Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance.

Recent advancements in large-scale position-reconfigurable antennas have opened up new dimensions to effectively utilize the spatial degrees of freedom (DoFs) of wireless channels. However, the deployment of existing antenna placement schemes is primarily hindered by their limited scalability and frequently overlooked near-field effects in large-scale antenna systems. In this article, we propose a novel antenna placement approach tailored for near-field massive multiple-input multiple-output systems, which effectively exploits the spatial DoFs to enhance spectral efficiency. For that purpose, we first reformulate the antenna placement problem in the angular domain, resulting in a weighted Fekete problem. We then derive the optimality condition and reveal that the optimal antenna placement is in principle an electrostatic equilibrium problem. To further reduce the computational complexity of numerical optimization, we propose an ordinary differential equation (ODE)-based framework to efficiently solve the equilibrium problem. In particular, the optimal antenna positions are characterized by the roots of the polynomial solutions to specific ODEs in the normalized angular domain. By simply adopting a two-step eigenvalue decomposition (EVD) approach, the optimal antenna positions can be efficiently obtained. Furthermore, we perform an asymptotic analysis when the antenna size tends to infinity, which yields a closed-form solution. Simulation results demonstrate that the proposed scheme efficiently harnesses the spatial DoFs of near-field channels with prominent gains in spectral efficiency and maintains robustness against system parameter mismatches. In addition, the derived asymptotic closed-form solution closely approaches the theoretical optimum across a wide range of practical scenarios.

The advent of massive multiple-input multiple-output (MIMO) technology has provided new opportunities for capacity improvement via strategic antenna deployment, especially when the near-field effect is pronounced due to antenna proliferation. In this paper, we investigate the optimal antenna placement for maximizing the achievable rate of a point-to-point near-field channel, where the transmitter is deployed with massive movable antennas. First, we propose a novel design framework to explore the relationship between antenna positions and achievable data rate. By introducing the continuous antenna position function (APF) and antenna density function (ADF), we reformulate the antenna position design problem from the discrete to the continuous domain, which maximizes the achievable rate functional with respect to ADF. Leveraging functional analysis and variational methods, we derive the optimal ADF condition and propose a gradient-based algorithm for numerical solutions under general channel conditions. Furthermore, for the near-field line-of-sight (LoS) scenario, we present a closed-form solution for the optimal ADF, revealing the critical role of edge antenna density in enhancing the achievable rate. Finally, we propose a flexible antenna array-based deployment method that ensures practical implementation while mitigating mutual coupling issues. Simulation results demonstrate the effectiveness of the proposed framework, with uniform circular arrays emerging as a promising geometry for balancing performance and deployment feasibility in near-field communications.

In this paper, we investigate a bistatic integrated sensing and communications (ISAC) system, consisting of a base station (BS) with multiple transmit antennas, a sensing receiver with multiple receive antennas, a single-antenna communication user (CU), and a point target to be sensed. Specifically, the BS transmits a superposition of Gaussian information and deterministic sensing signals to support ISAC. The BS aims to deliver information symbols to the CU, while the sensing receiver aims to estimate the target’s direction-of-arrival (DoA) with respect to the sensing receiver by processing the echo signals reflected by the target. For the sensing receiver, we assume that only the sequences of the deterministic sensing signals and the covariance matrix of the information signals are perfectly known, whereas the specific realizations of the information signals remain unavailable. Under this setup, we first derive the corresponding Cram´er-Rao bounds (CRBs) for DoA estimation and propose practical estimators to accurately estimate the target’s DoA. Subsequently, we formulate the transmit beamforming design as an optimization problem aiming to minimize the CRB, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the CU and a maximum transmit power constraint at the BS. When the BS employs only Gaussian information signals, the resulting beamforming optimization problem is convex, enabling the derivation of an optimal solution. In contrast, when both Gaussian information and deterministic sensing signals are transmitted, the resulting problem is non-convex and a locally optimal solution is acquired by exploiting successive convex approximation (SCA). Finally, numerical results demonstrate that the utilization of additional deterministic sensing signals is critical for sensing performance enhancement, while solely employing Gaussian information signals leads to a notable performance degradation for target sensing. It is unveiled that the proposed transmit beamforming design achieves a superior ISAC performance boundary compared with various benchmark schemes.

Pinching-antenna systems (PASS) have been recently proposed to improve the performance of wireless networks by reconfiguring both the large-scale and small-scale channel conditions. However, existing studies ignore the physical constraints of antenna placement and assume fixed antenna radiation power. To overcome this limitation, this paper investigates the design of PASS, taking into account the motion power consumption of pinching antennas (PAs) and the impact of adjustable antenna radiation power. To that end, we minimize the average power consumption for a given quality-of-service (QoS) requirement by jointly optimizing the antenna positions, antenna radiation power ratios, and transmit beamforming. To the best of the authors’ knowledge, this is the first work to consider radiation power optimization in PASS, which provides an additional degree of freedom (DoF) for system design. The cases with both continuous and discrete antenna placement are considered, where the main challenge lies in the fact that the antenna positions affect both the magnitude and phase of the channel coefficients of PASS, making system optimization very challenging. To tackle the resulting unique obstacles, an alternating direction method of multipliers (ADMM)-based framework is proposed to solve the problem for continuous antenna movement, while its discrete counterpart is formulated as a mixed integer nonlinear programming (MINLP) problem and solved by the block coordinate descent (BCD) method. Simulation results validate the performance enhancement achieved by incorporating PA movement power consumption and adjustable radiation power into the PASS design, while also demonstrating the efficiency of the proposed optimization framework. The benefits of PASS over conventional multiple-input multiple-output (MIMO) systems in mitigating the large-scale path loss and inter-user interference are also revealed.

Integrated sensing and communications (ISAC) is one of the key enabling technologies in future sixth-generation (6G) networks. Current ISAC systems predominantly rely on deterministic pilot signals within the signal frame to accomplish sensing tasks. However, these pilot signals typically occupy only a small portion, e.g., 3% to 25%, of the time-frequency resources. To enhance the system utility, a promising solution is to repurpose the extensive random data payload signals for sensing tasks. In this paper, we analyze the ISAC performance of a multi-antenna system where both deterministic pilot and random data symbols are employed for sensing tasks. By capitalizing on random matrix theory (RMT), we first derive a semi-closed-form asymptotic expression of the ergodic linear minimum mean square error (ELMMSE), which evaluates the average sensing error of ISAC systems involving random data payload signals. Then, we formulate an ISAC precoding optimization problem to minimize the ELMMSE, which is solved via a specifically tailored successive convex approximation (SAC) algorithm. To provide system insights, we further derive a closed-form expression for the asymptotic ELMMSE at high signal-to-noise ratios (SNRs). Our analysis reveals that, compared with conventional sensing implemented by deterministic signals, the sensing performance degradation induced by random signals is critically determined by the ratio of the transmit antenna size to the data symbol length. Based on this result, the ISAC precoding optimization problem at high SNRs is transformed into a convex optimization problem that can be efficiently solved. Simulation results validate the accuracy of the derived asymptotic expressions of ELMMSE and the performance of the proposed precoding schemes. Particularly, by leveraging data payload signals for sensing tasks, the sensing error is reduced by up to 5.6 dB compared to conventional pilot-based sensing.

This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation services for authorized unmanned aerial vehicles (UAVs), while sensing the low-altitude airspace to monitor the unauthorized mobile target. The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs’ trajectories, subject to the constraints on the average signal-to-noise ratio requirement for sensing, the flight mission and collision avoidance of UAVs, as well as the maximum transmit power at the GBS. Typically, this is a sequential decision-making problem with the given flight mission. Thus, we transform it to a specific Markov decision process (MDP) model called episode task. Based on this modeling, we propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique. In DeepLSC, a reward function and a new action selection policy termed constrained noise-exploration policy are judiciously designed to fulfill various constraints. To enable efficient learning in episode tasks, we develop a hierarchical experience replay mechanism, where the gist is to employ all experiences generated within each episode to jointly train the neural network. Besides, to enhance the convergence speed of DeepLSC, a symmetric experience augmentation mechanism, which simultaneously permutes the indexes of all variables to enrich available experience sets, is proposed. Simulation results demonstrate that compared with benchmarks, DeepLSC yields a higher sum-rate while meeting the preset constraints, achieves faster convergence, and is more robust against different settings.

This paper investigates the joint modulation-coding scheme (MCS) adaptation and beamforming design for multi-user multi-input single-output (MISO) systems, where one base station serves multiple user equipments (UEs) under imperfect and outdated channel state information (CSI). The sum-rate of the system is maximized while satisfying all UEs’ data rate requirements and the maximum transmit power constraint at the BS. Most existing beamforming designs overlooked that only a finite number of MCSs can be supported in practical communication systems. Moreover, previous works rely on perfect and real-time CSI for decision-making, neglecting processing delays and channel estimation errors. To circumvent the above issues, this paper puts forth an intelligent joint optimization scheme based on deep reinforcement learning (DRL) techniques. Specifically, a new DRL framework, termed constrained hybrid DRL (CHDRL), is first proposed, which incorporates Lagrangian primal-dual optimization theory and a constrained action selection policy into conventional DRL to tackle various constraints. By integrating deep Q-network (DQN) and deep deterministic policy gradient algorithms, CHDRL is capable of simultaneously optimizing MCS in the discrete action domain and beamforming in the continuous action domain. In addition, to handle the large discrete action space of DQN, we develop an action branch architecture for CHDRL to enable independent and concurrent MCS decisions at different UEs. Finally, a multi-parameter experience replay mechanism is designed to synchronously train Lagrangian multipliers and neural network parameters. Simulation results demonstrate that under imperfect and outdated CSI, CHDRL outperforms other benchmark schemes by (i) achieving a significantly higher sum-rate, (ii) meeting more UEs’ data rate requirements, and (iii) being more robust against different CSI delays and numbers of UEs.

Intelligent omni-surface (IOS) is a promising technique to enhance the capacity of wireless networks, by reflecting and refracting the incident signal simultaneously. Traditional IOS configuration schemes, relying on all subchannels’ channel state information and user equipments’ mobility, are difficult to implement in complex realistic systems. Existing works attempt to address this issue employing deep reinforcement learning (DRL), but this method requires a lot of trial-and-error interactions with the external environment for efficient results and thus cannot satisfy the real-time decision making. To enable model-free and real-time IOS control, this article puts forth a new framework that integrates DRL and digital twins. As a first step, deep reinforcement learning IOS (DeepIOS), a DRL based IOS configuration scheme with the goal of maximizing the sum data rate, is developed to jointly optimize the phase-shift and amplitude of IOS in multiuser multiple-input-multiple-output (MU-MIMO) systems. Thereafter, in order to further reduce the computational complexity, DeepIOS introduces an action branch architecture, which decides two optimization variables in parallel in a separate fashion. Finally, a digital twin module is constructed through supervised learning as a preverification platform for DeepIOS, such that the decision making’s real-time can be guaranteed. The formulated framework is a closed-loop system, in which the physical space provides data to establish and calibrate the digital space, while the digital space generates a large number of experience samples for DeepIOS training and sends the trained parameters to the IOS controller for configurations. Numerical results show that compared with random and MAB schemes, the proposed framework attains a higher data rate and is more robust to different settings. Furthermore, the action branch architecture reduces DeepIOS’s computational complexity, and the digital twin module improves DeepIOS’s convergence speed and run-time.

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%.

This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cramér-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex due to the binary on-off non-transmission power consumption of RF chains and PSs, the non-linear PA efficiency, and the coupling between analog and digital beamformers. To tackle this problem, we first approximate the binary on-off non-transmission power consumption into a continuous form, and accordingly propose an iterative algorithm to find a high-quality approximate solution with ensured convergence, by employing techniques from alternating optimization (AO), sequential convex approximation (SCA), and semi-definite relaxation (SDR). Then, based on the optimized beamforming weights, we develop an efficient method to determine the binary on-off control of RF chains and PSs, as well as the associated hybrid beamforming solution. Numerical results show that the proposed design achieves an improved energy efficiency for ISCAP than other benchmark schemes without joint design of hybrid beamforming and dynamic on-off control. This validates the benefit of dynamic on-off control in energy reduction, especially when the multi-functional performance requirements become less stringent.

Intelligent reflecting surfaces (IRSs) have been regarded as a promising enabler for future wireless communication systems due to their capability of customizing favorable propagation environments. In the literature, IRSs have been considered power-free or assumed to have constant power consumption. However, recent experimental results have shown that for positive-intrinsic-negative (PIN) diode-based IRSs, the power consumption dynamically changes with the phase shift configuration, which implies that the beamforming quality of the IRS depends on the available power. Therefore, this phase shift-dependent power consumption (PS-DPC) introduces a challenging power allocation problem between the base station (BS) and the IRS, requiring to balance the BS transmit power and the IRS beamforming quality during system design. To tackle this issue, in this paper, we investigate a rate maximization problem for IRS-assisted systems under a practical PS-DPC model. For the single-user case, we propose a generalized Benders decomposition-based beamforming method to maximize the achievable rate while satisfying a total system power consumption constraint. Moreover, we propose a low-complexity beamforming design, where the powers allocated to BS and IRS are optimized offline based on statistical channel state information. Furthermore, we extend the beamforming design to the multi-user case, where we solve an equivalent weighted mean square error minimization problem with two different joint power allocation and phase shift optimization methods. Simulation results indicate that compared to baseline schemes, our proposed methods can flexibly optimize the power allocation between BS and IRS, thus achieving better performance. The optimized power allocation strategy strongly depends on the system power budget. Specifically, when the available system power budget is high, the PS-DPC is not the dominant factor in the system power consumption, allowing the IRS to turn on as many PIN diodes as needed to achieve high beamforming quality. When the system power budget is limited, however, more power tends to be allocated to the BS to enhance the transmit power, which consequently reduces the beamforming quality at the IRS due to the limited PS-DPC budget.

Cell-free massive MIMO is one of the core technologies for next-generation wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, energy efficiency, and uniform coverage. However, the radically distributed architecture of cell-free massive MIMO necessitates new paradigms for transceiver design, especially by exploiting efficient distributed processing algorithms. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO, which consists of two modules: a nonlinear module at the central processing unit (CPU) and a linear module at each access point (AP). The turbo principle in iterative channel decoding is utilized to compute and pass the extrinsic information between the two modules. An analytical framework is provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Furthermore, a distributed iterative channel estimation and data detection (ICD) algorithm is developed to handle the practical scenario with imperfect channel state information (CSI). Simulation results will show that the proposed method outperforms existing detectors for cell-free massive MIMO systems in terms of the bit-error rate and the developed theoretical analysis can be utilized as an asymptotic lower bound. Finally, it is shown that with imperfect CSI, the proposed ICD algorithm can significantly improve the system performance and reduce the pilot overhead.

This paper investigates an intelligent omni-surface (IOS)-assisted integrated sensing and communication (ISAC) system, where a base station provides both target sensing and communication services with an IOS. The sensing signal-to-noise ratio (SNR) is maximized while satisfying the communication requirement by optimizing IOS configurations. Conventional approaches typically need real-time and accurate channel state information (CSI) and have high computational complexity, making them difficult to implement in realistic systems. To circumvent this problem, this paper puts forth a new framework based on deep reinforcement learning (DRL) with knowledge transfer. In particular, an online learning scheme called Deep reinforcement learning IOS-ISAC (DeepOSC), is first proposed to optimize the reflecting and refracting coefficients of the IOS. Thereafter, to enable powerful reasoning and fast decision-making, we incorporate an echo state network (ESN) with separate output into DeepOSC. To further accelerate convergence, two transfer learning approaches, namely staged policy reuse (SPR) and staged policy distillation (SPD), are developed to guide the learning process of a newly deployed agent by leveraging policies of pre-trained agents. Numerical results show that compared to various benchmarks, DeepOSC attains significant sensing and communication performance gains and is more robust against outdated CSI coefficients. In addition, in comparison to conventional neural networks, ESN shortens the run-time of DeepOSC by more than ten times and is more efficient for temporal inference. Besides, we demonstrate the capabilities of SPR and SPD in accelerating the convergence of DeepOSC.

Rapid advancement of antenna technology catalyses the popularization of extremely large-scale multiple-input multiple-output (XL-MIMO) antenna arrays, which pose unique challenges for localization with the inescapable near-field effect. In this paper, we propose an efficient near-field localization algorithm by leveraging a sectored uniform circular array (sUCA). In particular, we first customize a backprojection algorithm in the polar coordinate for sUCA-enabled near-field localization, which facilitates the target detection procedure. We then analyze the resolutions in both angular and distance domains via deriving the interval of zero-crossing points, and further unravel the minimum required number of antennas to eliminate grating lobes. The proposed localization method is finally implemented using fast Fourier transform (FFT) to reduce computational complexity. Simulation results verify the resolution analysis and demonstrate that the proposed method remarkably outperforms conventional localization algorithms in terms of localization accuracy. Moreover, the low-complexity FFT implementation achieves an average runtime that is hundreds of times faster when large numbers of antenna elements are employed.

The multiple-input and multiple-output (MIMO) technique is considered as a promising approach for improving the throughput and reliability of optical fiber communications. However, the finite blocklength (FBL) analysis of optical fiber MIMO systems is not available in the literature. Considering the Jacobi MIMO channel, which was proposed to model the nearly lossless propagation and the crosstalks in optical fiber channels, this paper studies the optimal average error probability (OAEP) of optical fiber multicore/multimode systems in the FBL regime. In particular, we consider the case where the coding rate is in the ${\mathcal{O}}\left({\frac{1}{{\sqrt {LM} }}}\right)$ proximity of the capacity, with M and L denoting the number of transmit channels and blocklength, respectively. To this end, a central limit theorem (CLT) for the information density is first established in the asymptotic regime where the blocklength and the number of transmit, receive, and available channels approach infinity with fixed ratios. With the aid of the CLT, the closed-form upper and lower bounds for the OAEP with the concerned rate are then derived. It is shown that the derived bounds could degenerate to those for Rayleigh MIMO channels if the number of available channels goes to infinity. Numerical simulations indicate that the derived bounds are closer to the performance of low-density parity check (LDPC) coding schemes than outage probability, thus providing a better characterization with the concerned the rate.

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. While accurate channel estimation is essential for beamforming and data detection, the unique characteristics of near-field channels pose additional challenges to the effective acquisition of channel state information. In this paper, we propose a novel codebook design, which allows efficient near-field channel estimation with significantly reduced codebook size. Specifically, we consider the eigen-problem based on the near-field electromagnetic wave transmission model. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field codebook design, we further introduce a two-step channel estimation scheme. Simulation results demonstrate that the proposed codebook design not only achieves superior sparsification performance of near-field channels with a lower leakage effect, but also significantly improves the accuracy in compressive sensing channel estimation.

Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel. Nevertheless, the promised gain of HMIMO could not be fully unleashed without an efficient means to estimate the high-dimensional channel. Bayes-optimal estimators typically necessitate either a large volume of supervised training samples or a priori knowledge of the true channel distribution, which could hardly be available in practice due to the enormous system scale and the complicated EM environments. It is thus important to design a Bayes-optimal estimator for the HMIMO channels in arbitrary and unknown EM environments, free of any supervision or priors. This work proposes a self-supervised minimum mean-square-error (MMSE) channel estimation algorithm based on powerful machine learning tools, i.e., score matching and principal component analysis. The training stage requires only the pilot signals, without knowing the spatial correlation, the ground-truth channels, or the received signal-to-noise-ratio. Simulation results will show that, even being totally self-supervised, the proposed algorithm can still approach the performance of the oracle MMSE method with an extremely low complexity, making it a competitive candidate in practice.

To meet the stringent requirements of future communication systems, ultra-massive multiple-input and multiple-output (UM-MIMO) technology has garnered significant attention as a key enabling technology for 6G. However, the deployment of UM-MIMO introduces new challenges, particularly the near-field effect. In this paper, by leveraging the unique characteristics of near-field channels, we propose a novel near-field channel estimation algorithm based on the Newton's method. We also design a near-field codebook that meets the requirements for convergence guarantee. Our algorithm overcomes the limitations of existing approaches by offering a low-complexity, tuning-free, and convergence-guaranteed solution. Simulation results show that our proposed algorithm outperforms state-of-the-art baselines in terms of estimation accuracy, establishing its effectiveness in near-field channel estimation for UM-MIMO systems.

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial computational resources for processing. Consequently, DNN models are typically trained and deployed on resource-rich cloud servers. However, due to potential latency issues associated with cloud communications, deep learning (DL) workflows (e.g., DNN training and inference) are increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs). This shift is designed to support latency-sensitive applications and has given rise to a new paradigm of edge AI, which will play a critical role in upcoming sixth-generation (6G) networks to support ubiquitous AI applications. Despite its considerable potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL. Specifically, the acquisition of large-scale data, as well as the training and inference processes of DNNs, can rapidly deplete the battery energy of EUDs. This necessitates an energy-conscious approach to edge AI to ensure both optimal and sustainable performance. In this article, we present a contemporary survey on green edge AI. We commence by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edge AI. Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference. Finally, we underscore potential future research directions to further enhance the energy efficiency (EE) of edge AI.

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. We consider two cases with point and extended targets, in which each IRS aims to estimate the target direction-of-arrival (DoA) and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cramér-Rao bounds (CRBs) for parameter estimation under the two target models. Then, we assume that the BS sends combined information and dedicated sensing signals for ISAC, and accordingly consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals. Under this setup, we minimize the maximum CRB at all IRSs, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. To tackle the highly non-convex SINR-constrained max-CRB minimization problems, we propose efficient algorithms based on alternating optimization and semi-definite relaxation, to obtain converged solutions. Finally, numerical results are provided to verify the benefits of our proposed designs over various benchmark schemes based on separate or heuristic beamforming designs.

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to its superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering thelow complexityof the AMP algorithm andadaptabilityof GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to preprocess its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate.

Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its large array aperture and small wavelength, the near-field region of THz UM-MIMO is greatly enlarged. The high-dimensional channel of such systems thus consists of a stochastic mixture of far and near fields, which renders channel estimation extremely challenging. Previous works based on uni-field assumptions cannot capture the hybrid far- and near-field features, thus suffering significant performance loss. This motivates us to consider hybrid-field channel estimation. We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. Built upon classic orthogonal approximate message passing, we transform each iteration into a contractive mapping, comprising a closed-form linear estimator and a neural network based non-linear estimator. A major algorithmic innovation involves applying fixed point iteration to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions. Simulation results verify our theoretical analysis and show significant performance gains over state-of-the-art approaches in the estimation accuracy and convergence rate.

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devices due to significant advances in low-power electronics technology and wireless communications techniques. To realize the promised potentials, advanced resource allocation design plays a decisive role in revealing, understanding, and exploiting the intrinsic rate–energy tradeoff capitalizing on the dual use of radio frequency (RF) signals for wireless charging and communication. In this article, we provide a comprehensive tutorial overview of SWIPT from the perspective of resource allocation design. The fundamental concepts, system architectures, and RF energy harvesting (EH) models are introduced. In particular, three commonly adopted EH models, namely, the linear EH model, the nonlinear saturation EH model, and the nonlinear circuit-based EH model, are characterized and discussed. Then, for a typical wireless system setup, we establish a generalized resource allocation design framework that subsumes conventional resource allocation design problems as special cases. Subsequently, we elaborate on relevant tools from optimization theory and exploit them for solving representative resource allocation design problems for SWIPT systems with and without perfect channel state information (CSI) available at the transmitter, respectively. The associated technical challenges and insights are also highlighted. Furthermore, we discuss several promising and exciting future research directions for resource allocation design for SWIPT systems intertwined with cutting-edge communication technologies, such as intelligent reflecting surfaces, unmanned aerial vehicles, mobile edge computing, federated learning, and machine learning.

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter-wave (mm-wave) wireless communication. In this paper, we investigate channel estimation for IRS-assisted mm-wave multiple-input multiple-output (MIMO) wireless systems. By leveraging the sparsity of mm-wave channels in the angular domain, we formulate the channel estimation problem as an$\ell _{1}$-norm regularized optimization problem with fixed-rank constraints. To tackle the non-convexity of the formulated problem, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. To further reduce the computational complexity of the estimation algorithm, we propose a compressive sensing- (CS-) based channel estimation approach. In particular, a three-stage estimation protocol is put forward where the subproblem in each stage can be solved via low-complexity CS methods. Furthermore, based on the acquired channel state information (CSI) of the cascaded channel, we design a passive beamforming algorithm for maximization of the spectral efficiency. Simulation results reveal that the proposed MO-based estimation (MO-EST) and beamforming algorithms significantly outperform two benchmark schemes while the CS-based estimation (CS-EST) algorithm strikes a balance between performance and complexity.

A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-output communications. For the VPS architecture, a hybrid precoding design (HPD) scheme, called VPS-HPD, is proposed to optimize the phases according to the channel state information by alternately optimizing the analog precoder and digital precoder. To reduce the computational complexity of the VPS-HPD scheme, a low-complexity HPD scheme for the VPS architecture (VPS-LC-HPD) including alternating optimization in three stages is then proposed, where each stage has a closed-form solution and can be efficiently implemented. To reduce the hardware complexity introduced by the large number of switches, we consider a group-connected VPS architecture and propose a HPD scheme, where the HPD problem is divided into multiple independent subproblems with each subproblem flexibly solved by the VPS-HPD or VPS-LC-HPD scheme. Simulation results verify the effectiveness of the propose schemes and show that the proposed schemes can achieve satisfactory spectral efficiency performance with reduced computational complexity or hardware complexity.

In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-of-service requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physics-based model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promised by large IRS-assisted communication systems in practice.

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO. The detector is composed of two modules, a nonlinear module at the central processing unit (CPU) and a linear module at the access point (AP). The turbo principle in iterative decoding is utilized to compute and pass the extrinsic information between modules. An analytical framework is then provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Simulation results will show that the proposed method outperforms the distributed detectors in terms of bit-error rate.

To reduce the number of phase shifters for analog precoding in millimeter wave massive multiple-input multiple-output communications, we investigate the hybrid use of expensive phase shifters and low-cost switches. Different from the existing fixed phase shifter (FPS) architecture where the phases are fixed and independent of the channel state information, we consider variable phase shifter (VPS) whose phases are variable and subject to the hardware constraint. Based on the VPS architecture, a hybrid precoding design (HPD) scheme named VPS-HPD is proposed to optimize the phases according to the channel state information. Specifically, we alternately optimize the analog precoder and the digital precoder, where the former is converted into several subproblems and each subproblem further includes the alternating optimization of the phase matrix and switch matrix. Simulation results show that the spectral efficiency of the VPS-HPD scheme is very close to that of the fully digital precoding, higher than that of the existing MO-AltMin scheme for the fully-connected architecture with much fewer phase shifters, and substantially higher than that of the existing FPS-AltMin scheme for the FPS architecture with the same number of phase shifters.

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it difficult to achieve a global system optimality. In this paper, we propose a deep learning-based approach that directly optimizes the beamformers at the base station according to the received uplink pilots, thereby, bypassing the explicit channel estimation. Different from the existing fully data-driven approach where all the modules are replaced by deep neural networks (DNNs), a neural calibration method is proposed to improve the scalability of the end-to-end design. In particular, the backbone of conventional time-efficient algorithms, i.e., the least-squares (LS) channel estimator and the zero-forcing (ZF) beamformer, is preserved and DNNs are leveraged to calibrate their inputs for better performance. The permutation equivariance property of the formulated resource allocation problem is then identified to design a low-complexity neural network architecture. Simulation results will show the superiority of the proposed neural calibration method over benchmark schemes in terms of both the spectral efficiency and scalability in large-scale wireless networks.

In this paper, we investigate resource allocation algorithm design for large intelligent reflecting surface (IRS)assisted simultaneous wireless information and power transfer (SWIPT) systems. To this end, we adopt a physics-based IRS model that, unlike the conventional IRS model, takes into account the impact of the incident and reflection angles of the impinging electromagnetic wave on the reflected signal. To facilitate efficient resource allocation design for large IRSs, we employ a scalable optimization framework, where the IRS is partitioned into several tiles and the phase shift elements of each tile are jointly designed to realize different transmission modes. Then, the beamforming vectors at the base station (BS) and the transmission mode selection of the tiles of the IRS are jointly optimized for minimization of the BS transmit power taking into account the quality-of-service requirements of both non-linear energy harvesting receivers and information decoding receivers. For handling the resulting non-convex optimization problem, we apply a penalty-based method, successive convex approximation, and semidefinite relaxation to develop a computationally efficient algorithm which asymptotically converges to a locally optimal solution of the considered problem. Our simulation results show that the proposed scheme enables considerable power savings compared to two baseline schemes. Moreover, our results also illustrate that the advocated physics-based model and scalable optimization framework for large IRSs allows us to strike a balance between system performance and computational complexity, which is vital for realizing large IRS-assisted communication systems.

In this paper, we investigate resource allocation for IRS-assisted green multiuser multiple-input single-output (MISO) systems. To minimize the total transmit power, both the beamforming vectors at the access point (AP) and the phase shifts at multiple IRSs are jointly optimized, while taking into account the minimum required quality-of-service (QoS) of multiple users. First, two novel algorithms, namely a penalty-based alternating minimization (AltMin) algorithm and an inner approximation (IA) algorithm, are developed to tackle the non-convexity of the formulated optimization problem when perfect channel state information (CSI) is available. Existing designs employ semidefinite relaxation in AltMin-based algorithms, which, however, cannot ensure convergence. In contrast, the proposed penalty-based AltMin and IA algorithms are guaranteed to converge to a stationary point and a Karush-Kuhn-Tucker (KKT) solution of the design problem, respectively. Second, the impact of imperfect knowledge of the CSI of the channels between the AP and the users is investigated. To this end, a non-convex robust optimization problem is formulated and the penalty-based AltMin algorithm is extended to obtain a stationary solution. Simulation results reveal a key trade-off between the speed of convergence and the achievable total transmit power for the two proposed algorithms. In addition, we show that the proposed algorithms can significantly reduce the total transmit power at the AP compared to various baseline schemes and that the optimal numbers of transmit antennas and IRS reflecting elements, which maximize the system energy efficiency of the considered system, are finite.

Intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimeter wave (mmWave) wireless communication, due to their ability to create favorable line-of-sight (LoS) propagation environments. In this paper, we investigate channel estimation in downlink IRS-assisted mmWave multiple-input multiple-output (MIMO) systems. By leveraging the sparsity of mmWave channels, we formulate the channel estimation problem as a fixed-rank constrained non-convex optimization problem. To tackle the non-convexity, an efficient algorithm is proposed by capitalizing on alternating minimization and manifold optimization (MO), which yields a locally optimal solution. Simulation results show that the proposed MObased estimation (MO-EST) algorithm significantly outperforms two benchmark schemes and demonstrate the robustness of the MO-EST algorithm with respect to imperfect knowledge of the sparsity level of the channels in practical implementations.

Intelligent reflecting surfaces (IRSs) are regarded as key enablers of next-generation wireless communications, due to their capability of customizing the wireless propagation environment. In this paper, we investigate power-efficient resource allocation for IRS-assisted multiuser multiple-input single-output (MISO) systems. To minimize the transmit power, both the beamforming vectors at the access point (AP) and phase shifts at the IRS are jointly optimized while taking into account the minimum required quality-of-service (QoS) of the users. To tackle the non-convexity of the formulated optimization problem, an inner approximation (IA) algorithm is developed. Unlike existing designs, which cannot guarantee local optimality, the proposed algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Our simulation results show the effectiveness of the proposed algorithm compared to baseline schemes and reveal that deploying IRSs is more promising than leveraging multiple antennas at the AP in terms of energy efficiency.

In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate users, which do not have line-of-sight communication links, in the presence of multiple multi-antenna potential eavesdroppers whose channel state information (CSI) is not perfectly known. Artificial noise (AN) is transmitted from the AP to deliberately impair the eavesdropping channels for security provisioning. We investigate the joint design of the beamformers and AN covariance matrix at the AP and the phase shifters at the IRSs for maximization of the system sum-rate while limiting the maximum information leakage to the potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the impact of the imperfect CSI of the eavesdropping channels. To address the non-convexity of the optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization, a penalty-based approach, successive convex approximation, and semidefinite relaxation. Simulation results show that IRSs can significantly improve the system secrecy performance compared to conventional architectures without IRS. Furthermore, our results unveil that, for physical layer security, uniformly distributing the reflecting elements among multiple IRSs is preferable over deploying them at a single IRS.

In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total spectral efficiency of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system spectral efficiency for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

In this paper, we propose to utilize intelligent reflecting surfaces (IRSs) for enhancing the physical layer security of wireless communications systems. In particular, an IRS-assisted secure wireless system is considered, where a multi-antenna transmitter communicates with a single-antenna receiver in the presence of an eavesdropper. To maximize the secrecy rate, both the beamformer at the transmitter and the IRS phase shifts are jointly optimized. Based on the block coordinate descent (BCD) and minorization maximization (MM) techniques, two efficient algorithms are developed to solve the resulting non-convex optimization problem for small- and large-scale IRSs, respectively. Simulation results show that IRSs can significantly improve physical layer security if the proposed algorithms are employed. Furthermore, we reveal that deploying large-scale IRSs is more efficient than enlarging the antenna array size of the transmitter for both boosting the secrecy rate and enhancing the energy efficiency.

Densifying networks and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, the complicated distributions of the signal power and the accumulated interference power, largely induced by various space-time processing techniques, make it highly challenging to quantitatively characterize the performance of multi-antenna networks. In this paper, using tools from stochastic geometry, a unified framework is developed for the analysis of such networks. The major results are two innovative representations of the coverage probability, which make the analysis of multi-antenna networks almost as tractable as the single-antenna case. One is expressed as an ℓ1-induced norm of a Toeplitz matrix, and the other is given in a finite sum form. With a compact representation, the former incorporates many existing analytical results on single- and multi-antenna networks as special cases and leads to tractable expressions for evaluating the coverage probability in both ad hoc and cellular networks. While the latter is more complicated for numerical evaluation, it helps analytically gain key design insights. In particular, it helps prove that the coverage probability of ad hoc networks is a monotonically decreasing convex function of the transmitter density and that there exists a peak value of the coverage improvement when increasing the number of transmit antennas. On the other hand, in multi-antenna cellular networks, it is shown that the coverage probability is independent of the transmitter density and that the outage probability decreases exponentially as the number of transmit antennas increases.

Hybrid precoding has been recently proposed as a cost- effective transceiver solution for millimeter wave (mm- wave) systems. The analog component in such precoders, which is composed of a phase shifter network, is the key differentiating element in contrast to conventional fully digital precoders. While a large number of phase shifters with unquantized phases are commonly assumed in existing works, in practice the phase shifters should be discretized with a coarse quantization, and their number should be reduced to a minimum due to cost and power consideration. In this paper, we propose a new hybrid precoder implementation using a small number of phase shifters with quantized and fixed phases, i.e., a fixed phase shifter (FPS) implementation, which significantly reduces the cost and hardware complexity. In addition, a dynamic switch network is proposed to enhance the spectral efficiency. Based on the proposed FPS implementation, an effective alternating minimization (AltMin) algorithm is developed with closed-form solutions in each iteration. Simulation results show that the proposed algorithm with the FPS implementation outperforms existing ones. More importantly, it needs much fewer phase shifters than existing hybrid precoder proposals, e.g., around 10 fixed phase shifters are sufficient for practically relevant system settings.

Densifying the network and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, due to the complicated distributions of random signal and interference channel gains, largely induced by various space-time processing techniques, it is highly challenging to quantitatively characterize the performance of dense multi-antenna networks. In this paper, using tools from stochastic geometry, a tractable framework is proposed for the analytical evaluation of such networks. The major result is an innovative representation of the coverage probability, as an induced ℓ1-norm of a Toeplitz matrix. This compact representation incorporates lots of existing analytical results on single-and multi-antenna networks as special cases, and its evaluation is almost as simple as the single-antenna case with Rayleigh fading. To illustrate its effectiveness, we apply the proposed framework to investigate two kinds of prevalent dense wireless networks, i.e., physical layer security aware networks and millimeter-wave networks. In both examples, in addition to tractable analytical results of relevant performance metrics, insightful design guidelines are also analytically obtained.

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of the defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.

Millimeter wave (mmWave) communications has been considered as a promising technology for 5G cellular networks. Exploiting directional beamforming using antenna arrays to combat path loss is one of the defining features in mmWave cellular networks. However, previous works on mmWave network analysis usually adopt simplified antenna patterns for tractability. In this paper, we show that there are huge discrepancies between the simplified and actual antenna patterns when investigating the coverage probability of mmWave networks. Analytical expressions for the coverage probabilities are derived using tools from stochastic geometry, by considering the actual antenna pattern with the uniform linear array. Moreover, the impact of the array size is investigated, which cannot be revealed from existing results with simplified antenna patterns. Numerical results will show that large-scale antenna arrays are required for satisfactory coverage in mmWave cellular networks. Furthermore, dense mmWave cellular networks are shown to achieve much higher rate coverage than conventional sub-6 GHz cellular systems.

Millimeter wave (mmWave) communications holds a promise to offer an unprecedented capacity boost for 5G cellular networks. Due to the small wavelength of mmWave signals, mmWave multiple-input-multiple-output (MIMO) systems can leverage large-scale antennas to combat the path loss and rain attenuation via precoding. Different from conventional MIMO systems, mmWave MIMO cannot realize precoding entirely at baseband using digital precoders, as a result of potentially high power consumed by signal mixers and analog-to-digital converters (ADCs). As a cost- effective alternative, a hybrid precoding transceiver architecture for mmWave MIMO systems has received considerable attention. However, the optimal design of such hybrid precoding has not been fully understood. In this paper, an alternating minimization algorithm based on manifold optimization is proposed to design the hybrid precoder, thereby making it comparable in performance to the digital precoder. Numerical results show that our proposed algorithm can significantly outperform existing ones in terms of spectral efficiency and, more importantly, it can achieve the optimal performance in certain cases. Finally, the alternating minimization approach is shown to be generally applicable to precoding design with different hybrid structures, and the corresponding comparison will show interesting design insights for hybrid precoding.