Using environmental sensory data can enhance communications beam training and reduce its overhead compared to conventional methods. However, the availability of fresh sensory data during inference may be limited due to sensing constraints or sensor failures, necessitating a realistic model for multimodal sensing. This paper proposes a joint multimodal sensing and beam prediction framework that operates under a constraint on the average sensing rate, i.e., how often fresh sensory data should be obtained. The proposed method combines deep reinforcement learning, i.e., a deep Q-network (DQN), with a neural network (NN)-based beam predictor. The DQN determines the sensing decisions, while the NN predicts the best beam from the codebook. To capture the effect of limited fresh data during inference, the age of information (AoI) is incorporated into the training of both the DQN and the beam predictor. Lyapunov optimization is employed to design a reward function that enforces the average sensing constraint. Simulation results on a real-world dataset show that AoI-aware training improves top-1 and top-3 inference accuracy by 44.16% and 52.96%, respectively, under a strict sensing constraint. The performance gain, however, diminishes as the sensing constraint is relaxed.

The massive surge in device connectivity demands higher data rates, increased capacity with low latency and high throughput. Hence, to provide ultra-reliable, low-latency communication with ubiquitous connectivity for Internet-of-Things (IoT) devices, next-generation wireless communication leverages the incorporation of machine learning tools. However, standard data-driven models often need large datasets and lack interpretability. To overcome this, model-driven deep learning (DL) approaches combine domain knowledge with learning to improve accuracy and efficiency. Deep unfolding is a model-driven method that turns iterative algorithms into deep neural network (DNN) layers. It keeps the structure of traditional algorithms while allowing end-to-end learning. This makes deep unfolding both interpretable and effective for solving complex signal processing problems in wireless systems. We first present a brief overview of the general architecture of deep unfolding to provide a solid foundation. We also provide an example to outline the steps involved in unfolding a conventional iterative algorithm. We then explore the application of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, integrated sensing and communication, power allocation, and physical layer security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios.

This paper explores the energy efficiency (EE) of integrated sensing and communication (ISAC) systems employing massive multiple-input multiple-output (mMIMO) techniques to leverage spatial beamforming gains for both communication and sensing. We focus on an mMIMO-ISAC system operating in an orthogonal frequency-division multiplexing setting with a uniform planar array, zero-forcing downlink transmission, and mono-static radar sensing to exploit multi-carrier channel diversity. By deriving closed-form expressions for the achievable communication rate and Cramér-Rao bounds (CRBs), we are able to determine the overall EE in closed-form. A power allocation problem is then formulated to maximize the system’s EE by balancing communication and sensing efficiency while satisfying communication rate requirements and CRB constraints. Through a detailed analysis of CRB properties, we reformulate the problem into a more manageable form and leverage Dinkelbach’s and successive convex approximation (SCA) techniques to develop an efficient iterative algorithm. A novel initialization strategy is also proposed to ensure high-quality feasible starting points for the iterative optimization process. Extensive simulations demonstrate the significant performance improvement of the proposed approach over baseline approaches. Results further reveal that as communication spectral efficiency rises, the influence of sensing EE on the overall system EE becomes more pronounced, even in sensing-dominated scenarios. Specifically, in the high ω regime of 2 × 10−3, we observe a 16.7% reduction in overall EE when spectral efficiency increases from 4 to 8 bps/Hz, despite the system being sensing-dominated.

Mega-constellations of low Earth orbit (LEO) satellites are envisioned to deliver global broadband and direct-to-cell services, requiring seamless handovers (HOs) to ensure uninterrupted connectivity. Conventional break-before-make HO protocols, although supported by inter-satellite links, suffer from beamforming delays due to the high orbital velocities of LEO satellites. To address these limitations, we propose an integrated sensing and communication (ISAC)-assisted HO (ISAC-HO) protocol that enables make-before-break HOs via ISAC-enabled ground terminals (GTs). A novel ISAC design capable of generating a three-dimensional (3D) beampattern under realistic conditions allows GTs to sense approaching LEO satellites without significantly compromising communication with the currently serving LEO satellite. The design problem is highly challenging due to its non-convexity and mixed-integer nature. To tackle these challenges, we propose an approach that leverages Riemannian manifold optimization and closed-form solutions. For multi-GT scenarios, we introduce a multi-agent deep reinforcement learning framework that mitigates sensing collisions and ensures quality-of-service under shared spectrum constraints. Numerical results confirm that the proposed ISAC design significantly improves the communication–sensing trade-off, enables smooth HO, and remains robust in the presence of imperfect channel state information.

This work provides a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication system in the presence of phase errors. We first derive the exact expressions for the first and second moments of the maximum signal-to-noise ratio at the receiver. We then use these moments to obtain highly accurate and insightful expressions for the outage probability, average symbol error probability (SEP), and ergodic rate (ER) for various phase error distributions, leveraging the moment-matching approach. Furthermore, we present closed-form asymptotic expressions for these performance metrics. The derived expressions are then leveraged to gain important insights into the impact of phase errors on the scaling laws of diversity order, coding gain, and ER. The numerical results show that the presence of phase errors can significantly degrade the system’s diversity order and coding gain. Specifically, we show that, in the absence of phase errors, the diversity order scales linearly with the number of RIS elements, while the diversity order reduces to unity in the presence of uniformly distributed phase errors. Finally, the analytical findings are demonstrated through extensive simulation results.

Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: i) maximizing system sum spectral efficiency and ii) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

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

While Cramér-Rao lower bound is an important metric in sensing functions in integrated sensing and communications (ISAC) designs, its optimization usually involves a computationally expensive solution such as semidefinite relaxation. In this paper, we aim to develop a low-complexity yet efficient algorithm for CRLB optimization. We focus on a beamforming design that maximizes the weighted sum between the communications sum rate and the sensing CRLB, subject to a transmit power constraint. Given the non-convexity of this problem, we propose a novel method that combines successive convex approximation (SCA) with a shifted generalized power iteration (SGPI) approach, termed SCA-SGPI. The SCA technique is utilized to approximate the non-convex objective function with convex surrogates, while the SGPI efficiently solves the resulting quadratic subproblems. Simulation results demonstrate that the proposed SCA-SGPI algorithm not only achieves superior tradeoff performance compared to existing method but also significantly reduces computational time, making it a promising solution for practical ISAC applications.

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

This work studies joint waveform design and subcarrier allocation in a multiuser multicarrier monostatic joint communications and sensing (JCAS) system. We aim to design the JCAS transmit waveform with subcarrier allocation that maximizes the energy efficiency (EE) of the multiuser communications subject to the constraints on the minimum communications and sensing signal-to-interference-plus-noise ratios (SINRs) and transmit power budget. The formulated EE design problem is a mixed-integer non-convex fractional program, which is highly challenging to solve directly. To overcome the challenges, we leverage alternating optimization (AO) and divide the original problem into two subproblems, namely, the waveform design and subcarrier allocation. For the non-convex fractional objective function in the waveform design, we propose an efficient method combining two typical techniques in fractional programming, namely, the quadratic and Dinkelbach transforms, along with successive convex approximation (SCA). For the subcarrier allocation, we leverage quadratic transform and penalty function method. Numerical results demonstrate the effectiveness of the proposed method, showing a significant improvement in energy efficiency compared to a baseline scheme.

Low-resolution analog-to-digital converters (ADCs) and hybrid beamforming have emerged as efficient solutions to reduce power consumption with satisfactory spectral efficiency (SE) in massive multiple-input multiple-output (MIMO) systems. In this paper, we investigate the performance of a hybrid receiver in massive MIMO orthogonal frequency-division multiplexing (OFDM) uplink systems with low-resolution ADCs and over-sampling. Considering both the temporal and spatial correlation of the quantization distortion (QD), we derive a closed-form approximation of the frequency-domain QD covariance matrix, which facilitates the evaluation of the system's SE. Then we jointly design the analog and digital combiners of the hybrid receiver to maximize the SE. The formulated problem is challenging due to the constant-modulus constraint of the analog combiner and its coupling with the digital one. To overcome these challenges, we transform the objective function into an equivalent but more tractable form and then iteratively update the analog and digital combiners. Numerical simulations verify the superiority of the proposed algorithm over the considered benchmarks and show the resilience of the hybrid receiver to beam squint with low-resolution ADCs. Furthermore, the proposed hybrid receiver design with oversampling can achieve significantly higher energy efficiency compared with the fully digital one.

In integrated sensing and communication (ISAC) systems, reconfigurable intelligent surface (RIS) is often able to provide a virtual line-of-sight path to overcome blockage and introduce new degrees of freedom to enhance both communications and sensing performances. In this paper, we explore the synergistic integration of massive multiple-input-multiple-output (MIMO) ISAC and RIS technologies. We first consider the uplink training phase and estimate the channels between the base station and communication users, by using the minimum mean square error method. Then, we derive the closed-form expressions for the downlink communications rate and the Cramér-Rao lower bound for the estimation of the azimuth and elevation angles associated with the sensing target. Extensive simulations verify that the derived analytical results match well with Monte Carlo simulation results. Furthermore, communication and sensing performances are significantly improved owing to the deployment of the RIS. Our analysis lay an important foundation for optimizing RIS-assisted ISAC systems.

By combining the benefits of passive reflecting and active relay, the hybrid active-passive reconfigurable intelligent surfaces (RIS) (HRIS) architecture can overcome the double path loss and limited performance gains of the conventional passive RIS. In this work, we investigate the secrecy performance of a millimeter wave multiple-input-multiple-output (MIMO) network in the presence of a multiantenna eavesdropper with imperfect channel state information (CSI). To maximize the secrecy rate, we propose a joint design of the transmit beamformer and HRIS coefficients utilizing the combined tools of block coordinate ascent (BCA), stationary solution to Rayleigh quotient, Dinkelbach method, and concave-convex procedure (CCCP). The design problem encompasses both fixed and dynamic HRIS architectures. For the former, a pre-manufactured set of active elements is considered. For the latter, the set of active elements is adaptively updated, and their coefficients are optimized via a stochastic game-based algorithm. Simulation results demonstrate that the proposed HRIS schemes outperform the conventional passive RIS, particularly when high transmit power and many active coefficients are deployed at the HRIS. Additionally, a dynamic HRIS can offer 60% improvement in the secrecy rate compared to the passive RIS scheme.

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first present the fundamental properties of optimal quantization and leverage them to derive a more accurate approximation of the covariance matrix of the quantization distortion. This theoretical finding facilitates the analysis of the system’s SE in the presence of low-resolution ADCs. Then, considering resolution-adaptive ADCs, we focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Subsequently, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a 64 × 64 MIMO system demonstrate that the proposed design offers 6% improvements in both SE and EE with 40% fewer active ADC bits compared with uniform bit allocation. Furthermore, it is unveiled that receiving more data streams with low-resolution ADCs can lead to higher SE and EE compared with receiving fewer data streams with high-resolution ADCs.

Unmanned aerial vehicle (UAV) and active reconfigurable intelligent surface (ARIS) can enhance wireless communications channels through flexible location deployment and intelligent signal reflection. On the other hand, rate-splitting multiple access (RSMA) is an efficient framework for non-orthogonal transmission and multiple access. In this paper, we explore the synergistic advantages of integrating these technologies within a unified UAV-mounted ARIS-assisted communications system employing RSMA. Specifically, we analyze the system outage probability and sum capacity considering independent and non-identically distributed Nakagami-m channels, circularly uniformly distributed random RIS phase shifts, and imperfect successive interference cancellation. We first derive the exact cumulative distribution and probability density functions of the sum single and double Nakagami-m vectors via multivariate Fox’s H-function. These allow us to derive the closed-form expressions for the outage probability, asymptotic outage probability, and sum ergodic capacity of the system. The analytical findings are then verified via extensive numerical simulations, which highlight the superior performance of UAV-ARIS RSMA compared to conventional UAV-based systems.

Joint communications and sensing (JCAS) have recently emerged as a promising technology to utilize the scarce spectrum in wireless networks and to reuse the same hardware to save infrastructure costs. In practical JCAS systems, dual functional constant-modulus waveforms can avoid signal distortion in nonlinear power amplifiers. However, the designs of such wave-forms are highly complex due to the nonconvex constant-modulus constraint and high-dimensional variables, especially in wideband multicarrier systems. In this paper, we propose an efficient deep unfolding-based waveform design in a wideband large multiple-input multiple-output (MIMO) JCAS system. The deep unfolding model has a sparsely-connected structure and is trained in an unsupervised fashion. Simulation results show that the proposed deep unfolding design achieves better communications-sensing performance tradeoff while performing 98.5% faster than the conventional branch-and-bound method.

To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold:$i$) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units;$ii$) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and$iii$) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

In this paper, we aim to enable an intelligent traffic (TS) steering application in the open radio access network (O-RAN) by jointly optimizing the flow-split distribution, congestion control and scheduling (i.e. so-called JFCS). To do so, we develop a multi-layer optimization framework based on network utility maximization and stochastic optimization methods. The proposed algorithm provides fast convergence, long-term utility-optimality and significantly low latency compared to state-of-the-art RAN approaches. In particular, our main contributions are as follows: i) we propose the novel JFCS framework to efficiently and adaptively route traffic to indented users in appropriate radio units, and ii) we develop low-complexity algorithms to effectively solve the JFCS problem in different time scales, enabling a closed-loop control of the TS in the O-RAN context. The insights presented in this work will pave the way for 0- RAN that are completely automated, offering improved control and flexibility.

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

We investigate the beam squint effect in uniform planar arrays (UPAs) and propose an efficient hybrid beam-forming (HBF) design to mitigate the beam squint in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems operating at terahertz band. We first analyze the array gain and derive the closed-form beam squint ratio that characterizes the severity of the beam squint effect on UPAs. The effect is shown to be more severe with a higher fractional bandwidth, while it can be significantly mitigated when the shape of a UPA approaches a square. We then focus on the HBF design that maximizes the system spectral efficiency. The design problem is challenging due to the frequency-flat nature and hardware constraints of the analog beamformer. We over-come the challenges by proposing an efficient decoupling design in which the digital and analog beamformers admit closed-form solutions, which facilitate practical implementations. Numerical results validate our analysis and show that the proposed HBF design is robust to beam squint, and thus, it outperforms the state-of-the-art methods in wideband massive MIMO systems.

A cell-free (CF) massive multiple-input-multiple-output (mMIMO) system can provide uniform spectral efficiency (SE) with simple signal processing. On the other hand, a recently introduced technology called hybrid relay-reflecting intelligent surface (HR-RIS) can customize the physical propagation environment by simultaneously reflecting and amplifying radio waves in preferred directions. Thus, it is natural that incorporating HR-RIS into CF mMIMO can be a symbiotic convergence of these two technologies for future wireless communications. This motivates us to consider an HR-RIS-aided CF mMIMO system to utilize their combined benefits. We first model the uplink/downlink channels and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis of SE performance of the considered system. Specifically, we derive closed-form expressions for the uplink and downlink SE. The results reveal important observations on the performance gains achieved by HR-RISs compared to conventional systems. The presented analytical results are also valid for conventional CF mMIMO systems and those aided by passive reconfigurable intelligent surfaces. Such results play an important role in designing new transmission strategies and optimizing HR-RIS-aided CF mMIMO systems. Finally, we provide extensive numerical results to verify the analytical derivations and the effectiveness of the proposed system design under various settings.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

We consider in this work a cell-free (CF) massive multiple-input-multiple-output (mMIMO) system where multiple hybrid relay-reflecting intelligent surfaces (HR-RIS) are deployed to assist communication between access points and users. We first present the signal model and derive the minimum-mean-square-error estimate of the effective channels. We then present a comprehensive analysis for the considered HR-RIS-aided CF mMIMO system, where the closed-form expression of the downlink throughput is derived. The presented analytical results are also valid for conventional CF mMIMO systems, i.e., CF mMIMO systems with and without passive reconfigurable intelligent surfaces. Finally, the analytical derivations are verified by extensive numerical results.

The hybrid relay-reflecting intelligent surface (HRRIS) has been recently introduced as an efficient solution to overcome the double path loss and limited beamforming diversity of the conventional fully passive reflecting surface. This motivates us to investigate the application of the HR-RIS in improving the secrecy capacity of millimeter wave multiple-input-multiple-output (MIMO) systems with the presence of multi-antenna eavesdropper. The joint optimization of the transmit beamformer and the relay-reflecting coefficients at RIS is tackled via alternating optimization. In the proposed solution, a closed-form expression for the optimal transmit beamformer at the transmitter is derived, and a metaheuristic solution based on particle swarm optimization is proposed to optimize the active and passive elements at the HR-RIS. The simulation results verify that under various scenarios, the HR-RIS provides significant improvement in the secrecy capacity with respect to the conventional passive reflecting surface.

With massive deployment, multiple-input–multiple-output (MIMO) systems continue to take mobile communications to new heights, but the ever-increasing demands mean that there is a need to look beyond MIMO and pursue the next disruptive wireless technologies. Reconfigurable intelligent surface (RIS) is widely considered a key candidate technology block to provide the next generational leap. The first part of this article provides an updated overview of the conventional reflection-based RIS technology, which complements the existing literature to include active and semiactive RIS, and the synergies with cell-free massive MIMO (CF mMIMO). Then, we widen the scope to discuss the surface-wave-assisted RIS that represents a different design dimension in utilizing metasurface technologies. This goes beyond being a passive reflector and can use the surface as an intelligent propagation medium for superb radio propagation efficiency. The third part of this article turns the attention to the fluid antenna, a novel antenna technology that enables a diverse form of reconfigurability that can combine with RIS for ultrahigh capacity, power efficiency, and scalability. This article concludes with a discussion of the potential synergies that can be exploited between MIMO, RIS, and fluid antennas.

This paper presents a deep learning-based method to automatically recognize low probability of intercept (LPI) radar waveforms against diversified jamming attacks. Concretely, an efficient convolutional neural network (CNN) architecture, namely Densely-Accumulated Network (DANet), is introduced to learn the time-frequency representation transformed by the Wigner-Ville distribution. Such an architecture has several novel densely-accumulated connection modules specified by various symmetric and asymmetric convolutional layers to enrich diversified features at multiple representational maps. Besides, the skip-connection and dense-connection are leveraged to improve feature learning efficiency and prevent the vanishing gradient when the network goes deeper. Some image processing techniques (e.g., global thresholding and digital filtering) are adopted to enhance the quality of time-frequency image. Relying on simulations, we benchmark the proposed method on a synthetic 13-waveform dataset and also investigate the influence of hyper-parameters (such as image size, number of modules, training data size) on the overall recognition performance. Remarkably, with average accuracy of 98.2% at 0 dB signal-to-noise ratio (SNR), DANet outperforms several backbone CNNs and state-of-the-art networks of LPI waveform recognition while keeping a cost-efficient model.

Hybrid beamforming is considered a key enabler to realize millimeter wave (mmWave) multiple-input multiple-output (MIMO) communications due to its capability of considerably reducing the number of costly and power-hungry radio frequency chains in the transceiver. However, in mmWave MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems, hybrid beamforming design is challenging because the analog precoder and combiner are required to be shared across the whole employed bandwidth. In this paper, we propose closed-form solutions to the problem of designing the analog precoder/combiner in a mmWave MIMO-OFDM system by maximizing the upper bound of the spectral efficiency. The closed-form solutions facilitate the design of analog beamformers while guaranteeing state-of-art performance. Numerical results show that the proposed algorithm attains a slightly improved performance with much lower computational complexity compared to the considered benchmarks.

Although the sphere decoder (SD) is a powerful detector for multiple-input multiple-output (MIMO) systems, it has become computationally prohibitive in massive MIMO systems, where a large number of antennas are employed. To overcome this challenge, we propose fast deep learning (DL)-aided SD (FDL-SD) and fast DL-aided$K$-best SD (KSD, FDL-KSD) algorithms. Therein, the major application of DL is to generate a highly reliable initial candidate to accelerate the search in SD and KSD in conjunction with candidate/layer ordering and early rejection. Compared to existing DL-aided SD schemes, our proposed schemes are more advantageous in both offline training and online application phases. Specifically, unlike existing DL-aided SD schemes, they do not require performing the conventional SD in the training phase. For a$24 \times 24$MIMO system with QPSK, the proposed FDL-SD achieves a complexity reduction of more than 90% without any performance loss compared to conventional SD schemes. For a$32 \times 32$MIMO system with QPSK, the proposed FDL-KSD only requires$K = 32$to attain the performance of the conventional KSD with$K=256$, where$K$is the number of survival paths in KSD. This implies a dramatic improvement in the performance–complexity tradeoff of the proposed FDL-KSD scheme.

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.

In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network (DNN) architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net. Furthermore, in this algorithm, an adaptive early termination (ET) algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a 32 × 32 MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance.