multiple-input-multiple-output downlink. While conventional precoding approaches aim to minimize the multi-user interference (MUI) with a total sum-power constraint at the transmitter, in the proposed scheme we consider MUI as a source of additional energy to increase the signal-to-interference-and-noise-ratio at the receiver. In our studies, we focus on two different CEP approaches: a first technique, where the power at each antenna is fixed to a specific value, and a two-step approach, where we first relax the power constraints to be lower than a defined parameter and then enforce CEP transmission. The algorithms are studied in terms of computational costs, with a detailed comparison between the proposed approach and the classical interference suppression schemes from the literature. Moreover, we analytically derive a robust optimization region to counteract the effects of channel-state estimation errors. The presented schemes are evaluated in terms of achievable symbol error rate in a perfect and imperfect channel-state information scenario for different modulation orders. Our results show that the proposed techniques further extend the benefits of classical CEP by judiciously relaxing the optimization region.

In this paper, we propose a joint analog-digital (A/D) beamforming scheme for the point-to-point multiple-input-multiple-output system, where we exploit mutual coupling by optimizing the load impedances of the transmit antennas. Contrary to the common conception that mutual coupling strictly harms the system performance, we show that mutual coupling can be beneficial by exploiting the concept of constructive interference. By changing the value of each load impedance for the antenna array based on convex optimization, the mutual coupling effect can be manipulated so that the resulting interference aligns constructively to the useful signal vector. We first prove that the full elimination of mutual coupling effect is not achievable solely by tuning the values of the antenna load impedances. We then introduce the proposed A/D scheme for both PSK and QAM modulations, where performance gains with respect to conventional techniques are obtained. The implementation of the proposed schemes is also discussed, where a lookup table can be built to efficiently apply the calculated load impedances. The numerical results show that the proposed schemes can achieve an improved performance compared to systems with fixed mutual coupling, especially when the antenna spacing is small.

Quality of service (QoS) is commonly measured in terms of signal to interference plus noise ratio (SINR), where multiuser interference is mitigated in order to improve the performance. As opposed to conventional suppression, interference can be exploited constructively to enhance the desired signal. With the aid of channel state information (CSI) at the transmitter and data information, we study symbol-level downlink beamforming problems based on noise robustness and outage probability, respectively, subject to power constraints. We further show that an equivalence relationship between the noise robustness and outage probability symbol-level downlink beamforming problems can be obtained. Finally, we provide an analytic symbol error rate (SER) upper bound of the worst user by solving the outage probability-based problem. Our simulations demonstrate that the proposed techniques provide substantial performance improvements over conventional downlink beamforming techniques.

We examine the deployment of a large multi-user multiple-input multiple-output (MIMO) system and the resulting Energy Efficiency (EE) considering a 2D rectangular array with increasing antenna elements within a fixed physical space. The resulting increasing mutual coupling and correlation among the base station (BS) antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements. We also provide a realistic analysis of the energy consumption using a new model, taking into account the circuit power consumption as a function of the number of BS antennas and then present a performance analysis of the system with respect to EE. Our analysis shows that while spectral efficiency increases with increasing number of BS antennas in a massive MIMO system, EE does not increase boundlessly when the increasing number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the BS antennas. Accordingly, analytic expressions for the optimum number of antennas to attain maximum EE are obtained.

Space shift keying (SSK) transmission is a low-complexity complement to spatial modulation (SM) that solely relies on a spatial-constellation diagram for conveying information. The achievable performance of SSK is determined by the channel conditions, which in turn define the minimum Euclidean distance (MED) of the symbols in the received SSK constellation. In this contribution we concentrate on improving the power efficiency of SSK transmission via symbol pre-scaling. Specifically, we pose a pair of related optimization problems for a) enhancing the MED at reception while satisfying a given power constraint at the transmitter, and b) reducing the transmission power required for achieving a given MED. The resultant optimization problems are NP-hard, hence they are subsequently reformulated and solved via semidefinite programming. The results presented demonstrate that the proposed pre-scaling strategies are capable of enhancing the attainable performance of conventional SSK, while simultaneously extending its applicability and reducing the complexity of the existing pre-scaling schemes.

Existing MIMO precoding techniques assume conventional antenna arrays with multiple radio-frequency (RF) chains each connected to a different antenna. Towards small portable devices and base stations, single-fed compact arrays, also known as electronically steerable parasitic antenna radiators (ESPAR) have recently emerged as a new antenna structure that requires only a single RF chain. In this paper, we study the ESPAR based antenna arrays and explore linear precoding schemes for ESPAR antennas. The closed-form expression for the computation of the tunable loads and the feeding voltage is firstly shown and the impact of impedance errors and imperfect CSI on the performance is also investigated analytically. It will be shown that the impedance errors will act as an additional noise source that is independent of the SNR and thus result in an error floor at high SNR. We further study the energy efficiency of both conventional MIMO and ESPAR-based MIMO systems. Simulation results validate our analysis and show that ESPAR without impedance errors can achieve a similar performance to conventional antenna arrays and a higher energy efficiency, while the performance degradation due to impedance errors motivates the design of robust precoding schemes.

We explore a bandwidth efficient transmission scheme that amalgamates multiple-input-multiple-output spatial multiplexing (SMX) with receive antenna based spatial modulation (RSM). The RSM here is applied to the combined spatial and power-level domain, not by activating and de-activating the receive antennas, but rather by choosing between two power levels {P1,P2} for the received symbols in these antennas, such that all receive antennas are active and SMX can still be accommodated. This allows for the coexistence of RSM with SMX and the results show an increased bandwidth efficiency for the proposed scheme compared to both SMX and RSM. We further carry out a mathematical analysis to optimize the ratio between P1and P2for attaining the minimum error rates. Our analytical and simulation results demonstrate significant bandwidth efficiency gains for the proposed scheme compared to conventional SMX and RSM.

In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast body of related literature, which considers fixed inter-element spacing, we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter-antenna distance. For this scenario, we derive exact and approximate expressions, as well as simplified upper/lower bounds, for the SE of maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-squared error receivers (MMSE) receivers. In particular, our analysis shows that the MRC receiver is non-optimal for space-constrained massive MIMO topologies. On the other hand, ZF and MMSE receivers can still deliver an increasing SE as the number of BS antennas grows large. Numerical results corroborate our analysis and show the effect of the number of antennas, the number of users, and the total antenna array space on the sum SE performance.

In this paper, we propose a power-efficient approach for information and energy transfer in multiple-input single-output downlink systems. By means of data-aided precoding, we exploit the constructive part of interference for both information decoding and wireless power transfer. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information signal energy and electrical wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference, based on the constructive regions in the signal constellation. Although the resulting problem is nonconvex, several methods are developed for its solution. First, we derive necessary and sufficient conditions for the feasibility of the considered problem. Then we propose second-order cone programming and semi-definite programming algorithms with polynomial complexity that provide upper and lower bounds to the optimal solution and establish the asymptotic optimality of these algorithms when the modulation order and SINR threshold tend to infinity. A practical iterative algorithm is also proposed based on successive linear approximation of the nonconvex terms yielding excellent results. More complex algorithms are also proposed to provide tight upper and lower bounds for benchmarking purposes. Simulation results show significant power savings with the proposed data-aided precoding approach compared to the conventional precoding scheme.

In this paper, we explore low-complexity transmission in physically-constrained massive multiple-input multiple-output (MIMO) systems by means of channel state information (CSI) relaxation. In particular, we propose a strategy to take advantage of the correlation experienced by the channels of neighbour antennas when deployed in tightly packed antenna arrays. The proposed scheme is based on collecting CSI for only a subset of antennas during the pilot training stage and, subsequently, using averages of the acquired CSI for the remaining closely-spaced antennas. By doing this, the total number of radio frequency (RF) chains, for both CSI acquisition and data transmission, and the baseband signal processing are reduced, hence simplifying the overall system operation. At the same time, this impacts the quality of the channel estimation produced after the CSI acquisition process. To characterize this tradeoff, we explore the impact that the number of antennas with instantaneous CSI has on the performance, signal processing complexity, and energy efficiency of time-division duplex (TDD) systems. The analytical and simulation results presented in this paper show that the application of the proposed strategy in size-constrained antenna arrays is able to significantly enhance the energy efficiency against systems with full CSI availability, while approximately preserving their average performance.

Massive multiple-input-multiple-output (MIMO) systems deploying a large number of antennas at the base station (BS) have been shown to produce high spectral and energy efficiency (EE) under the assumptions of increasing BS physical space and critical antenna spacing. We examine the deployment of massive MIMO systems and resulting EE with a more realistic scenario considering a 2-D rectangular array with increasing antenna elements within a fixed physical space. Mutual coupling and correlation among the BS antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements within a BS. We also provide a realistic analysis of the energy consumption using a model, which takes into account the circuit power consumptions as a function of the number of BS antennas and then present a performance analysis of two practical low complexity detectors/receivers keeping EE into consideration. The simulation results obtained show that EE does not monotonically increase with the number of BS antennas. On the contrary, it is a decreasing concave or quasi-concave function of the number of BS antennas depending on the detection technique used at the receiver. We also show that with decreasing spacing between the antennas, mutual coupling increases, contributing toward reduction in EE. Our analysis thus shows that EE does not increase infinitely in a massive MIMO system when the increasing number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the antennas. Accordingly, closed-form expressions for the optimum number of antennas to attain maximum EE for zero forcing (ZF) are obtained.

In this paper we explore low-complexity transmission in massive multiple-input multiple-output (MIMO) systems by exploiting their tolerance to incomplete channel state information (CSI). We propose a strategy to exploit the transmit antenna correlation that arises in the deployment of size-constrained massive base stations (BSs). The feasibility of acquiring the instantaneous CSI for a subset of antennas and then using averages of the acquired CSI for the remaining array elements is studied. In particular, we focus on determining the influence that the percentage of inactive antennas during the CSI acquisition stage has over the signal processing (SP) complexity and the system performance. The presented results show that the proposed approach significantly reduces the computational complexity of size-constrained BSs while approximately preserving the performance of massive MIMO systems with complete CSI.

We propose a new beamforming scheme for the multi- user multiple-input-single-output (MISO) downlink channel. While conventional beamforming aims at the minimization of the transmit power subject to suppressing interference to guarantee quality of service (QoS) constraints, here we exploit, rather than suppress, the constructive part of interference. By exploiting the power of constructively interfering symbols, the proposed scheme achieves the required QoS at lower transmit power. In addition, we derive an equivalent virtual multicast formulation for the proposed optimization to facilitate the design of a more efficient solver. Our simulation and analysis show significant power savings for small scale MISO downlink channels with the proposed optimization compared to conventional beamforming optimization.

In this work we present a low-complexity detection scheme for spatial modulation (SM) systems in the large-scale multiple access channel (MAC). The proposed strategy is based on compressive sensing (CS) and it exploits the sparsity and structure of the transmitted SM signals in the MAC to enhance the detection performance. The analytical and simulation results presented in this paper show that the proposed technique outperforms conventional CS and linear detectors with a reduced signal processing complexity.

Low-complexity linear precoders are known to be asymptotically optimal in massive multi-input multi-output (MMIMO) systems. However, employing a very large set of antennas at the transmitter imposes an equally large number of radiofrequency (RF) chains. As a consequence, a MIMO approach for massive systems is prohibitive, as it leads to very high computational burdens and high hardware complexity at the transmitter. In this paper, a low complexity antenna selection (AS) scheme based on constructive interference is presented to reduce hardware complexity. We show that the proposed AS algorithm, combined with a simple matched filter (MF) precoder, outperforms more complex and computationally expensive AS techniques that involve channel inversion (CI) precoding. We derive the computational burdens of the proposed technique and analyze the benefits through the definition of a new power efficiency metric, that combines performances in terms of throughput with the circuit power required.

In this paper we propose a spatial modulation (SM) technique with improved energy efficiency (EE) for the multiple access channel (MAC) with a large number of antennas. The proposed scheme builds upon compressive sensing (CS) and accounts for the sparsity and structure of the signals transmitted via SM in multi-user scenarios to further improve the performance and reduce the complexity of linear detectors. In particular, the proposed technique incorporates additional prior knowledge to conventional CS-based approaches by exploiting the existence of a maximum number of active antennas per user when SM transmission is used in the MAC. The results presented in this paper show that the proposed algorithm offers both a) reduced complexity and b) improved performance compared to conventional CS and linear detection strategies and also allow us to determine the conditions under which the use of SM systems in the MAC is beneficial from an EE point of view.

In this paper we propose a power-efficient transfer of information and energy, where we exploit the constructive part of wireless interference as a source of green useful signal power. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information and wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference. We derive a sub-optimal solution and a local optimum solution to the precoding optimization problem. The proposed precoding reduces the transmit power compared to conventional schemes, by adapting the constraints to accommodate constructive interference as a source of useful signal power. Our simulation results show significant power savings with the proposed data-aided precoding compared to the conventional precoding.

Communications in millimeter-wave (mm-wave) spectrum (30-300 GHz) have experienced a continuous increase in relevance for short-range, high-capacity wireless links, because of the wider bandwidths they are able to provide. In this work, we introduce a new mm-wave frequency transmission scheme that exploits a combination of the concepts of beamspace multi-input multi-output (B-MIMO) communications and beam selection to provide near-optimal performances with a low hardware-complexity transceiver. While large-scale MIMO approaches in mm-wave are affected by high dimensional signal space that increases considerably both complexity and costs of the system, the proposed scheme is able to achieve near-optimal performances with a reduced radio-frequency (RF) complexity thanks to beam selection. We evaluate the advantages of the proposed scheme via capacity computations, comparisons of numbers of RF chains required and by studying the trade-off between spectral and power efficiency. Our analytical and simulation results show that the proposed scheme is capable of offering a significant reduction in RF complexity with a realistic low-cost approach, for a given performance. In particular, we show that the proposed beam selection algorithms achieve higher power efficiencies than a full system where all beams are utilized.

In this paper, we propose a detector, based on the compressive sensing (CS) principles, for multiple-access spatial modulation (SM) channels with a large-scale antenna base station (BS). Particularly, we exploit the use of a large number of antennas at the BSs and the structure and sparsity of the SM transmitted signals to improve the performance of conventional detection algorithms. Based on the above, we design a CS-based detector that allows the reduction of the signal processing load at the BSs particularly pronounced for SM in large-scale multiple-input-multiple-output (MIMO) systems. We further carry out analytical performance and complexity studies of the proposed scheme to evaluate its usefulness. The theoretical and simulation results presented in this paper show that the proposed strategy constitutes a low-complexity alternative to significantly improve the system's energy efficiency against conventional MIMO detection in the multiple-access channel.

The performance of space shift keying (SSK) is known to be dominated by the minimum Euclidean distance (MED) in the received SSK constellation. In this paper, we propose a method of enhancing the MED in the received SSK constellation and improving both the attainable performance and the power efficiency by means of symbol scaling at the transmitter. To this aim, we formulate a pair of optimization problems, one for maximizing the MED subject to a specific transmit power constraint and one for minimizing the transmit power subject to a MED threshold. As these problems are NP-hard, we re-formulate their optimization using semidefinite relaxations, which results in convex problem formulations that can be efficiently solved using standard approaches. Moreover, we design pre-scaling techniques for imperfect channel state information at the transmitter, where the existing approaches are inapplicable. Our results show that the proposed schemes substantially improve the power efficiency of SSK systems with respect to state-of-the-art techniques by offering an improved performance for specific transmit power requirements or, equivalently, a transmit power reduction for a given MED threshold.

A selective vector perturbation technique is introduced for low-power Small Cell downlink. In contrast to conventional vector perturbation (VP) where the search for perturbation vectors involves all users' symbols, here the perturbation is applied to a subset of the transmitted symbols. This therefore introduces a performance-complexity tradeoff, where the complexity is greatly reduced compared to VP by limiting the dimensions of the sphere search, at the expense of a performance penalty compared to VP. By changing the size of the subset of perturbed users, the above tradeoff can be controlled. We further propose three distinct criteria for selecting which users' symbols to perturb, each of which yields a different performance-complexity tradeoff. The presented analytical and simulation results show that the proposed is most useful in the low-power small cell scenarios where power efficiency levels improved by up to 300% compared to VP are demonstrated.

A vector perturbation (VP) scheme is proposed for the downlink of multi-user multiple input multiple output (MU-MIMO) systems with limited feedback. Instead of a computationally expensive sphere search used in conventional VP, the proposed scheme uses a MinMax Optimization to select the perturbation quantities. In addition, the proposed VP circumvents the need for receive-scaling by constraining the search of perturbing vectors to the area in the symbol constellation which is constrictive to the information symbols, i.e. the area where the distances from the decision thresholds are increased with respect to a distance threshold. Consequently, while conventional VP requires the feed-forwarding of the scaling factor to the receiver for correct detection, the proposed scheme does not require the scaling of the received symbols. This advantage is particularly pronounced in limited feedback scenarios where the scaling factors forwarded to the receiver are prone to quantization errors. As illustrated by the results, the error floor encountered in conventional VP in limited feedback scenarios is avoided in the proposed scheme.

We propose a performance-complexity tradeoff for vector perturbation (VP) precoding in the downlink of multiuser multiple input multiple output (MU-MIMO) systems, that bridges the gap between linear and non-linear channel-inversion type precoding. To this end, we introduce a VP design that applies a performance-determined threshold to the desired norm of the preceded signal, to reduce the search through candidate perturbation vectors by the sphere encoder. Once this threshold is met, the search for the perturbation vectors finishes thus saving significant complexity at the transmitter. Towards future largescale MIMO systems, we test t he proposed tradeoff in systems with increasing transmit antennas is fixed physical space. While it is known that linear precoding becomes near-optimal in the massive MIMO region when ignoring transmit correlation, a) we show that t here still remains a performance gap between linear and non-linear precoding once realistic antenna array deployment is considered and b) we utilize the proposed scalable approach to bridge this gap. The presented analysis and results show that t he sum rates achieved are within 90% of those for conventional VP at less than half the computational complexity.

Exploiting interference as a useful resource will be one of the main innovations in 5G communications. In this paper, we explore how to handle the interference to reduce the total transmit power of the Tomlinson-Harashima precoder (THP). We show that the energy efficiency can be improved by adaptively scaling the constellation symbols so that they are better aligned with the interfering signals to be canceled. The theoretical and simulation results of this paper prove that the proposed technique is not only able to preserve the average performance of the conventional THP, but it can also increase its power efficiency by a factor of 2 in the considered scenarios.

With the knowledge of both channel and data information at the base station prior to downlink transmission, we can increase the received signal-to-noise ratio (SNR) of each user without the need to increase the transmitted power. Achievability is based on the idea of phase alignment (PA) precoding where instead of removing the destructive interference, it judiciously rotates the phases of the transmitted symbols. In this way, for each user, the received interference from the other users add up coherently, and consequently we can glean higher received SNRs at all mobile terminals. In addition, it is well-known that the regularized channel inversion (RCI) improves the performance of channel inversion (CI). In line with this and similar to the RCI precoding, in this paper we propose the idea of regularized PA (RPA) which is shown to improve the performance of original PA precoding. To do so, we first rectify the original PA precoding by deriving a closed-form expression of its scaling factor. We then use this new analysis to select an appropriate regularization factor for the proposed RPA scheme. Finally, we drive an explicit formula regarding its received SNR. Since the focus of this work is on linear precoders, we show that the proposed RPA precoding outperforms CI, RCI, and PA precoders from both symbol-error rate (SER) and sum rate perspectives. We also consider the performance of RPA under imperfect channel state information at transmit side. We show that even in this case, RPA precoding is as sensitive as other linear precoders to channel imperfections.

We present a new millimeter-wave (mm-wave) transmission scheme that combines beamspace Multi-Input Multi-Output (B-MIMO) communications and beam selection techniques to achieve near-optimal performances with a low hardware-complexity transceiver. The use of mm-wave frequencies in MIMO systems is afflicted by high complexity and costs, caused by the high dimensional signal space. The proposed scheme is characterized by a reduced radio-frequency (RF) complexity, achieved through beam selection, while leading to near-optimal performances. The benefits of the proposed scheme are evaluated via performance computations, comparisons of numbers of required RF chains and a joint sum-rate and complexity metric that shows the trade-off between spectral and power efficiency. The analytical and numerical results presented in the paper show that the proposed scheme offers an improved power efficiency with respect to the full system with a realistic low-cost approach.

Massive Multiple-Input-Multiple-Output (MIMO) systems deploying a large number of antennas at the base station (BS) have been shown to produce high spectral and energy efficiencies (EE) under the assumptions of increasing BS physical space and critical antenna spacing. We propose a more realistic system model considering a fixed physical space: incorporating coupling and correlation among the BS antennas while providing a realistic analysis of the power consumption using a new power consumption model, taking into account the circuit power consumptions as a function of the number of BS antennas. We also touch on the performance analysis of two practical low complexity detectors keeping EE into consideration. The simulation results obtained show that EE does not monotonically increase with the number of BS antennas. On the contrary, it is a decreasing concave or quasi concave function of the number of BS antennas depending on the detection technique used at the receiver. Also shown is that with decreasing spacing between the antennas, mutual coupling increases contributing towards reduction in EE. Our analysis thus shows that EE does not increase in a massive MIMO system when a large number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the antennas.

We propose a power-efficient Tomlinson-Harashima precoder (THP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems, where a transmit power reduction is achieved by means of interference optimization. The adopted approach is based on adaptively scaling the symbols of a number of users whose received signal-to-noise ratio (SNR) thresholds are known to the transmitter. By doing this, the interference can be better aligned to the symbols of interest, thus reducing the power required to cancel it. The scaling is performed by forming a constrained optimization problem, solved with existing well-known techniques, which entails an increase in the computational complexity at the base station. To quantify this trade-off in performance and complexity, a study of the impact in the signal processing load is carried out by means of a power efficiency analysis. The presented analytical and simulation results in this paper confirm that the proposed technique increases the power efficiency up to 100% with respect to previous THP-based approaches while, at the same time, maintaining the same average performance.

We propose an energy-efficient vector perturbation (VP) technique for the downlink of multiuser multiple-input-single-output (MU-MISO) systems. In contrast to conventional VP where the search for perturbation vectors involves all users' symbols, here, the perturbation is applied to a subset of the transmitted symbols. This, therefore, introduces a performance-complexity tradeoff, where the complexity is greatly reduced compared to VP by limiting the dimensions of the sphere search, at the expense of a performance penalty compared to VP. By changing the size of the subset of perturbed users, the aforementioned tradeoff can be controlled to maximize energy efficiency. We further propose three distinct criteria for selecting which users' symbols to perturb, each of which yields a different performance-complexity tradeoff. The presented analytical and simulation results show that partially perturbing the data provides a favorable tradeoff, particularly at low-power transmission where the power consumption associated with the signal processing becomes dominant. In fact, it is shown that diversity close to the one for conventional VP can be achieved at energy efficiency levels improved by up to 300% compared to VP.

Vector precoding (VP) requires the feed-forwarding of the transmit scaling factor to the receiver for correct detection. This can be problematic in fast fading scenarios where the statistics of the scaling factor change frequently and in limited feedback scenarios where the feed-forwarding to the receiver is prone to quantization errors. In response to this, a new VP scheme is proposed for the downlink of multi-user multiple input multiple output (MU-MIMO) systems with limited feedback. The proposed VP circumvents the need for receive-scaling by constraining the search of perturbing vectors to the area in the symbol constellation which is constrictive to the information symbols, i.e. the area where the distances from the decision thresholds are increased with respect to a distance threshold. By doing this, the perturbation quantities need not be removed at the receiver and successful detection can be done without the use of the modulo operation and the scaling factor. In addition, instead of a computationally expensive sphere search used in conventional VP, the proposed scheme uses a MinMax Optimization to select the perturbation quantities. As illustrated by the analysis and results, the error floor encountered in conventional VP in limited feedback scenarios is avoided in the proposed scheme.

We propose a low-complexity vector precoding (VP) scheme for the downlink of multi-user multiple input multiple output (MU-MIMO) systems. Instead of performing a full sphere search to maximize the receive signal to noise ratio (SNR), the search for the perturbation vectors finishes once a threshold SNR value is reached, thus saving significant computational burden at the transmitter. This threshold is determined by the quality of service (QoS) requirements of the mobile users. To evaluate the advantages of the proposed technique compared to VP, we analytically calculate its computational complexity in terms of the volume of the associated search space. The results show that the proposed thresholded VP (TVP) offers a significantly reduced complexity compared to VP.

We investigate he performance-complexity tradeoff for vector perturbation (VP) precoding in he downlink of multiuser multiple input multiple output (MU-MIMO) systems. To his end, we introduce a VP design ha applies a performance-determined threshold o he desired norm of he preceded signal, o reduce he search through multiple candidate perturbation vectors by he sphere encoder. Once his threshold is me, the search for the perturbation vectors finishes thus saving significant computational burden at the transmitter. Contrary o conventional VP which performs a compuationally intense sphere search to find the absolute minimum of the transmit signal norm, he proposed scheme can trade off error rate performance for a much reduced complexity of precoding. The presented analysis and results show ha he proposed thresholded VP (TVP) offers a complexity reduction of up o 90% in a 4 × 4 and 99% in a 10 × 10 MIMO, for a moderately increased error rate.

A sum rate and throughput enhancing closed form precoding scheme is proposed for multiple input multiple output (MIMO) broadcast systems. The technique proposed utilizes interference judiciously to enhance the received signal energy and improve performance. Instead of completely eliminating interference as in conventional precoders, the proposed adapts the angle of correlation between the MIMO sub-channels, so that interference is aligned to the signal of interest at each receive antenna. By doing so, the co-channel interference (CCI) is always kept constructive and the received signal to interference-plus-noise ratio (SINR) delivered to the mobile units (MUs) is enhanced without the need to invest additional signal power per transmitted symbol at the MIMO base station (BS). By exploiting this important source of green signal power, the presented results and analysis demonstrate significant benefits in the performance of the MIMO downlink.

We propose a low-complexity vector perturbation (VP) precoding scheme for the downlink of multi-user multiple input multiple output (MU-MIMO) systems. While conventional VP performs a computationally intensive sphere search through multiple candidate perturbation vectors to minimize the norm of the precoded signal, the proposed precoder applies a threshold to the desired norm to reduce the number of search nodes visited by the sphere encoder. This threshold is determined by the performance requirements of the mobile users. Once the threshold is met, the search for the perturbation vectors finishes thus saving significant computational burden at the transmitter. To evaluate the advantages of the proposed technique compared to VP, we further derive the computational complexity in terms of the volume of the associated search space and the resulting numerical operations. In addition, we use a new performance-complexity metric to study the relevant tradeoff and look at the power efficiency of the system, both of which metrics can be used to optimize the user-determined threshold accordingly\color{black}. The presented analysis and results show that the proposed thresholded VP (TVP) offers a favorable tradeoff between performance and complexity where significant complexity reduction is attained while the user threshold performance is guaranteed.

We explore the performance of multiple input multiple output (MIMO) transmitters in correlated channels where increasing numbers of antenna elements are fitted in a fixed physical space. As well investigated in the literature, two main effects emerge in such a design: transmit spatial correlation and mutual antenna coupling. In contrast to the literature however, here we investigate the combined effect of reducing the distance between the antenna elements with increasing the number of elements in a fixed transmitter space. In other words, towards the implementation of large-scale MIMO transmitters in limited physical spaces, we investigate the joint effect of two contradicting phenomena: the reduction of spatial diversity due to reducing the separation between antennas and the increase in transmit diversity by increasing the number of elements. Within this context, we analytically approximate the performance of two distinct linear precoding designs. The theoretical analysis and simulations show the somewhat surprising result that for a given number of receivers the improved transmit diversity dominates the performance of practical linear precoders. Consequently, important benefits in the system sum rate can be gleaned by fitting more antenna elements in a fixed space by employing separations smaller than the wavelength of the transmit frequency.

This paper investigates the multiuser diversity introduced by opportunistic user selection in cognitive radio (CR) networks, where multiple cognitive users request to access the spectral resources of the licensed (primary) user. We investigate a simple cognitive user selection strategy aiming at maximizing the received signal-to-interference-plus-noise ratio (SINR) for a given power budget, under interference constraints to the primary. We study the statistics of the SINR at the cognitive receiver, and derive exact analytical expressions of its probability density function (PDF). We then analytically calculate the diversity gains introduced in the system due to the selection of one cognitive user amongst multiple candidates compared to the case when only one cognitive user exists and no selection occurs. Furthermore, we utilize the PDF of the SINR to predict the bit error rate (BER) of the selected cognitive user. Finally, the asymptotic behavior of the diversity gains for the low transmit power region of the primary and cognitive links, and as the number of candidate links becomes large is also investigated. All three multiaccess scenarios are investigated, namely multiple access channel (MAC), broadcast channel (BC) and parallel access channel (PAC), and the results show that the analytically derived expressions closely match simulated performance.

In this paper, we investigate the outage performance of interference aided adaptive linear precoding in the downlink of a multiuser multiple-input-single-output (MISO) overlay cognitive radio (CR) network. While previous research studies on linear precoding techniques in CR network aim to limit or completely cancel the interference to the primary users while achieving maximum downlink throughput of the CR system, we here make use of adaptive linear precoding at the cognitive base station (CBS) to exploit the interference to the primary and the secondary systems. We show by analysis and simulations that the outage performance of the proposed precoding technique is significantly enhanced in comparison to the conventional techniques.

In this paper, an improved multiple input multiple output Tomlinson-Harashima precoder (MIMO-THP) is introduced, where the transmit power loss is reduced based on optimizing the interference to be canceled. The concept behind the proposed technique builds on the fact that both the desired and interfering signals originate from the base station (BS) of the downlink system itself. Based on this observation, the proposed method influences the resulting interference, to reduce the transmission power required to cancel it, without altering the information content of the downlink message. The aim is to bring the interference closer to the replicas of the desired symbols for all users in the THP modulo-extended constellation. In this way, the power required to pre-subtract interference is decreased. Theoretical and simulation results both confirm that, by optimizing the interference to be canceled, the proposed technique offers a considerable transmit power reduction compared to conventional THP while securing an equal error rate performance.

We investigate the outage performance of relay assisted transmissions of coexisting radio systems. Employing simple linear precoding designs, we explore the potential of utilising the cross-interference between the two coexisting systems to mutually improve performance. While conventionally the aim of the relay is to isolate the two concurrent transmissions by completely removing the interference between them, we make use of interference energy when the interference between the two systems is mutually constructive. In this direction two adaptive linear precoding techniques are proposed for the relay and compared to conventional precoding. We use the cognitive radio paradigm to illustrate the associated benefits. It is shown by theoretical analysis and simulation that the constructive interference provides a source of additional green signal energy that benefits the outage performance of both communication links.

This paper proposes a dynamic symbol adaptation scheme for downlink and point-to-point multiple input multiple output (MIMO) systems aiming at the minimization of the overall system bit error rate. The proposed scheme adaptively changes the order or the values of the symbols to be transmitted within a sub-frame of symbols with the objective to enhance the received symbols' power. The adaptation is based on the instantaneous interference between the symbols taking into account the channel's characteristics as well as the actual symbol values. This procedure is targeted to optimizing, rather than strictly minimizing the interference between the symbols. In this way, constructive instantaneous interference is utilized in enhancing the decision variables at the receiver on a symbol-by-symbol basis, so that detection is made more reliable. The proposed scheme can be used in conjunction with various conventional MIMO precoding and detection techniques. The presented simulations show that it provides significant benefits to the corresponding conventional system's error rate performance.

This work focuses on the admission of new (secondary) users in a network where existing (primary) users are interference aligned. One of the challenges in this scenario is to achieve the promised degrees of freedom for the secondary users with limited available dimensions. We design a secondary admission network to achieve interference alignment as in equivalent general peer-peer networks, even when the secondary nodes are configured with limited antenna/spatial dimensions, along with a very strict zero-interference constraint from the primary network. To provide enough dimensions for secondary users to achieve interference alignment, we utilize three effective methods: a) time extension structure; b) adaptivity to network partial connectivity; c) partial interference alignment. The presented analysis shows how the degrees of freedom (DoF) can be achieved for the secondary network in both the constant and time-variant channels.

This paper investigates the potential of exploiting interference as a source of green signal energy in parallel transmissions of co-existing radio systems assisted by a cognitive relay. Assuming a cognitive radio setup, the purpose of the relay is to allow the resources of a primary downlink to be efficiently utilised by a secondary system. While conventionally the relay aims to completely remove the interference, we investigate a strategy of making use of interference energy when the cross-interference between the two systems is mutually constructive. In this way, the interference that already exists in the communication medium provides a source of green signal energy that mutually enhances the received signal power of primary and secondary users without the need to raise the transmitted power. In this direction two adaptive linear precoding techniques are proposed for the cognitive relay and compared to conventional precoding. The effect of green interference on the received signal to noise ratio (SNR) of the primary and secondary users is studied through theoretical analysis and used to predict the resulting error probability and outage performance. The results show that by exploiting free interference power, the secondary downlink can access the primary resources without deteriorating the primary users' performance.

This paper investigates the potential of exploiting interference power in order to enhance the performance of multiple input multiple output (MIMO) broadcast systems using phase shift keying (PSK) modulation. Towards this end, the proposed linear precoding aims at adaptively rotating, rather than zeroing, the correlation between the MIMO sub-streams depending on the transmitted data, so that the signal of interfering transmissions is aligned to the signal of interest at each receive antenna. By doing so, the co-channel interference (CCI) is always kept constructive and the received signal to interference-plus-noise ratio (SINR) delivered to the mobile units (MUs) is enhanced without the need to invest additional signal power per transmitted symbol at the MIMO base station (BS). It is shown by means of theoretical analysis and simulations that by using interference as a source of green energy the proposed technique outperforms conventional precoding.

This paper investigates the multiuser diversity introduced by opportunistic user selection in the cognitive radio parallel access channel (CR-PAC), where multiple cognitive users request to access the spectral resources of the licensed user. Assuming a simple cognitive user selection strategy based on maximizing the received signal-to-interference-plus- noise ratio (SINR), we study the statistics of the SINR at the cognitive receiver. We then use this to analytically calculate the diversity gains introduced in the system due to the selection of one cognitive user amongst multiple candidates, compared to the case when only one cognitive user exists in the network and no selection occurs. Finally, we investigate potential gains for the primary network from this user selection. The results show a close match between analytical expressions and simulation results, while a tight lower bound for the multiuser gain is derived in closed form.

This paper investigates linear precoding designs for cognitive relay assisted transmissions of coexisting radio systems. The aim of this work is to characterise the interference between the primary and secondary systems in the cognitive radio setup when transmission is assisted by a cognitive relay. While conventionally the aim of the relay is to completely remove the interference between primary and secondary transmissions, we explore the potential of making use of interference energy when the interference between the two systems is mutually constructive. In this direction two adaptive linear precoding techniques are proposed for the cognitive relay and compared to conventional precoding. The presented theoretical analysis and simulations show that the proposed outperform conventional techniques, as the existence of constructive interference energy improves the received signal to noise ratio (SNR) at both primary and secondary receivers.

A dynamic symbol mapping technique is proposed for downlink and point-to-point multiple input multiple output (MIMO) systems. The proposed mapping introduces a perturbation to the transmitted symbols by adaptively changing the order or the values of the symbols to be transmitted within a sub-frame of symbols with the objective to enhance the received symbols' power. The symbol mapping aims at taking advantage of constructive instantaneous interference, considering the channel's characteristics as well as the actual symbol values. In other words, rather than minimising interference between the symbols, the goal is to optimize the resulting interference so that it is more constructive. In this way the received symbol energy is enhanced and detection is made more reliable. It is shown by the presented results and analysis that it provides significant benefits to the corresponding conventional system's error rate performance.

A novel linear precoding scheme is proposed for the downlink of multiuser multiple input multiple output (MIMO) systems, that offers enhanced data throughput compared to conventional precoding. The proposed technique is based on the fact that part of the instantaneous multiple access interference (MAI) inherent in a multiuser MIMO system adds constructively to the useful signal. It utilises this concept by applying partial linear precoding such that the destructive part is eliminated while the constructive part of MAI is preserved and exploited. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced, without the need to invest in additional transmitted signal power at the MIMO base station (BS). The presented analysis and simulations verify that the proposed precoding scheme significantly enhances the data throughput compared to conventional precoding.

An extension to decomposition based transmit beamforming is proposed for multiple input multiple output (MIMO) multiuser systems. In contrast to conventional beamforming which is constrained towards orthogonalizing the subchannels of the different users to yield groups of correlated antennas that belong to only one user, a more flexible orthogonalization approach is proposed which is targeted directly towards the optimization of the decision variables at the multi-antenna receivers of all users. The resulting groups of correlated antennas may consist of elements belonging to multiple users. Thus, the proposed beamforming has more degrees of freedom and therefore attains a more efficient performance optimization. The trade-off to the performance improvement is an increase in the precoding complexity imposed by the adaptive nature of the proposed beamforming. A suboptimal adaptive-decomposition beamforming scheme is therefore also proposed with a reduced complexity overhead. Comparative simulations of the proposed scheme to conventional beamforming show considerable error rate reductions that justify the increased complexity and verify the superiority of the proposed scheme.

This paper proposes a hybrid transmission technique based on code optimization and partial linear precoding for interference exploitation on the downlink of phase shift keying (PSK) based mulitcarrier code division multiple access (MC-CDMA) systems. The first stage of the technique is to dynamically allocate codes to the users so that the combination of instantaneous data and user cross correlations yield a favourable constructive to destructive multiple access interference (MAI) ratio. To further optimize the received signal to interference plus noise ratio (SINR), the second stage is to employ a dynamic, partial transmitter based pre-decorrelation scheme specifically designed for the exploitation of constructive MAI. The decorrelation processing is targeted to the destructive interferers, while users that interfere constructively are let correlated. This results in a significant SINR enhancement without the need for additional power-per-user investment. Mathematical analysis and simulations show that significant bit error rate (BER) performance benefits can be achieved with this technique.

In this paper a novel channel inversion (CI) precoding scheme is introduced for the downlink of multiple input multiple output (MIMO) systems. The proposed technique outperforms conventional CI by exploiting some of the consequential inter- channel interference (ICI). It achieves this by applying partial channel inversion such that the constructive part of ICI is preserved and exploited while the destructive part is eliminated by means of CI precoding. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced, without the need to invest in additional transmitted signal power at the MIMO base station (BS). The trade-off to this achievement is a minor increase in the complexity of the BS processing. The presented theoretical analysis and simulations show that due to the SINR enhancement, significant performance gains are offered by the proposed MIMO precoding technique compared to its conventional counterpart.

A novel antenna selection scheme is introduced in conjunction with a partial linear preceding technique for downlink and point-to-point multiple input multiple output (MIMO) systems. The proposed technique outperforms conventional channel inversion (CI) linear preceding by utilizing part of the existing inter-channel interference (ICI) between the MIMO sub-channels. It applies partial sub-channel orthogonalization in contrast to the full decorrelation of the channels attained by conventional CI. This yields an increased signal to interference-plus-noise ratio (SINR) to the multi-antenna receiver, without investing additional transmitted signal power at the multi-antenna transmitter. This benefits performance and throughput in a variety of transmission scenarios. However, the focus of this paper is on transmit antenna selection where a customized selection criterion is introduced that enhances the residue-interference optimization. Comparative simulations to conventional schemes are presented to illustrate the superiority of the proposed technique.

The power constraint factor in precoding methods plays an important role in the reduction of SER at the receiver. The value of this scaling factor lies in the individual power assigned to the transmit symbols prior the transmission. Such assignment will depend entirely on the matrix condition of the channel, in this case the eigenvalue's power of the channel inverse. Thus, the minimisation of this power constraint will rely on closing the power gap among the transmit symbols, and one solution is to use an auxiliary vector for fixing the matrix condition. This is equivalent to solving the integer least-squares problem. For achieving this specific goal there are two effective choices: the lattice-reduction, whose objective is to reduce the basis of any given matrix, and the sphere techniques which enumerate all the lattice points inside a sphere centered at the query point. Both choices aim for finding or fitting an approximated least-squares solution occasioning astonishing results in performance when minimising the scaling factor prior to transmit.

This paper introduces a novel channel inversion (CI) precoding scheme for the downlink of phase shift keying (PSK)-based multiple input multiple output (MIMO) systems. In contrast to common practice where knowledge of the interference is used to eliminate it, the main idea proposed here is to use this knowledge to glean benefit from the interference. It will be shown that the system performance can be enhanced by exploiting some of the existent inter-channel interference (ICI). This is achieved by applying partial channel inversion such that the constructive part of ICI is preserved and exploited while the destructive part is eliminated by means of CI precoding. By doing so, the effective signal to interference-plus-noise ratio (SINR) delivered to the mobile unit (MU) receivers is enhanced without the need to invest additional transmitted signal power at the MIMO base station (BS). It is shown that the trade-off to this benefit is a minor increase in the complexity of the BS processing. The presented theoretical analysis and simulations demonstrate that due to the SINR enhancement, significant performance and throughput gains are offered by the proposed MIMO precoding technique compared to its conventional counterparts.

This paper proposes a hybrid transmission technique based on adaptive code-to-user allocation and linear precoding for the downlink of phase shift keying (PSK) based multi-carrier code division multiple access (MC-CDMA) systems. The proposed scheme is based on the separation of the instantaneous multiple access interference (MAI) into constructive and destructive components taking into account the dependency on both the channel variation and the instantaneous symbol values of the active users. The first stage of the proposed technique is to adaptively distribute the available spreading sequences to the users on a symbol-by-symbol basis in the form of codehopping with the objective to steer the users' instantaneous crosscorrelations to yield a favourable constructive to destructive MAI ratio. The second stage is to employ a partial transmitter based zero forcing (ZF) scheme specifically designed for the exploitation of constructive MAI. The partial ZF processing decorrelates destructive interferers, while users that interfere constructively remain correlated. This results in a signal to interference-plus-noise ratio (SINR) enhancement without the need for additional power-per-user investment. It will be shown in the results section that significant bit error rate (BER) performance benefits can be achieved with this technique.

A novel code optimization scheme is presented for the downlink of multicarrier code division multiple access (MC-CDMA) systems employing phase shift keying (PSK) modulation. The optimization is based on dynamically re-allocating the available signature waveforms amongst the users on a symbol-by-symbol basis in order to manipulate and utilize interference amongst them. By applying the appropriate allocation criteria destructive interference between users is minimized while constructive is maximized. As a result, employing this on top of conventional schemes enhances the received signal to interference-plus-noise ratio (SINR) without additional transmitted power-per-user investment. The trade-off to the resulting performance improvement is the need for transmission of some extra side information, which by using the proposed processing is kept to a minimum. While performance benefits can be expected for various scenarios, the focus of this paper is on MC-CDMA precoding schemes. Analysis and simulations show a considerable bit error rate (BER) reduction for the case of decorrelating precoding methods.

Finding the least-squares solution to a system of linear equations where the unknown vector is comprised of integers, but the matrix coefficient and given vector are comprised of real or complex numbers is a problem equivalent to finding the closest lattice-point to a given point and is well known that the search is hard. However, in communications applications the given vector is not arbitrary but rather is an unknown lattice-point that has been perturbed by an additive offset vector whose statistical properties are known, making it relatively easier to decode. In this paper we will discuss the vector- perturbation technique proposed for solving this problem and analyse a possible solution for overcome the complexity issues.

In this paper a new dynamic code distribution technique is presented. This method chooses the optimal code-to-user allocation for transmission between different combinations of the available signature waveforms according to the data to be transmitted at every symbol period by the base station (BS) at the downlink of a cellular code division multiple access (CDMA) system. The selection is done with the aim to minimise the destructive and maximize constructive interference between users. This effectively spreads the signal constellation and enhances the signal to interference-plus-noise ratio (SINR) at the receiver with no additional power-per-user investment. However, the trade-off to this is some extra side information. Theoretical analysis and simulations presented suggest that this method can provide a bit error rate reduction of an order of a magnitude when combined with multiuser detection.

A novel transmitter code optimisation technique based on adaptive code-to-user allocation is presented for interference exploitation on the time division duplex downlink of binary phase shift keying-based multiple carrier code division multiple access systems. The principle of the proposed technique is to exploit the dependency of multiple access interference on the instantaneous symbol values of the active users. The objective is to adaptively allocate the available spreading sequences to users on a symbol-by-symbol basis to enhance the ratio between constructive and destructive interference and thus optimise the decision variables at the downlink receivers. The resulting signal-to-interference plus noise improvement happens by making use of the energy inherent in the system so the performance benefit is attained with no additional power-per-user investment. It will be shown that when this optimisation technique is incorporated with common pre- and post-equalisation schemes, a significant bit error rate performance improvement is achieved while the associated adaptation overhead is kept less than 6% of the available bandwidth.

A new technique based on adaptive code-to-user allocation for interference management on the downlink of BPSK based TDD DS-CDMA systems is presented. The principle of the proposed technique is to exploit the dependency of multiple access interference on the instantaneous symbol values of the active users. The objective is to adaptively allocate the available spreading sequences to users on a symbol-by-symbol basis to optimize the decision variables at the downlink receivers. The presented simulations show an overall system BER performance improvement of more than an order of a magnitude with the proposed technique while the adaptation overhead is kept less than 10% of the available bandwidth.

A novel scheme is presented that enhances the near-far resistance of conventional transmitter precoding for CDMA with unequal power distribution. The proposed technique applies a partial pre-decorrelation to the users in contrast to the full orthogonalisation performed in conventional precoding. When the appropriate criteria are employed for the selection of the users to be decorrelated, an improved signal to interference-plus-noise ratio (SINR) is obtained without the need for additional power-per-user investment. Performance enhancement is attained for a variety of communication scenarios, but the focus of this paper is on unequal power users systems and the resistance to the near-far effect. Comparative simulations to conventional precoding methods demonstrate the superiority of the proposed technique.

In this paper a new transmitter preceding technique is presented that outperforms conventional preceding by making use of a portion of the interference between the users in a CDMA system downlink. The proposed technique selectively pre-decorrelates users that are experiencing destructive interference while allowing interference to other users when it is expected to contribute to their signal. The existence and exploitation of constructive interference effectively spreads the signal constellation and enhances the SNR at the receiver. The SNR improvement happens by making use of energy that is already in the system so the performance improvement is attained with no additional power-per-user investment. This however comes with the trade-off of some extra processing at the transmitter for the measurement of the expected interference. The proposed technique applies to the downlink of cellular CDMA systems employing PSK modulation. Theoretical analysis supported by comparative simulations of this and other preceding methods are presented and discussed.

Multiple-input multiple-output (MIMO) systems are still attracting the attention of many researchers and designers for their promising improvements in performance and bandwidth efficiency. This paper presents a low-complexity precoding technique which aims to maximise the MIMO diversity at the receiver. The proposed technique achieves maximum diversity by matching the transmitted data symbols with a suitable precoding matrix from a list of predetermined precoding matrices at the transmitter. This paper shows that significant performance improvements can be obtained with this technique and without any bandwidth degradation.

A new transmitter precoding technique is presented that selectively pre-decorrelates users to destructive multiple access interference while allowing interference when it is expected to contribute to the useful signal. This enhances the effective signal to interference-plus-noise ratio at the receiver. The resulting benefit comes by making use of energy that is already in the system so performance improvement is attained with no additional power-per-user investment. The proposed technique applies to the downlink of cellular CDMA systems employing phase-shift-keying modulation.