Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.

This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five other sensor modalities, namely LiDAR, RGB and depth camera, inertial measurement unit (IMU) and radar, all sampled at 100 Hz. It contains approximately 160,000 frames collected across four virtual towns, sixteen communication scenarios, and three weather conditions. This paper provides a comprehensive overview of the dataset, outlining its key features, overall framework, and technical implementation details. In addition, it explores potential research applications concerning communication and collaborative perception, exemplified by beam prediction using a multimodal large language model. The dataset is open in https://le-liang.github.io/mmw/.

Integrated sensing and communications (ISAC) is a key enabler for 6G applications such as drone surveillance, urban air mobility, and low-altitude logistics. However, the hybrid ISAC signal, which comprises deterministic pilots and random data payloads, poses challenges for target detection, since 1) these components jointly affect both the mean and variance of the received signal, and 2) the random data payloads are typically unknown to the sensing receiver in bistatic systems. To address these, we develop a generalized likelihood ratio test (GLRT)-based detector that exploits the known pilots and the statistical properties of the unknown payloads. Given the exact performance is analytically intractable, an asymptotic analysis of the false alarm probability is conducted. Simulation results validate the theoretical derivations and demonstrate the superiority of the proposed detector, which highlights the importance of tailored ISAC detection that fully leverages data payload resources.

Massive multiple-input multiple-output (MIMO) is a key enabler for the high data rates required by the sixth-generation networks, yet its performance hinges on effective beam management with low training overhead. This paper proposes an interpretable framework to tackle beam alignment in mixed line-of-sight (LoS) and non-line-of-sight (NLoS) propagation environments. Our approach utilizes multimodal data to construct virtual base stations (VBSs), which are geometrically defined as mirror images of the base station across reflecting surfaces reconstructed from 3D LiDAR points. These VBSs provide a sparse and spatial representation of the dominant features of the wireless environment. Based on the constructed VBSs, we develop a VBS-assisted beam alignment scheme comprising coarse channel reconstruction followed by partial beam training. Numerical results demonstrate that the proposed method achieves near-optimal performance in terms of spectral efficiency.

Industrial time series often display complex, non-stationary behaviors with trends, periodicity, and abrupt fluctuations. Generating high-quality synthetic data in such domains is essential for simulation, forecasting, and anomaly detection in Industrial Internet of Things (IIoT) applications. However, distributional heterogeneity, sparse failure patterns, and long-term dependencies make this task highly challenging. We introduce HNRF-TS, a rectified flow framework with hybrid noise initialization, designed for scalable and robust time series generation. The hybrid prior combines isotropic Gaussian noise with structured codes from a lightweight generative adversarial network (GAN), yielding semantically aligned and diverse latent representations. To improve sampling efficiency, we propose a bimodal adaptive strategy that allocates denser ordinary differential equation (ODE) steps at the beginning and end of the trajectory while using coarser steps in smoother middle regions. This preserves critical temporal features while lowering computational cost. We further enhance fidelity with modules dedicated to modeling trends and seasonality, which capture global drifts and periodic signals inherent in industrial data. Across multiple IIoT datasets, HNRF-TS outperforms state-of-the-art baselines, including GAN-based and diffusion-based methods. It achieves up to 75.8% reduction in Context-FID and over 60% improvement in correlation metrics on long-horizon tasks. Moreover, high-quality samples can be generated with as few as 20 sampling steps, offering significant efficiency gains without sacrificing accuracy.

This paper investigates latency-constrained resource synergization for mission-oriented non-terrestrial networks (NTNs) in post-disaster emergency scenarios. When terrestrial infrastructures are damaged, unmanned aerial vehicles (UAVs) equipped with edge information hubs (EIHs) are deployed to provide temporary coverage and synergize communication and computing resources for rapid situation awareness. We formulate a joint resource configuration and location optimization problem to minimize overall resource costs while guaranteeing stringent latency requirements. Through analytical derivations, we obtain closed-form optimal solutions that reveal the fundamental tradeoff between communication and computing resources, and develop a successive convex approximation method for EIH location optimization. Simulation results demonstrate that the proposed scheme achieves approximately 20% cost reduction compared with benchmark approaches, validating its optimality and effectiveness for mission-critical emergency response applications in the sixth-generation (6G) era.

Broadcasting systems often suffer from coverage limitation and uneven service quality, particularly in non-line-of- sight (NLOS) environments. Reconfigurable intelligent surface (RIS) has emerged as a promising technology capable of reshaping electromagnetic propagation paths to enhance signal coverage in complex wireless environments. However, the inability for RIS to directly acquire channel state information (CSI) restricts its adaptability. In this paper, we propose a novel hybrid RIS (HRIS)-assisted broadcasting scheme that maximizes the minimum signal-to-noise ratio (SNR) of users, where HRIS enables simultaneous signal reflection and real-time CSI acquisition. We formulate the problem as the maximization of the worst-case SNR of users by optimizing the HRIS phase configuration based on directly acquired CSI. A gradient-based optimization algorithm is developed to iteratively update the phase matrix of HRIS while preserving the fairness among users in broadcasting system. Simulation results demonstrate that the proposed scheme achieves considerable improvements in SNR and sum rate of all users. Moreover, a prototype system is implemented with an 8×8 HRIS array. The measurement results show good consistency with the simulation results. The experimental validation further confirms the effectiveness of the proposed scheme, achieving up to 16.47 dB performance enhancement.

5G-Advanced (5G-A) has enabled the vibrant development of low altitude integrated sensing and communication (LA-ISAC) networks. As a core component of these networks, unmanned aerial vehicles (UAVs) have witnessed rapid proliferation in recent years. However, due to the lag in traditional industry regulatory norms, unauthorized flight incidents occur frequently, posing a severe security threat to LA-ISAC networks. To surveil the non-cooperative UAVs, in this paper, we propose a multi-domain supervised contrastive learning (MD-SupContrast) framework for UAV radio frequency (RF) open-set recognition. Specifically, first, the texture features and the time-frequency position features from the ResNet and the TransformerEncoder (TE) are fused, and then the supervised contrastive learning is applied to optimize the feature representation of the closed-set samples. Next, to surveil the invasive UAVs that appear in real life, we propose an improved generative OpenMax (IG-OpenMax) algorithm and construct an open-set recognition model, namely Open-RFNet. According to the unknown samples, we first freeze the feature extraction layers and then only retrain the classification layer, which achieves excellent recognition performance both in closed-set and open-set recognitions. We analyze the computational complexity of the proposed model. Experiments are conducted with a large-scale UAV open dataset. The results show that the proposed Open-RFNet outperforms the existing benchmark methods in terms of recognition accuracy between the known and the unknown UAVs, as it achieves 95.12% in closed-set and 96.08% in open-set under 25 UAV types, respectively.

Artificial intelligence (AI) has emerged as a promising tool for channel state information (CSI) feedback. While recent research primarily focuses on improving feedback accuracy on a specific dataset through novel architectures, the underlying mechanism of AI-based CSI feedback remains unclear. This study explores the mechanism through analyzing performance across diverse datasets, with findings suggesting that superior feedback performance stems from AI models’ strong fitting capabilities and their ability to leverage environmental knowledge. Building on these findings, we propose a prompt-enabled large AI model (LAM) for CSI feedback. The LAM employs powerful Transformer blocks and is trained on extensive datasets from various scenarios. Meanwhile, the channel distribution (environmental knowledge), represented as the mean of channel magnitude in the angular-delay domain, is incorporated as a scenario-specific prompt within the decoder to further enhance reconstruction quality. Simulation results confirm that the proposed prompt-enabled LAM significantly improves feedback accuracy and generalization performance while reducing data collection requirements in new scenarios.

In industrial automation or emergency rescue, sensors and robots work together with the help of an edge information hub (EIH) containing both communication and computing modules. Typically, the EIH collects the sensing data via the sensor-to-EIH link, processes data and then makes decisions on board before sending commands to the robot via the EIH-to-robot link. This forms a sensing-communication-computing-control (SC3) closed loop. In practice, the inherent openness of wireless links within the closed loop leads to susceptibility to eavesdropping. To this end, this paper refines the conventional physical layer security (PLS) approach with a systematic thinking to safeguard the SC3closed loop. The closed-loop negentropy (CNE), a new metric for the performance of the whole SC3closed loop, is maximized under the closed-loop security constraint. The transmit time, power, bandwidth of both wireless links, and the computing capability, are jointly designed. The optimization problem is non-convex. We leverage the Karush-Kuhn-Tucker (KKT) conditions and the monotonic optimization (MO) theory to derive its globally optimal solution. Simulation results show the performance gain of the proposed systematic approach, and reveal the advantage of exploiting the closed-loop structure-level PLS over the link-level or sum-link-level designs.

The Terahertz band holds a promise to enable both super-accurate sensing and ultra-fast communication. However, challenges arise that severe Doppler effects call for a waveform with high Doppler robustness while severe propagation path loss urges for an ultra-massive multiple-input multiple-output (UM-MIMO) structure. To tackle these challenges, hybrid beamforming with orthogonal delay-Doppler multiplexing modulation (ODDM) is investigated in this paper. First, the integration of delay-Doppler waveform and MIMO is explored by establishing a hybrid beamforming-based UM-MIMO ODDM input-output relation. Then, a multi-dimension sensing algorithm on target azimuth angle, elevation angle, range, and velocity is proposed, which features low complexity and high accuracy. Finally, an innovative decoupling of optimization goals is achieved by prioritizing sensing performance: Cramér-Rao lower bounds (CRLB) are minimized through dynamic beam scanning at the sensing combiner, which in turn allows the spectral efficiency to be independently maximized with stable beams at the precoder. Numerical results show that the sensing accuracy of the proposed sensing algorithm is sufficiently close to CRLB. Moreover, the proposed hybrid beamforming design allows us to achieve maximal spectral efficiency, millimeter-level range estimation accuracy and millidegree-level angle estimation accuracy.

Large artificial intelligence (AI) models for wireless communications have demonstrated remarkable success across a range of wireless downstream tasks. However, their high computational overhead, low training efficiency, and limited privacy protection pose significant challenges for deployment on resource-constrained terminal devices. To address this issue, we propose a novel distributed framework that utilizes a three-layer cooperative paradigm to effectively achieve cooperation among agents, namely Multi-agent cooperative Large AI Models for Wireless Communications: MaLAM4Com. However, two key challenges in MaLAM4Com are how to effectively extract knowledge from shared information and how to alleviate the significant complexity arising from high-dimensional information sharing. To address these bottlenecks, we introduce federated distillation and Lyapunov cooperation to achieve robust knowledge transfer and consistent dynamic evolution, enabling the agents to capture the intrinsic structure of wireless channels. Subsequently, we innovatively utilize low-dimensional embeddings to facilitate information sharing among agents, significantly reducing cooperation complexity by up to 94% while enhancing privacy protection. This breaks traditional cooperative paradigms that rely on wireless channels. Moreover, we further introduce dataset distillation to enhance training efficiency by synthesizing elite data instead of directly utilizing raw datasets. Numerical results demonstrate that MaLAM4Com significantly outperforms existing baselines, with gains exceeding 45% under low sampling ratios. Remarkably, low-dimensional embeddings have also shown significant advantages in downstream tasks, reducing inference complexity by over 96%.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for enhancing communication systems. This paper investigates a novel flexible-position RIS-assisted communication system, where the RIS is mounted on a slide rail, enabling spatial adaptability. Unlike traditional fixed-position RISs, the proposed system leverages both spatial flexibility and phase reconfigurability to optimize system performance while reducing overhead through strategic position adjustment. To characterize the spatial variations introduced by RIS movement, we propose a generalized RIS channel model that integrates a practical visibility region function with near-field spherical wave propagation. This model captures the spatial correlation characteristics influenced by multipath angular spread, scatterer distribution, and RIS positioning. Furthermore, we introduce a measurement scheme using a multi-state RIS hardware to analyze segmented channels across fixed-position and flexible-position scenarios. Our measurement reveals that the intra-cluster power angular spectrum follows a Gaussian distribution, and in strong scattering environments, spatial correlation exhibits an enhanced degree of freedom due to spatial non-stationarity effects. In particular, the experimental results demonstrate that the gain of the received power varies from 0.4 dB to 5.3 dB across different RIS positions, providing empirical evidence that spatial adaptability of RIS effectively resists channel non-stationarity. These findings highlight the potential of flexible-position RIS to enhance future wireless communication systems.

The mid-band frequency range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is emerging as a key enabler for future communication systems. By exploiting the advent of new spectrum resources and degrees of freedom brought by the near-field propagation, the mid-band XL-MIMO system is expected to significantly enhance throughput and inherently support advanced functionalities such as integrated sensing and communication. Although theoretical studies have highlighted the benefits of mid-band XL-MIMO systems, the promised performance gains have yet to be validated in practical systems, posing a major challenge to the standardization. In this paper, preliminaries including frame structure, channel modeling, and signal models are first discussed, followed by an analysis of key challenges in constructing a real-time prototype system. Subsequently, the design and implementation of a real-time mid-band XL-MIMO prototype system are presented. Underpinned by a novel architecture, the proposed prototype system supports specifications aligned with standardization, including a bandwidth of 200 MHz, up to 1024 antenna elements, and up to 256 transceiver chains. Operating in time-division duplexing mode, the prototype enables multiuser communication for up to 12 users, while retaining standard communication procedures. Built on hybrid software-defined radio and field programmable gate array platforms, the prototype is programmable and allows for flexible deployment of advanced algorithms. Moreover, the modular architecture ensures high scalability, making the prototype adaptable to various configurations, including distributed deployments and decentralized signal processing. Experimental results demonstrate that the prototype handles real-time digital sample processing at 1453.33 Gbps and achieves a peak data throughput of 15.81 Gbps for 12 users.

Integrated sensing and communication (ISAC) is a key feature of future cellular systems, enabling applications such as intruder detection, monitoring, and tracking using the same infrastructure. However, its potential for structural health monitoring (SHM), which requires the detection of slow and subtle structural changes, remains largely unexplored due to challenges such as multipath interference and the need for ultra-high sensing precision. This study introduces a novel theoretical framework for SHM via ISAC by leveraging reconfigurable intelligent surfaces (RIS) as reference points in collaboration with base stations and users. By dynamically adjusting RIS phases to generate distinct radio signals that suppress background multipath interference, measurement accuracy at these reference points is enhanced. We theoretically analyze RIS-aided collaborative sensing in three-dimensional cellular networks using Fisher information theory, demonstrating how increasing observation time, incorporating additional receivers (even with self-positioning errors), optimizing RIS phases, and refining collaborative node selection can reduce the position error bound to meet SHM’s stringent accuracy requirements. Furthermore, we develop a Bayesian inference model to identify structural states and validate damage detection probabilities. Both theoretical and numerical analyses confirm ISAC’s capability for millimeter-level deformation detection, highlighting its potential for high-precision SHM applications.

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone programming (SOCP), penalty-dual-decomposition (PDD) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the active RIS and UAV techniques into the optimization design, the covert transmission performance of ISCC systems are improved while ensuring a certain level of target sensing and EC performance.

Security and reliability have always evolved alongside advancements in communication technologies. Facing imminent the sixth generation of mobile communication (6G) era, this work explores a jamming exploitation-enabled covert transmission framework for satellite-aerial-terrestrial integrated networks (SATINs), aiming to meet user privacy requirements in future complex adversarial scenarios characterized by stereoscopic coverage and multi-domain collaboration. The research scenario involves a three-dimensional space comprising four core elements: a satellite, an unmanned aerial vehicle (UAV) equipped with an active simultaneously transmitting and reflecting reconfigurable intelligent surface (active STAR-RIS), an eavesdropper possessing dual functionalities of jamming and detection, and a ground terminal. Upon sensing malicious jamming from the eavesdropper, the UAV aerial platform serving as a relay node utilizes its on-board active STAR-RIS to achieve the targeted reflection and manipulation of the malicious jamming signals while concurrently facilitating the effective forwarding of the legitimate signals. Namely, breaking through the conventional mindset of “jamming suppression”, it equivalently constructs a “self-interference loop” centered on the eavesdropper, thereby degrading the adversary’s detection sensitivity. For this process, we construct a covert analysis framework featuring the joint design of the static/dynamic scenarios and the active STAR-RIS reflection-transmission matrices dominated by the UAV’s limited power, derive the analytical expression of the Kullback-Leibler (KL) divergence, and establish rigorous covertness constraints for the system. To address the highly coupled non-convex problem in the joint optimization,we propose a solution combining semidefinite relaxation (SDR), Dinkelbach transformation, Gaussian randomization, and the proximal policy optimization (PPO) framework, maximizing the system covert transmission rate while satisfying various constraints. Numerical results demonstrate that, compared with benchmark schemes, the proposed scheme exhibits superior flexibility and covert transmission advantages in adversarial environments.

In this article, a framework of artificial intelligence (AI)-native cross-module optimized physical layer with cooperative control agents is proposed, which involves optimization across global AI/machine learning (ML) modules of the physical layer with innovative design of multiple enhancement mechanisms and control strategies. Specifically, it achieves simultaneous optimization across global modules of uplink AI/ML-based joint source-channel coding with modulation, and downlink AI/ML-based modulation with precoding and corresponding data detection, reducing traditional inter-module information barriers to facilitate end-to-end optimization toward global objectives. Moreover, multiple enhancement mechanisms are also proposed, including i) an AI/ML-based cross-layer modulation approach with theoretical analysis for downlink transmission that breaks the isolation of inter-layer features to expand the solution space for determining improved constellation, ii) a utility-oriented precoder construction method that shifts the role of the AI/ML-based CSI feedback decoder from recovering the original CSI to directly generating precoding matrices aiming to improve end-to-end performance, and iii) incorporating modulation into AI/ML-based CSI feedback to bypass bit-level bottlenecks that introduce quantization errors, non-differentiable gradients, and limitations in constellation solution spaces. Furthermore, AI/ML-based control agents for optimized transmission schemes are proposed that leverage AI/ML to perform model switching according to channel state, thereby enabling integrated control for global throughput optimization. Finally, simulation results demonstrate the superiority of the proposed solutions in terms of block error rate and throughput. These extensive simulations employ more practical assumptions that are aligned with the requirements of the 3rd Generation Partnership Project (3GPP), which hopefully provides valuable insights for future 3GPP standardization discussions.

The upper mid-band balances coverage and capacity for the future cellular systems and also embraces extremely large-scale multiple-input multiple-output (XL-MIMO) systems, offering enhanced spectral and energy efficiency. However, these benefits are significantly degraded under mobility due to channel aging, and further exacerbated by the unique near-field (NF) and spatial non-stationarity (SnS) propagation in such systems. To address this challenge, we propose a novel channel prediction approach that incorporates dedicated channel modeling, probabilistic representations, and Bayesian inference algorithms for this emerging scenario. Specifically, we develop tensor-structured channel models in both the spatial-frequency-temporal (SFT) and beam-delay-Doppler (BDD) domains, which capture the NF and SnS propagation effects and leverage temporal correlations among multiple snapshots for channel prediction. In this model, the factor matrices of multi-linear transformations are parameterized by BDD domain grids and SnS factors, where beam domain grids are jointly determined by angles and slopes under spatial-chirp based NF representations. To enable tractable inference, we replace these environment-dependent BDD domain grids with uniformly sampled ones, and introduce perturbation parameters in each domain to mitigate grid mismatch.We further propose a hybrid beam domain strategy that integrates angle-only sampling with slope hyperparameterization to avoid the computational burden of explicit slope sampling. On this basis, we develop tensor-structured bi-layer inference (TS-BLI) algorithm under the expectation-maximization (EM) framework, which reduces the computational complexity by leveraging the inherent separation across different domains. In the E-step, we develop the bi-layer factor graph representation to isolate the bilinear mixing in the spatial domain induced by SnS propagation, thus facilitating bi-layer iterations using approximate inference techniques. In the M-step, we leverage an alternating strategy for hyperparameter learning, with closed-form rules derived by the quadratic approximation of objective functions. Numerical simulations based on a near-practical channel simulator developed upon QuaDRiGa with SnS extensions demonstrate the superior channel prediction performance of the proposed algorithm.

Integrated sensing and communication (ISAC) has emerged as a key enabler for future wireless networks by unifying communication and sensing functionalities within a shared framework. However, achieving the coordination gains of these two functionalities critically depends on accurate estimation of the sensing targets and communication channels, while their joint estimation remains challenging. To address this, this paper proposes a Bayesian tensor decomposition (BTD) approach for joint channel estimation and target sensing in multiple-input multiple-output (MIMO)-ISAC systems, where parts of sensing targets also act as communication scatterers. Specifically, we develop space-frequency domain received signal models for target sensing and channel estimation and formulate them as canonical polyadic decomposition (CPD) problems under the tensor decomposition framework. This formulation reveals the common multilinear structure and the partially shared physical parameters between sensing and communication, which underpins the ensuing joint estimation task. To solve these problems, we propose a dual-module Vandermonde structure-assisted BTD (V-BTD) algorithm that incorporates propagation-induced Vandermonde structure constraints within a Bayesian framework to enable effective sensing-communication collaboration while maintaining problem feasibility. In this algorithm, Module A estimates the factor matrices via unstructured BTD with Gaussian priors, whereas Module B exploits the Vandermonde structure to recover the underlying physical parameters using generalized von Mises priors. The dual-module design alternates between an unstructured tensor decomposition step and a structure-aware parameter recovery step, yielding a favorable trade-off between inference exactness and computational tractability. With the flexible prior models in the BTD framework, the proposed algorithm supports both uninformative and informative settings, thereby allowing sensing-derived information to be incorporated for communication channel estimation to further improve estimation accuracy. Simulation results demonstrate that the proposed method significantly outperforms the benchmarks, highlighting its superiority for advanced ISAC systems.

Reconfigurable intelligent surface (RIS) technology has gained widespread recognition for its ability to significantly enhance communication performance between base stations (BS) and users in blind spot areas. This paper investigates the energy efficiency (EE) of an RIS-assisted multi-cell communication system, incorporating a measurement-based empirical RIS power consumption model. Exploiting only statistical channel state information (CSI), we aim to maximize the overall EE of the system. An alternating optimization (AO) algorithm is proposed for joint optimization of the transmit beamforming vectors at the BSs and the RIS phase shift matrix. To circumvent the challenges arising from the discrete relationship between the power consumption of positive-intrinsic-negative (PIN) diode-based reflecting elements and their phase shifts, we introduce a continuous function to approximate the relationship. With this approximation, the complex circle manifold (CCM) technique is applied to optimize the continuous RIS phase shifts, which are then quantized within a discrete set. Then, to further reduce the complexity of the AO algorithm, a sub-optimal algorithm is proposed. In this algorithm, the statistical maximum ratio transmission beamforming is employed at each BS, which decouples the design of BS beamforming vectors from the design of BS transmit power and RIS phase shifts. Given the BS beamforming vectors, the BS transmit power and RIS phase shifts are then designed using the quadratic transformation and CCM techniques. Simulation results show that the proposed algorithm achieves near-optimal EE while offering guidance on PIN diode encoding to balance phase resolution and power consumption in practical RIS design.

Since the beam squint and near-field effects both inherently exist in upper-6 GHz (U6G) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, wideband near-field channel estimation faces severe challenges, such as higher computational complexity, and higher pilot overhead particularly at hybrid architectures with fewer radio frequency (RF) chains. To precisely reduce the complexity and number of pilots, theparametric symmetry of wideband near-field channelsis explored, such that the channel parameters, including angle, distance, and range, can be decoupled based on the delay variations observed by different antennas. Based on this, adistributed parametric symmetry-based (DPS) algorithm, applicable to U6G XL-MIMO, is proposed. The delays observed by different subarrays are estimated and extrapolated across the local processing units (LPUs) firstly, and then, the channel parameters are decoupled and estimated at the central processing unit (CPU), by only linearly combining the delays from different LPUs. The path gains are calculated at different LPUs, respectively, to reconstruct the channel with low complexity. Since the proposed algorithm does not rely on scanning the polar-domain dictionary, onlya single pilotis required even with hybrid architectures. Furthermore, the computational complexity, multiple-path resolution, Cramér–Rao lower bound (CRLB) and lower bound (LB) of the estimates in hybrid architectures and the DPS algorithm, respectively, are analyzed, to evaluate the realizable potential of the proposed algorithm. The simulation results prove that the proposed algorithm has a higher estimation accuracy, while requiring less complexity and pilots.

Compact ultra-massive antenna arrays (CUMA) share key characteristics with holographic communication systems, featuring densely spaced and individually controlled antenna elements that enable precise manipulation of electromagnetic waves. In this paper, we investigate the spectral efficiency (SE) of CUMA deployed within some constrained physical space. Departing from prior works that assume ideal isotropic antennas, we derive a closed-form expression for the SE assuming a line-of-sight (LoS) channel at the electromagnetic level, explicitly accounting for mutual coupling and antenna orientation. The analysis reveals that the channel gain is highly sensitive to both the array orientation and individual antenna directions. In the single-user case, our results show that the optimal orientation of the user array is either aligned parallel or perpendicular to the signal direction, depending on the inter-element spacing. Notably, near-optimal channel gain is achieved when individual antennas are oriented perpendicular to the signal direction. In the multi-user case, we further optimize transceiver configurations under mutual coupling constraints. Simulation results confirm that SE is strongly influenced by the directional alignment of user antennas and array placement in the near-field regime. CUMA significantly outperforms traditional half-wavelength spaced arrays in terms of SE when constrained to the same physical aperture.

For ultra-dense vehicular networks, dynamic resource optimization among a large number of heterogeneous agents is rather challenging. This paper proposes a learning-based resource allocation scheme in a vehicle-assisted mobile edge computing (MEC) network, where the uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs) are equipped with the MEC servers to provide the computational offloading services to the ground users with time-varying computing demands. Each self-interested vehicle jointly optimizes its trajectory planning and data offloading policies to maximize the expectation of its locally cumulative energy efficiency under the collision and energy constraints. We model the non-cooperative interactions among the massive co-channel vehicles as a multi-population mean-field game (MPMFG), where each vehicle constructs two types of mean-field terms to model the UAVs’ and UGVs’ population distributions, respectively. We propose a multi-population mean-field parameterized deep Q network (MPMF-PDQN) algorithm to solve the equilibrium among the vehicular servers in a discrete-continuous hybrid action space. The simulation results demonstrate that the proposed algorithm significantly enhances the average energy efficiency of the vehicles compared with the baseline algorithms.

Channel State Information (CSI) provides a detailed description of the wireless channel and has been widely adopted for Wi-Fi sensing, particularly for high-precision indoor positioning. However, complete CSI is rarely available in real-world deployments due to hardware constraints and the high communication overhead required for feedback. Moreover, existing positioning models lack mechanisms to detect when users move outside their trained regions, leading to unreliable estimates in dynamic environments. In this paper, we present FPNet, a unified deep learning framework that jointly addresses channel feedback compression, accurate indoor positioning, and robust anomaly detection (AD). FPNet leverages the beamforming feedback matrix (BFM), a compressed CSI representation natively supported by IEEE 802.11ac/ax/be protocols, to minimize feedback overhead while preserving critical positioning features. To enhance reliability, we integrate ADBlock, a lightweight AD module trained on normal BFM samples, which identifies out-of-distribution scenarios when users exit predefined spatial regions. Experimental results using standard 2.4 GHz Wi-Fi hardware show that FPNet achieves positioning accuracy above 97% with only 100 feedback bits, boosts net throughput by up to 22.92%, and attains AD accuracy over 99% with a false alarm rate below 1.5%. These results demonstrate FPNet’s ability to deliver efficient, accurate, and reliable indoor positioning on commodity Wi-Fi devices.

Mid-band extra-large-scale multiple-input multiple-output (XL-MIMO), emerging as a critical enabler for future communication systems, is expected to deliver significantly higher throughput by leveraging the extended bandwidth and enlarged antenna aperture. However, power consumption remains a significant concern due to the expanded system dimension, underscoring the need for thorough investigations into efficient system design and deployment. To this end, an in-depth study is conducted on mid-band XL-MIMO systems. Specifically, a comprehensive power consumption model is proposed, encompassing the power consumption of major hardware components and signal processing procedures, while capturing the influence of key system parameters. Considering typical near-field propagation characteristics, closed-form approximations of throughput are derived, providing an analytical framework for assessing energy efficiency (EE). Based on the proposed framework, the scaling law of EE with respect to key system configurations is derived, offering valuable insights for system design. Subsequently, extensions and comparisons are conducted among representative multi-antenna technologies, demonstrating the superiority of mid-band XL-MIMO in EE. Extensive numerical results not only verify the tightness of the throughput analysis but also validate the EE evaluations, unveiling the potential of energy-efficient mid-band XL-MIMO systems.

Mid-band extra large-scale multiple-input multiple-output (XL-MIMO) systems are considered a key enabler for future wireless communications, offering enhanced throughput and extended coverage. Combined with subarray-based architecture and distributed signal processing, the computational complexity and implementation overhead are reduced. However, uncertain channel characteristics associated with the novel frequency band present significant bottlenecks, hindering the development of hardware architecture and algorithm design. Meanwhile, channel characteristics across distributed processing units remain insufficiently explored. In response, a mid-band channel sounder is constructed, and extensive measurement campaigns are carried out across various typical scenarios. Initially, mid-band channel characteristics are unveiled and compared across different scenarios. Subsequently, the mid-band XL-MIMO channel characteristics are analyzed using a virtual array comprising 256 array antennas and 64 transceiver chains. Moreover, motivated by the potential of distributed processing, mid-band XL-MIMO channel characteristics are particularly investigated from the perspectives of subarrays and sub-bands, encompassing subarray-wise non-stationarities, consistencies, far-field approximations, and sub-band characteristics. Through the combination of analysis and measurement validation, several insights and benefits are revealed, particularly relevant to distributed architecture and processing, which provides practical guidance for the real-world deployment of mid-band XL-MIMO systems.

Multiple-input multiple-output (MIMO) has been a key technology for wireless systems for decades. For typical MIMO communication systems, antenna array elements are usually separated by half of the carrier wavelength, thus termed as co-located MIMO. In this paper, we investigate the performance of multi-user sparse MIMO communication, with sparse arrays at both the transmitter and receiver side, i.e., the array elements are separated by more than half wavelength. Given the same number of array elements, the performance of sparse MIMO is compared with co-located MIMO. On one hand, sparse MIMO has a larger aperture, which can achieve narrower main lobe beams that make it easier to resolve densely located users. Besides, increased array aperture also enlarges the near-field communication region, which can enhance the spatial multiplexing gain, thanks to the spherical wavefront property in the near-field region. On the other hand, element spacing larger than half wavelength leads to undesired grating lobes, which, if left unattended, may cause severe multi-user interference (MUI). Specifically, we first study the spatial multiplexing gain of the basic single-user sparse MIMO communication system, where a closed-form expression of the near-field effective degree of freedom (EDoF) is derived. The result shows that EDoF increases with the array sparsity for sparse MIMO before reaching its upper bound, which equals to the minimum value between the transmit and receive antenna numbers. Furthermore, the scaling law for the achievable data rate with varying array sparsity is analyzed and an array sparsity-selection strategy is proposed.We then consider the more general multi-user sparse MIMO communication system. It is shown that sparse MIMO is less likely to experience severe MUI than co-located MIMO, especially when users are densely located, thanks to the non-uniform distribution of spatial angle difference among users. Finally, numerical results are provided to validate our theoretical analysis.

Judicious resource allocation can effectively enhance federated learning (FL) training performance in wireless networks by addressing both system and statistical heterogeneity. However, existing strategies typically rely on block fading assumptions, which overlook rapid channel fluctuations within each round of FL gradient uploading, leading to a degradation in FL training performance. Therefore, this paper proposes a small-scale-fading-aware resource allocation strategy using a multi-agent reinforcement learning (MARL) framework. Specifically, we establish a one-step convergence bound of the FL algorithm and formulate the resource allocation problem as a decentralized partially observable Markov decision process (Dec-POMDP), which is subsequently solved using the QMIX algorithm. In our framework, each client serves as an agent that dynamically determines spectrum and power allocations within each coherence time slot, based on local observations and a reward derived from the convergence analysis. The MARL setting reduces the dimensionality of the action space and facilitates decentralized decision-making, enhancing the scalability and practicality of the solution. Experimental results demonstrate that our QMIX-based resource allocation strategy significantly outperforms baseline methods across various degrees of statistical heterogeneity. Additionally, ablation studies validate the critical importance of incorporating small-scale fading dynamics, highlighting its role in optimizing FL performance.

Accurate channel state information (CSI) acquisition is essential for modern wireless systems, which becomes increasingly difficult under large antenna arrays, strict pilot overhead constraints, and diverse deployment environments. Existing artificial intelligence-based solutions often lack robustness and fail to generalize across scenarios. To address this limitation, this paper introduces a predictive-foundation-model-based channel estimation framework that enables accurate, low-overhead, and generalizable CSI acquisition. The proposed framework employs a predictive foundation model trained on large-scale cross-domain data to extract universal channel representations and provide predictive priors with strong cross-scenario transferability. A pilot processing network based on a vision transformer architecture is further designed to capture spatial, temporal, and frequency correlations from pilot observations. An efficient fusion mechanism integrates predictive priors with real-time measurements, enabling reliable CSI reconstruction even under sparse or noisy conditions. Extensive evaluations across diverse configurations demonstrate that the proposed estimator significantly outperforms both classical and data-driven baselines in accuracy, robustness, and generalization capability.

With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRS) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to ensure orthogonality and minimize pilot interference. In this paper, we investigate uplink DMRS-based channel estimation for MU-MIMO systems with Type II OCC pattern standardized in third generation partnership project (3GPP) Release 18, leveraging location-specific statistical channel state information (SCSI) to enhance performance. Specifically, we propose a SCSI-assisted Bayesian channel estimator (SA-BCE) based on the minimum mean square error criterion to suppress the pilot interference and noise, albeit at the cost of cubic computational complexity due to matrix inversions. To reduce this complexity while maintaining performance, we extend the scheme to a windowed version (SA-WBCE), which incorporates antenna-frequency domain windowing and beam-delay domain processing to exploit asymptotic sparsity and mitigate energy leakage in practical systems. To avoid the frequent real-time SCSI acquisition, we construct a grid-based location-specific SCSI database based on the principle of spatial consistency, and subsequently leverage the uplink received signals within each grid to extract the SCSI. Facilitated by the multilinear structure of wireless channels, we formulate the SCSI acquisition problem within each grid as a tensor decomposition problem, where the factor matrices are parameterized by the multi-path powers, delays, and angles. The computational complexity of SCSI acquisition can be significantly reduced by exploiting the Vandermonde structure of the factor matrices. Simulation results demonstrate that the proposed location-specific SCSI database construction method achieves high accuracy, while the SA-BCE and SA-WBCE significantly outperform state-of-the-art benchmarks in MU-MIMO systems.

Despite impressive capability in learning over graphstructured data, graph neural networks (GNN) suffer from adversarial topology perturbation in both training and inference phases. While adversarial training has demonstrated remarkable effectiveness in image classification tasks, its suitability for GNN models has been doubted until a recent advance that shifts the focus fromtransductivetoinductivelearning. Still, GNN robustness in the inductive setting is under-explored, and it calls for deeper understanding of GNN adversarial training. To this end, we introduce a concept of graph subspace energy (GSE)—a generalization of graph energy that measures graph stability—of the adjacency matrix, as an indicator of GNN robustness against topology perturbations. To further demonstrate the effectiveness of such concept, we propose an adversarial training method with the perturbed graphs generated by maximizing the GSE regularization term, referred to as AT-GSE. To deal with the local and global topology perturbations raised respectively by LRBCD and PRBCD, we employ randomized SVD (RndSVD) and Nyström low-rank approximation to favor the different aspects of the GSE terms. An extensive set of experiments shows that AT-GSE outperforms consistently the state-of-the-art GNN adversarial training methods over different homophily and heterophily datasets in terms of adversarial accuracy, whilst more surprisingly achieving a superior clean accuracy on non-perturbed graphs.

Symmetric multilevel diversity coding (SMDC) is a multi-source coding problem where the independent sources are ordered according to their importance. Prior work demonstrated thatsuperposition coding, where sources are encoded independently, is optimal. This paper investigates the(L,s)sliding secure SMDC problem, whereLrepresents the number of encoders andsis the security threshold. The security requirement dictates that each sourceXαmust be kept perfectly secure if no more than α –sencoders are accessible. The problem is specialized to the(L,s)multilevel secret sharingproblem when the firsts – 1sources are constants. Fors= 1, the two problems coincide, and we show that superposition coding is optimal. The rate regions for the(L,s)=(3,2)problems are characterized, which implies that superposition coding is suboptimal for the general case. The core insight for achieving lower rates through joint encoding is leveraging less important sources likeXα–1as secret keys for more important sources likeXα. Based on this idea, we propose a joint coding scheme that achieves the minimum sum rate of the general(L,s)multilevel secret sharing problem. Moreover, a pseudo-superposition coding scheme is proposed to achieve the minimum sum rate of the general sliding secure SMDC problem, which uses superposition coding for thessets of sourcesX1, X2,..., Xs–1, (Xs,Xs+1,XL)and joint coding amongXs,Xs+1,XL.

Consider a distributed coding for computing problem with constant decoding locality, i.e., with a vanishing error probability, any single sample of the function can be approximately recovered by probing only a constant number of compressed bits. We establish an achievable rate region by designing an efficient layered coding scheme, where the coding rate is reduced by introducing auxiliary random variables and local decoding is achieved by exploiting the expander graph code. Then we show the rate region is optimal under mild regularity conditions on source distributions. The proof relies on the reverse hypercontractivity and a rounding technique to construct auxiliary random variables. The rate region is strictly smaller than that for the classical problem without the constant locality constraint in most cases, which indicates that more rate is required in order to achieve lower coding complexity. Moreover, a coding for computing problem with side information is analogously studied. We also develop graph characterizations, which simplifies the computation of the achievable rate region.

The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation con firms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5 dB level.

Unmanned aerial vehicles (UAVs) are widely deployed as aerial base stations to provide flexible communication coverage for ground users (GUs), yet the air-ground communications remain highly vulnerable to the jamming attacks. Unlike conventional fixed-policy jammers, the intelligent jammers dynamically adapt their jamming strategies based on the observed UAV communication policies, creating significant anti-jamming challenges particularly under asymmetric information. In this paper, we formulate the strategic interactions between a UAV-mounted server and a jammer as a partially observable pursuit-evasion game, where the UAV aims to maximize the GUs' uplink rates through dynamic evasion while the jammer strategically pursues to maximize the jamming effect. The information asymmetry is explicitly modeled by considering both the jammer's hidden location from the UAV and the jammer's inability to observe the UAV's remaining energy state. To optimize the UAV's anti-jamming policy under these challenges, we propose a novel opponent-modeling based reinforcement learning algorithm, named neural fictitious self-play with dueling double deep recurrent Q network (NFSP-D3RN). This algorithm optimizes the UAV's anti-jamming policy through reinforcement learning, while maintaining robustness against non-stationarity induced by the jammer's adaptive behavior through implicit opponent modeling. Extensive simulations demonstrate that our proposed algorithm achieves superior anti-jamming performance compared with the benchmarks under unknown jammer locations, with results approaching the upper bound of perfect location knowledge.

High-density Wi-Fi deployments often result in significant co-channel interference, which degrades overall network performance. To address this issue, coordination of multi access points (APs) has been considered to enable coordinated spatial reuse (CSR) in next generation wireless local area networks. This paper tackles the challenge of downlink spatial reuse in Wi-Fi networks, specifically in scenarios involving overlapping basic service sets, by employing hierarchical multi-agent reinforcement learning (HMARL). We decompose the CSR process into two phases, i.e., a polling phase and a decision phase, and introduce the HMARL algorithm to enable efficient CSR. To enhance training efficiency, the proposed HMARL algorithm employs a hierarchical structure, where station selection and power control are determined by a high- and low-level policy network, respectively. Simulation results demonstrate that this approach consistently outperforms baseline methods in terms of throughput, mean delay and delay jitter across various network topologies. Moreover, the algorithm exhibits robust performance when coexisting with legacy APs. Additional experiments in a representative topology further reveal that the carefully designed reward function not only maximizes the overall network throughput, but also improves fairness in transmission opportunities for APs in high-interference regions.

Reconfigurable Intelligent Surfaces (RIS) are an emerging technology that holds significant promise for customizing wireless channels to meet specific communication requirements. Accurate channel state information (CSI) is essential for fully realizing the potential of RIS. However, due to the passive nature of RIS and the large number of reflecting elements, acquiring CSI for the base station (BS)-RIS-user equipment (UE) link presents considerable challenges. In this paper, we propose a deep learning (DL)-based framework for downlink CSI acquisition. Specifically, we introduce a novel DL-based channel estimation framework, termed PPNet, which facilitates efficient pilot transmission. The key innovation of PPNet lies in the joint design and optimization of pilot signals from the BS and phase shifts from the RIS, both represented through neural networks, alongside the channel estimation module. By capturing environment-specific features with neural networks, PPNet enables more efficient utilization of pilot power. Furthermore, we propose a beamforming-oriented CSI acquisition framework, RIS-E2ENet, which jointly optimizes the entire CSI acquisition process, including channel estimation, CSI feedback, and active/passive beamforming design, to enhance CSI acquisition efficiency. To adapt to the dynamic nature of real-world environments, RIS-E2ENet incorporates a lightweight UE-side neural network design, enabling low-overhead online training. Extensive evaluations show that the proposed frameworks improve spectral efficiency by 58.55%, while maintaining minimal pilot and feedback overhead.

The increasing demand for services such as live streaming and virtual reality places significant pressure on wireless communication systems. Enhancing system performance or reducing bandwidth consumption is critical for delivering high-quality video experiences. Semantic communication, which focuses on the transmission of meaning, offers a promising solution. However, existing approaches are often limited to single scenarios, rely on simple channels, lack adaptability to dynamic wireless environments, and remain untested in practical air interfaces. To address these challenges, we propose a foundation model-aided universal video semantic communication framework designed for pixel-wise reconstruction across diverse scenarios. This framework enables the transmission of entire videos using joint source-channel coding (JSCC) based on optical flow estimation and leverages multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) for efficient semantic delivery in 3rd generation partnership project (3GPP) standard channels. In scenarios requiring full transmission for regions of interest and selective transmission for other areas, the framework employs a foundation model for segmentation, followed by JSCC and delivery. Furthermore, we introduce a channel condition number-adaptive semantic remapping method based on an attention mechanism to mitigate the effects of wireless fading. To validate our approach, we implement the framework on a testbed and develop two online demonstrations. Simulations and over-the-air experiments confirm significant improvements in video quality and substantial reductions in bandwidth overhead compared to existing methods.

Movable antenna (MA), which can flexibly change the position of antenna in three-dimensional (3D) continuous space, is an emerging technology for achieving full spatial performance gains. In this paper, a prototype of MA communication system with ultra-accurate movement control is presented to verify the performance gain of MA in practical environments. The prototype utilizes the feedback control to ensure that each power measurement is performed after the MA moves to a designated position. The system operates at 3.5 GHz or 27.5 GHz, where the MA moves along a one-dimensional horizontal line with a step size of 0.01λ and in a two-dimensional square region with a step size of 0.05λ, respectively, with λ denoting the signal wavelength. The scenario with mixed line-of-sight (LoS) and non-LoS (NLoS) links is considered. Extensive experimental results are obtained with the designed prototype and compared with the simulation results, which validate the great potential of MA technology in improving wireless communication performance. For example, the maximum variation of measured power in the considered scenario reaches over 40 dB and 23 dB at 3.5 GHz and 27.5 GHz, respectively, thanks to the flexible antenna movement. In addition, experimental results indicate that the power gain of MA system relies on the estimated path state information (PSI), including the number of paths, their elevation and azimuth angles of arrival (AoAs), as well as the complex gain of each path.

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schemes can be significantly deteriorated due to the signaling randomness induced by finite-alphabet modulations with non-constant modulus, such as quadrature amplitude modulation (QAM) constellations. Therefore, improving sensing performance without significantly compromising communication capability (i.e., maintaining randomness), remains a challenging task. To that end, we propose a unified probabilistic constellation shaping (PCS) framework that is compatible with both matched and mismatched filtering schemes, by maximizing the communication rate while imposing constraints on mean square error (MSE) of sensing channel state information (CSI), power, and probability distribution. Specifically, the MSE of sensing CSI is leveraged to optimize sensing capability, which is illustrated to be a more comprehensive metric compared to the output SNR after filtering (SNRout) and integrated sidelobes ratio (ISLR). Additionally, the internal relationships among these three sensing metrics are explicitly analyzed. Building upon this, we further reveal that the normalized MSE can be interpreted as a penalty function version of the dynamic range, which is usually exploited to evaluate the behavior of delay-Doppler profiles. Finally, both simulations and field measurements validate the efficiency of proposed PCS approach in achieving a flexible S&C trade-off, as well as its credibility in enhancing 6G wireless transmission in real-world scenarios.

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

Semantic communication has been introduced into collaborative perception systems for autonomous driving, offering a promising approach to enhancing data transmission efficiency and robustness. Despite its potential, existing semantic communication approaches predominantly rely on analog transmission models, rendering these systems fundamentally incompatible with the digital architecture of modern vehicle-to-everything (V2X) networks and posing a significant barrier to real-world deployment. To bridge this critical gap, we propose CoDS, a novel collaborative perception framework based on digital semantic communication, designed to realize semantic-level transmission efficiency within practical digital communication systems. Specifically, we develop a semantic compression codec that extracts and compresses task-oriented semantic features while preserving downstream perception accuracy. Building on this, we propose a novel semantic analog-to-digital converter that converts these continuous semantic features into a discrete bitstream, ensuring integration with existing digital communication pipelines. Furthermore, we develop an uncertainty-aware network (UAN) that assesses the reliability of each received feature and discards those corrupted by decoding failures, thereby mitigating the cliff effect of conventional channel coding schemes under low signal-to-noise ratio (SNR) conditions. Extensive experiments demonstrate that CoDS significantly outperforms existing semantic communication and traditional digital communication schemes, achieving state-of-the-art perception performance while ensuring compatibility with practical digital V2X systems.

To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user’s position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while the propagation environments between different base stations and a specific user vary and their respective channels are uncorrelated, the user’s position remains constant and unique across all channels. Building on this, the proposed PCEnet framework leverages the position as a bridge between channels to establish a mapping between the characteristics of different channels, thereby using one acquired channel to assist in the estimation and feedback of others. Specifically, this approach first utilizes neural networks (NNs) to infer the user’s position from the obtained channel. The estimated position, shared among BSs through a central processing unit (CPU), is then fed into an NN to design pilot symbols and concatenated with the feedback information to the channel reconstruction NN to reconstruct other channels, thereby significantly enhancing channel acquisition performance. Additionally, we propose a simplified strategy where only the estimated position is used in the reconstruction process without modifying the pilot design, thereby reducing latency. Furthermore, we introduce a position label-free approach that infers the relative user position instead of the absolute position, eliminating the need for ground truth position labels during the localization NN training. Simulation results demonstrate that the proposed PCEnet framework reduces pilot and feedback overheads by up to 50%.

In moderate- to high-mobility scenarios, channel state information (CSI) varies rapidly and becomes temporally non-stationary, leading to severe performance degradation in the massive multiple-input multiple-output (MIMO) transmissions. To address this issue, we propose a tensor-structured approach to dynamic channel prediction (TS-DCP) for massive MIMO systems with temporal non-stationarity, exploiting both dual-timescale and cross-domain correlations. Specifically, due to inherent spatial consistency, non-stationary channels over long-timescales can be approximated as stationary on short-timescales, decoupling complicated temporal correlations into more tractable dual-timescale ones. To exploit such property, we propose the sliding frame structure composed of multiple pilot orthogonal frequency-division multiplexing (OFDM) symbols, which capture short-timescale correlations within frames by Doppler domain modeling and long-timescale correlations across frames by Markov/autoregressive processes. Building on this, we develop the Tucker-based spatial-frequency-temporal domain channel model, incorporating angle-delay-Doppler (ADD) domain channels and factor matrices parameterized by ADD domain grids. Furthermore, we model cross-domain correlations of ADD domain channels within each frame, induced by clustered scattering, through the Markov random field and tensor-coupled Gaussian distribution that incorporates high-order neighborhood structures. Following these probabilistic models, we formulate the TS-DCP problem as variational free energy (VFE) minimization, and unify different inference rules through the structure design of trial beliefs. This formulation results in the dual-layer VFE optimization process and yields the online TS-DCP algorithm, where the computational complexity is reduced by exploiting tensor-structured operations. Numerical simulations demonstrate the significant superiority of the proposed algorithm over benchmarks in terms of channel prediction performance.

Wireless imaging has become a vital function in future integrated sensing and communication (ISAC) systems. However, traditional model-based and data-driven deep learning imaging methods face challenges related to multipath extraction, dataset acquisition, and multi-scenario adaptation. To overcome these limitations, this study innovatively combines implicit neural representation (INR) with explicit physical models to realize wireless imaging in reconfigurable intelligent surface (RIS)-aided ISAC systems. INR employs neural networks (NNs) to project physical locations to voxel values, which is indirectly supervised by measurements of channel state information with physics-informed loss functions. The continuous shape and scattering characteristics of targets are embedded into NN parameters through training, enabling arbitrary image resolutions and off-grid voxel value prediction. Additionally, three issues related to INR-based imager are further addressed. First, INR is generalized to enable efficient imaging under multipath interference by jointly learning image and multipath information. Second, the imaging speed and accuracy for dynamic targets are enhanced by embedding prior image information. Third, imaging results are employed to assist in RIS phase design for improved communication performance. Extensive simulations demonstrate that the proposed INR-based imager significantly outperforms traditional model-based methods with super-resolution abilities, and the focal length characteristics of the imaging system is revealed. Moreover, communication performance can benefit from the imaging results. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/INRImager.

The low-altitude economy is emerging as a key driver of future economic growth, necessitating effective flight activity surveillance using existing mobile cellular network sensing capabilities. However, traditional monostatic and localization-based sensing methods face challenges in fusing sensing results and matching channel parameters. To address these challenges, we model low-altitude surveillance as a compressed sensing (CS)-based imaging problem by leveraging the cooperation of multiple base stations and the inherent sparsity of aerial images. Additionally, we derive the point spread function to analyze the influences of different antenna, subcarrier, and resolution settings on the imaging performance. Given the random spatial distribution of unmanned aerial vehicles (UAVs), we propose a physics-embedded learning method to mitigate off-grid errors in traditional CS-based approaches. Furthermore, to enhance rare UAV detection in vast low-altitude airspace, we integrate an online hard example mining scheme into the loss function design, enabling the network to adaptively focus on samples with significant discrepancies from the ground truth during training. Simulation results demonstrate the effectiveness of the proposed low-altitude surveillance framework. The proposed physics-embedded learning algorithm achieves a 97.55% detection rate, significantly outperforming traditional CS-based methods under off-grid conditions. Part of the source code for this paper can be accessed at https://github.com/kiwi1944/LAEImager.

The Channel Quality Indicator (CQI) is a fundamental component of channel state information (CSI) that enables adaptive modulation and coding by selecting the optimal modulation and coding scheme to meet a target block error rate. While AI-enabled CSI feedback has achieved significant advances, especially in precoding matrix index feedback, AI-based CQI feedback remains underexplored. Conventional subband-based CQI approaches, due to coarse granularity, often fail to capture fine frequency-selective variations and thus lead to suboptimal resource allocation. In this paper, we propose an AI-driven subcarrier-level CQI feedback framework tailored for 6G and NextG systems. First, we introduce CQInet, an autoencoder-based scheme that compresses per-subcarrier CQI at the user equipment and reconstructs it at the base station, significantly reducing feedback overhead without compromising CQI accuracy. Simulation results show that CQInet increases the effective data rate by 7.6% relative to traditional subband CQI under equivalent feedback overhead. Building on this, we develop SR-CQInet, which leverages super-resolution to infer fine-grained subcarrier CQI from sparsely reported CSI reference signals (CSI-RS). SR-CQInet reduces CSI-RS overhead to 3.5% of CQInet's requirements while maintaining comparable throughput. These results demonstrate that AI-driven subcarrier-level CQI feedback can substantially enhance spectral efficiency and reliability in future wireless networks.

Reconfigurable intelligent surfaces (RISs) enhance communication performance by adjusting the propagation directions of incident signals. However, joint beamforming design requires the acquisition of channel state information, often leading to significant pilot overhead in RIS-assisted systems, particularly when the number of reflective elements is large. In this study, we analyze the characteristics of the cascaded channel and propose a method that combines amplitude correlation with existing structured sparsity. Leveraging these characteristics, we first derive an on-grid channel estimation method, demonstrating the effectiveness of incorporating additional characteristics in cascaded channel estimation. We then extend the proposed algorithm to off-grid channel estimation by refining the coarsely estimated channel using alternating optimization and gradient descent. Furthermore, we adapt the algorithm to enhance estimation accuracy with the support of digital twin (DT) technology, utilizing a few pilots to refine the channel generated by DT. Simulation results show up to a 5 dB improvement in normalized mean squared error compared to state-of-the-art channel estimation algorithms that employ structured sparsity. Additionally, with DT assistance, the proposed algorithm achieves nearly a two-fold performance improvement over traditional algorithms that do not incorporate amplitude correlation and structured sparsity.

With the capability to configure wireless propagation environment, reconfigurable intelligent surface (RIS) has attracted wide attention from both academia and industry, in which measurement campaign of RIS in commercial networks is of great importance for performance evaluation. However, existing RIS configuration schemes in practical environments are generally angle-based which require accurate angle information and mainly consider single incident beam, or statistical methods with high sampling overhead. In this paper, we propose a phase shift design scheme for RIS referred to as multiple incident beam superposition (MIBS) scheme, which can be applied in scenarios with incident signals on the RIS from multiple directions. Instead of utilizing random sampling, the proposed scheme firstly employs an incident beam scanning process to extract the incident beam information by pre-calculated codebook to reduce sampling cost, and requires no complex channel estimation or prior channel information. Then the proposed scheme concentrates the incident beams toward the desired reflection direction through a phase shift superposition algorithm. Numerical simulations verify the excellent performance and a 90% sample reduction of the proposed scheme compared with existing methods under the condition of 1-bit RIS hardware for practical applications. Furthermore, a measurement campaign in 5G commercial networks is conducted to validate the advantages of the proposed MIBS scheme in optimizing crucial signal metrics, yielding a 6.76 dB RSRP gain, a 4.9 dB SINR gain and a 37% throughput improvement, showing great potential of RIS for coverage enhancement.

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, signal detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to improve robustness against discrepancies between the time-varying channel conditions encountered during training and those experienced during testing. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.

The fast time-varying channels in high-speed scenarios impact signal transmission between transceivers and pose challenges to both the accuracy and bandwidth utilization of communication systems. Semantic communication, known for its ability to significantly reduce transmission bandwidth and enhance communication reliability, is especially effective in extreme environments. However, current semantic communication systems lack a comprehensive physical layer design, which limits their ability to achieve optimal performance in rapidly changing conditions. In this paper, we propose an adaptive semantic speech recognition and cloning transmission system with a superimposed pilot (SwitchAC-SIP) tailored for high-speed scenarios to ensure high-quality speech transmission. The system converts speech signals into textual content and speaker timbre features at the transmitter, while a speech cloning model reconstructs the speech at the receiver with a timbre closely resembling the original speaker based on these features, thereby eliminating the need to retrain the speech generation model for different users, ensuring both transmission quality and efficiency. To address the impact of high-speed environments on channel estimation performance, we introduce a superimposed pilot (SIP) in the physical layer. This method superimposes pilots and data across the entire time-frequency grid with a specific power ratio, significantly mitigating the detrimental effects of high-speed conditions on semantic communication systems. Furthermore, to enhance system flexibility in dynamic scenarios, we design a channel-adaptive network that dynamically allocates bandwidth ratios for text and audio semantics based on real-time channel conditions. This adaptive approach prioritizes the protection of critical semantic features according to user requirements. Simulation results demonstrate the substantial improvements in transmission efficiency and accuracy achieved by the proposed system.

In terahertz (THz) communication systems, extremely large scale arrays can effectively compensate for the limited communication distance problem. In this article, we consider the near-field channel estimation (CE) problem for an extremely large reconfigurable intelligence surface (XL-RIS)-assisted multi-user THz communication system. We first construct a near-field channel model based on a second-order Fresnel approximation derivation. Utilizing the spatial structure of the derived channel model, we sample the covariance matrix of the received signals. Then, we propose a tensor decomposition-based algorithm to estimate the angular parameters, and establish a truncated singular value decomposition (T-SVD) algorithm for the distance estimation. In the end, we estimate the path losses through the least squares (LS) method and recover the complete channel. Moreover, to further reduce the computational overhead, we construct a low-complexity tensor completion-based scheme for the angular parameters’ estimation. Simulation results indicate that the proposed tensor-based CE schemes outperform the conventional subspace-based approaches in terms of accuracy and computational complexity.

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as three-dimensional (3D) maneuverability, flexible deployment, and line-of-sight (LoS) air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground user equipments (UEs), by jointly optimizing the 3D UAV swarm placement positions, their individual MAs’ positions (or local positions), and receive beamforming for different UEs. To gain useful insights, we first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves inter-UE interference (IUI)-free communication when the uniform plane wave (UPW) model holds, where the UAV swarm forms a uniform sparse array (USA) satisfying minimum safe distance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.

Reconfigurable intelligent surfaces (RISs) offer new possibilities for manipulating wireless propagation environments. However, existing studies on RIS-assisted multiple-input multiple-output (MIMO) channel modeling are limited in their ability to explicitly separate and reveal the RIS-induced effects within the cascaded channel. This paper investigates the modeling and measurement of RIS-assisted MIMO channels from a spatial decomposition perspective. We propose a novel stochastic channel modeling framework based on the Weichselberger model, which decomposes the channel into a controllable RIS-induced component and an uncontrollable scattering component. This formulation reveals the RIS’s capability to reshape the spatial coupling structure of MIMO channels and enables the analysis of eigenmode control through spatial correlation and phase configuration. To experimentally validate the model, we develop a channel measurement and separation scheme using a multi-state RIS that can switch among reflective, absorptive, and antenna states. This setup allows for direct extraction of RIS channel component and supports segmented channel measurements. Measurement results show that the RIS-induced and environmental scattering components exhibit distinct power angular spectra and coupling matrices, and their superposition accurately reconstructs the global channel behavior. Additionally, the measured RIS coupling matrix closely matches the one calculated by the theoretical model, with a correlation matrix distance of 0.0394. These results confirm the effectiveness of the proposed model and measurement strategy, offering new insights into RIS-enabled spatial channel customization for next-generation wireless systems.

Reconfigurable intelligent surface (RIS)-enabled systems have been widely considered as one of the revolutionary technologies for the new generation of communications. In particular, RIS-assisted multiple-input multiple-output (MIMO) communications have come at the forefront of research, yet there is still lack of supportive channel measurements and modeling in real environments. Against this background, this paper conducts multi-frequency and multi-scenario channel measurements and channel modeling for RIS-assisted MIMO communication systems. Utilizing a temporal autocorrelation-based channel sounder and the fabricated RISs, the virtual RIS-assisted MIMO channels are realized by successively moving the transceiver antennas on movable rotary tables. The channel realizations are collected in indoor hotspot (InH) and urban microcellular (UMi) scenarios at sub-6 GHz and millimeter-wave (mmWave) frequency bands, respectively, where various communication states, different coding schemes of the RIS, as well as with and without RIS deployment are fully considered. Based on the measured channel realizations, critical channel metrics including the signal power, effective rank, spectral efficiency (SE), root-mean-square delay-spread (RMS DS), RiceanK-factor (KF), spatial correlation, etc., are illustrated and compared under different coding schemes, communication states, deployment scenarios, and frequency bands. The measurement results indicate that the coding scheme of the RIS significantly impacts the channel performance, while the capability of RIS to customize the channel is strongly related to the power intensity it provides. Deploying an RIS with energy-focused coding can provide significant signal power gains for non-line-of-sight (NLoS) links and spatial multiplexing gains for line-of-sight (LoS) and obstructed-line-of-sight (OLoS) links, thereby greatly improving the SE. Moreover, it is found that under different communication states, such as RIS-assisted NLoS/LoS/OLoS links, the influence imposed by RIS on the channel metrics could be completely opposite, especially for the KF, RMS DS, and effective rank. In addition, after the RIS deployment with a beamforming mode, the spatial correlation in the NLoS and LoS MIMO channels significantly increases and decreases, respectively. Furthermore, it is verified that the Weichselberger model can provide a satisfactory prediction accuracy on the SE of RIS-assisted MIMO channels, while the Kronecker model underestimates it.

This paper presents channel measurement campaigns and spatial channel characterization of reconfigurable intelligent surface (RIS)-assisted millimeter-wave multiple-input multiple-output (MIMO) systems. Utilizing a channel sounder and an RIS, the RIS-assisted MIMO channels are constructed in an indoor non-line-of-sight scenario. By rotating a narrow-beam directional antenna (DA) in the azimuth angle domain, the spatial signal distributions are captured. Meanwhile, multiple comparative experiments, including: wideband vs narrowband (NB) and DA vs omnidirectional antenna, are conducted. The influence of RIS deployment and different coding schemes is considered. Based on such channel realizations, spatial channel metrics, including the power azimuth spectrum (PAS), root mean square angular spread (RMS AS), root mean square delay spread (RMS DS), spatial correlation coefficient (SCC), effective rank, and etc., are thoroughly investigated. Measurement results show that, using the DA, NB signal, and beamforming provided by the RIS contributes to a lower RMS DS, a higher SCC, and a lower effective rank. A truncated Laplacian function is used to describe the PAS, indicating prominent signal strength improvements in both the primary direction facing RIS and the opposite direction to RIS. The RMS ASs are well-fitted by a generalized extreme value distribution, which manifest a distance-dependent variation in the space domain.

Intelligent reflecting surface (IRS) emerges as a promising technology to enhance wireless communication in recent years. However, the applications of deep learning algorithms within RIS-aided communication systems often suffer performance degradation under extreme conditions owing to a reliance on manual trial-and-error attempts. In this paper, the proposed beamforming neural network architecture search (BNAS) framework automates the design of of neural networks for the joint optimization of precoding vectors and IRS phase shift vectors. To improve robustness and performance, a specialized search space, incorporating two cascading supernets with selectable channel routes, diverse topological connections, and varied operations, is meticulously crafted for beamforming tasks. Meanwhile, the integration of neural tangent kernel theory, supported by alternative optimization guidance and bayesian optimization, not only enhances interpretability but also improves efficiency, thus enabling a more systematic and insightful search process compared to conventional approaches. Extensive numerical simulations confirm the applicability of BNAS, demonstrating superior performance compared to existing deep learning-based methods and traditional algorithms, particularly in challenging scenarios.

This paper investigates an intelligent reflecting surface (IRS)-aided wireless-powered communication network (WPCN) for maximizing the weighted sum rate (WSR). To reduce the complexity of traditional model-driven algorithms and improve convergence in data-driven deep learning approaches, a novel hybrid block coordinate descent (BCD) algorithm motivated by the dilation extraction and context attention (DECA) neural network (NN) is proposed. Specifically, the WSR maximization problem is firstly reformulated as a more tractable form, enabling the BCD algorithm to efficiently optimize the decoupled variables within the constraints. Meanwhile, at each BCD iteration, the DECA NN accelerates IRS phase shift optimization by facilitating the majorization-minimization (MM) algorithm to solve the computationally intensive fractional programming problem. Moreover, by leveraging dilation convolution and high-speed attention mechanisms, the DECA NN significantly outperforms existing deep learning benchmarks in both precision and convergence speed. Numerical results show that the proposed hybrid framework delivers performance comparable to the traditional BCD algorithm with dramatically reduced time consumption, while consistently maintaining robust performance under imperfect CSI and exhibiting strong transferability across diverse communication scenarios.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for wireless communication systems due to its ability to manipulate electromagnetic waves. With advantages such as low hardware complexity and low power consumption, RIS shows significant potential in positioning applications. This paper presents an RIS-based wireless signal sensing method that operates under the constraint of passive reflection while leveraging the space-time coding capabilities of RIS. By applying a space-time coding matrix on the RIS, the beamspace domain and the angle of arrival (AoA) of signals incident on the RIS can be efficiently estimated, requiring only the processing of single-channel received signals at the access point. Building upon this, a positioning prototype system utilizing two 27 GHz millimeter-wave RIS panels is developed and implemented, supporting real-time user positioning. Experimental results demonstrate that the prototype system achieves centimeter-level positioning accuracy, with errors below 10 cm in 97.22% of measurement cases, thereby validating the effectiveness of the proposed sensing and positioning scheme. These findings may pave the way for further exploration of RIS-based integration of sensing and communication technologies.

Diffractive deep neural network (D2NN), also known as reconfigurable intelligent metasurface based deep neural networks (Rb-DNNs) or stacked intelligent metasurfaces (SIMs) in wireless communications, has emerged as a promising signal processing paradigm that enables computing-by-propagation. However, existing architectures are limited to implementing specific functions such as precoding and combining, while still relying on digital baseband modules for other essential tasks like modulation and detection. In this work, we propose a baseband-free end-to-end (BBF-E2E) wireless communication system where modulation, beamforming, and detection are jointly realized through the propagation of electromagnetic (EM) waves. The BBF-E2E system employs D2NNs at both the transmitter and the receiver, forming an autoencoder architecture optimized within a complex-valued neural network (CVNN) framework. The transmission coefficients of each metasurface layer are trained using the mini-batch stochastic gradient descent (SGD) to minimize the cross-entropy loss. To reduce computational complexity during diffraction calculation, the angular spectrum method (ASM) is adopted over the Rayleigh–Sommerfeld formula. Extensive simulations demonstrate that BBF-E2E achieves robust symbol transmission under various channel conditions with significantly reduced hardware requirements. In particular, the proposed system matches the performance of a conventional multi-antenna system with 81 RF chains while requiring only a single RF chain and 1024 passive elements of metasurfaces. These results highlight the potential of this wave-domain neural computing paradigm to replace digital baseband modules in future wireless transceivers.

The mid-band frequency range, combined with extra-large-scale antenna arrays (ELAA), is emerging as a critical enabler for future communication systems. However, deploying mid-band ELAA systems presents significant challenges due to the high complexity and overhead associated with signal processing tasks such as channel state information (CSI) acquisition. This paper introduces an efficient transmission framework that incorporates a distributed hardware architecture, distributed channel modeling, and a dual time-scale transmission protocol. Building upon this framework, a practical implementation is proposed, leveraging the discrete Fourier transform (DFT)-based radio frequency (RF) front-ends and linear receivers as the hardware foundation. Additionally, a novel transmission strategy is developed, exploiting both statistical and instantaneous CSI. The proposed framework includes approximations of the ergodic spectral efficiency (SE) to guide DFT beam selection based on statistical CSI. Furthermore, two user scheduling strategies are introduced, utilizing statistical CSI and location information, respectively, with angular division implemented in a distributed manner. Reduced-dimensional instantaneous CSI is then employed for both local and centralized processing. To support system design, the proposed transmission strategy’s ergodic SE performance is analyzed, focusing on the DFT RF front-end and the eigenvalue characteristics of channel correlation matrices. Numerical results reveal that the proposed framework and transmission strategy achieve SE comparable to fully-digital architectures, while significantly reducing overhead and complexity.

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.

High-precision ranging plays a crucial role in future 6G Integrated Sensing and Communication (ISAC) systems. To improve the ranging performance while maximizing the resource utilization efficiency, future 6G ISAC networks have to reuse data payload signals for both communication and sensing, whose inherent randomness may deteriorate the ranging performance. To address this issue, this paper investigates the power allocation (PA) design for an OFDM-based ISAC system under random signaling, aiming to reduce the ranging sidelobe level of both periodic and aperiodic auto-correlation functions (P-ACF and A-ACF) of the ISAC signal. Towards that end, we first derive the closed-form expressions of the average squared P-ACF and A-ACF, and then propose to minimize the expectation of the integrated sidelobe level (EISL) under arbitrary constellation mapping. We then rigorously prove that the uniform PA scheme achieves the global minimum of the EISL for both P-ACF and A-ACF. As a step further, we show that this scheme also minimizes the P-ACF sidelobe level at every lag. Moreover, we extend our analysis to the P-ACF case with frequency-domain zero-padding, which is a typical approach to improve the ranging resolution. We reveal that there exists a tradeoff between sidelobe level and mainlobe width, and employ the Dinkelbach’s method to seek a globally optimal PA scheme that reduces the EISL. Finally, we validate our theoretical findings through extensive simulation results, confirming the effectiveness of the proposed PA methods in reducing the ranging sidelobe level for random OFDM signals.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

Recent advancements in foundation models (FMs) have attracted increasing attention in the wireless communication domain. Leveraging the powerful multi-task learning capability, FMs hold the promise of unifying multiple tasks of wireless communication with a single framework. Nevertheless, existing wireless FMs face limitations in the uniformity to address multiple tasks with diverse inputs/outputs across different communication scenarios. In this paper, we propose a MUlti-taSk Environment-aware FM (MUSE-FM) with a unified architecture to handle multiple tasks in wireless communications, while effectively incorporating scenario information. Specifically, to achieve task uniformity, we propose a unified prompt-guided data encoder-decoder pair to handle data with heterogeneous formats and distributions across different tasks. Besides, we integrate the environmental context as a multi-modal input, which serves as prior knowledge of environment and channel distributions and facilitates cross-scenario feature extraction. Simulation results illustrate that the proposed MUSE-FM outperforms existing methods for various tasks, and its prompt-guided encoder-decoder pair facilitates few-shot adaptation to new task configurations. Moreover, the incorporation of environment information improves the ability to adapt to different scenarios.

Reconfigurable intelligent surface (RIS) has garnered significant attention in wireless communications due to its electromagnetic wave manipulation capabilities. In order to ensure the effectiveness of various transmission designs in practical environment, the channel characteristics of RIS-assisted wireless communication systems warrant thorough investigation. While substantial research has been conducted on large-scale channel characteristics, there remains a lack of measurement-based studies on channel small-scale characteristics. This paper conducts channel measurement campaigns, analyzing delay characteristics and multi-cluster properties in multiple indoor and outdoor environments, including square, corridor, and classroom scenarios. The measurement results indicate that the RIS-assisted path (i.e. the virtual line-of-sight (VLOS) path) makes the channels exhibit two-cluster characteristics analogous to conventional line-of-sight channels. Moreover, in classroom scenario, VLOS path generates reflected echoes that degrade the channel delay characteristics. Building on these findings, this paper proposes a small-scale channel modeling framework comprising a two-cluster-based channel impulse response (CIR) model and a K-factor-based CIR model. The simulation and measurement results demonstrate satisfactory agreement, validating the effectiveness and practicality of the proposed framework. Two types of CIR models that are unified with the traditional CIR model form, along with an optional echo component, make this framework flexible and easy to use. The proposed small-scale modeling framework can effectively characterize the power delay profiles of RIS-assisted channels, providing References for future research on RIS-assisted wireless communications.

Large language models (LLMs) have achieved remarkable success across a wide range of tasks, particularly in natural language processing and computer vision. This success naturally raises an intriguing yet unexplored question: Can LLMs be harnessed to tackle channel state information (CSI) compression and feedback in massive multiple-input multiple-output (MIMO) systems? Efficient CSI feedback is a critical challenge in next-generation wireless communication. In this paper, we pioneer the use of LLMs for CSI compression, introducing a novel framework that leverages the powerful denoising capabilities of LLMs—capable of error correction in language tasks—to enhance CSI reconstruction performance. To effectively adapt LLMs to CSI data, we design customized pre-processing, embedding, and post-processing modules tailored to the unique characteristics of wireless signals. Extensive numerical results demonstrate the promising potential of LLMs in CSI feedback, opening up possibilities for this research direction.

Reconfigurable Intelligent Surface (RIS) is a promising technology with the potential to tailor wireless channels to specific communication needs. In RIS-assisted communications, channel estimation has long been a challenge due to the passive nature and the large number of RIS elements. In this paper, we introduce a novel deep learning-based downlink channel estimation framework, named PPNet, which facilitates efficient pilot transmission. The core innovation of PPNet lies in the joint design and optimization of pilot signals from the base station and phase shifts from the RIS, both of which are represented using neural networks, together with the channel estimation module. Simulation results show that the proposed framework significantly improves the estimation performance with limited pilot overhead.

Accurate channel state information (CSI) is critical for realizing the full potential of multiple-antenna wireless communication systems. While deep learning (DL)-based CSI feedback methods have shown promise in reducing feedback overhead, their generalization capability across varying propagation environments remains limited due to their data-driven nature. Existing solutions based on online training improve adaptability but impose significant overhead in terms of data collection and computational resources. In this work, we propose AdapCsiNet, an environment-adaptive DL-based CSI feedback framework that eliminates the need for online training. By integrating environmental information—represented as a scene graph—into a hypernetwork-guided CSI reconstruction process, AdapCsiNet dynamically adapts to diverse channel conditions. A two-step training strategy is introduced to ensure baseline reconstruction performance and effective environment-aware adaptation. Simulation results demonstrate that AdapCsiNet achieves up to 46.4% improvement in CSI reconstruction accuracy and matches the performance of online learning methods without incurring additional runtime overhead.

Reconfigurable intelligent surfaces (RISs) are widely recognized as a cutting-edge technology for the sixth-generation mobile communication systems (6G). In this paper, we investigate the effective channel gain and spatial multiplexing gain of multi-RIS-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) channels. A channel model for multi-RIS-assisted MIMO system is introduced. We propose two discrete phase design methods for multi-RIS-assisted MIMO systems. The factors influencing spatial multiplexing gain, including the power difference and spatial similarity between different virtual line-of-sight (VLOS) paths, are thoroughly analyzed. Numerical simulations and measurement experiments are conducted in three scenarios, analyzing the impact of the RIS unit number as well as the positions of the RISs and antennas. The results indicate that jointly considering the position information of multiple RISs in discrete phase design leads to a higher effective channel gain, whereas relying only on the position of each single RIS exhibits instability. These findings highlight the positive role of sufficient location information and joint phase design in multi-RIS-assisted systems. Moreover, the results demonstrate that reduced power difference and spatial similarity among VLOS paths enhance the effective rank of the cascaded mmWave MIMO channel.

Large AI models (LAMs) offer new opportunities for wireless intelligence, but their deployment in latency- and resource-constrained systems remains challenging. To explore this in the context of channel state information (CSI) feedback for frequency-division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems, we propose a novel framework, OLAMCF, that integrates LAMs via offline codebook optimization, thereby avoiding the need for real-time inference. Specifically, the large vision model (LVM) at the core of this framework is built upon a vision-based backbone, pre-trained on large-scale image datasets and fine-tuned with site-specific CSI. This strategy allows this framework to capture the structural similarity between CSI and image to refine codewords from the conventional codebook and generate customized codebooks tailored to the specific environments. Simulation results show that our approach significantly outperforms existing schemes in both reconstruction accuracy and system throughput, without introducing additional inference latency or computational overhead. This design philosophy—extracting the best offline and discarding the rest online—offers a practical perspective on integrating LAMs into communication systems.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

Millimeter-wave (mmWave) 5G New Radio (NR) communication systems, with their high-resolution antenna arrays and extensive bandwidth, offer a transformative opportunity for high-throughput data transmission and advanced environmental sensing. Although passive sensing-based SLAM techniques can estimate user locations and environmental reflections simultaneously, their effectiveness is often constrained by assumptions of specular reflections and oversimplified map representations. To overcome these limitations, this work employs a mmWave 5G NR system for active sensing, enabling it to function similarly to a laser scanner for point cloud generation. Specifically, point clouds are extracted from the power delay profile estimated from each beam direction using a binary search approach. To ensure accuracy, hardware delays are calibrated with multiple predefined target points. Pose variations of the terminal are then estimated from point cloud data gathered along continuous trajectory viewpoints using point cloud registration algorithms. Loop closure detection and pose graph optimization are subsequently applied to refine the sensing results, achieving precise terminal localization and detailed radio map reconstruction. The system is implemented and validated through both simulations and experiments, confirming the effectiveness of the proposed approach.

Wi-Fi sensing has gained substantial attention in recent years, particularly for indoor positioning applications. Conventional indoor wireless positioning methods typically assume access to complete channel state information (CSI), which is often impractical in real-world systems. This paper proposes a novel approach for indoor positioning utilizing a compressed beamforming feedback matrix (BFM), which is inherently integrated into the Wi-Fi protocol for meeting CSI feedback requirements. We introduce FPNet, a joint neural network model, in which the BFM is compressed into codewords by an encoder at the client station (STA) side. These codewords are subsequently transmitted to the access point (AP) side, which features a decoder and a positioning network responsible for reconstructing the codewords and estimating positions. The encoder and decoder are trained end-to-end. FPNet is implemented with standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental results demonstrate that this approach not only improves net throughput by up to 22.92% but also achieves positioning accuracy exceeding 97%.

Mid-band extra large-scale massive multiple-input multiple-output (XL-MIMO) systems are regarded as potential enablers in future communication systems, which are also trapped in high complexity and channel state information (CSI) acquisition overhead. In this paper, an efficient distributed XL-MIMO structure is first considered, and a novel transmission strategy is proposed by utilizing the joint instantaneous and statistical CSI. Specifically, a user scheduling scheme based on user locations is first presented. Subsequently, approximations of ergodic spectral efficiency (SE) are derived, serving as the basis of analog beamforming. Additionally, the signal processing at the local units and the central unit is carried out utilizing instantaneous CSI. Numerical results demonstrate that the proposed distributed XLMIMO structure and transmission strategy, leveraging angular division in a distributed manner, are capable of achieving SE comparable to that of a fully digital structure.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Spectrum sharing in Device-to-Device (D2D) communications with power control and link scheduling is a challenging non-convex combinatorial optimization problem. The state-of-the-art model-based iterative algorithms such as FPLinQ produce optimum-achieving solutions, whilst deep learning-based approaches have been proposed recently to approximate FPLinQ with reduced computational complexity. However, due to the highly non-convex nature of the optimization problem, FPLinQ exhibits certain deficiencies in the highly interference-limited networks, as it may be trapped within certain local sub-optimal solutions that may be far from the global optimum. To address these issues, we propose to unfold FPLinQ, with certain parameters inside the iterative procedure adjusted by a graph reinforcement learning (GRL) method, and end up with a novel hybrid model/data-driven approach, termed UFPLinQ. Not only does UFPLinQ inherit the advantages of FPLinQ and GRL with respect to local optimality, explainability, scalability, and generalizability, but it also provides excellent solutions in interference-limited networks where FPLinQ fails. By numerical evaluations, UFPLinQ outperforms existing learning-based power control mechanisms, with substantially reduced training samples and iterations, and more interestingly remedies the potential deficiencies of FPLinQ in highly interference-limited networks.

Future 6G Integrated Sensing and Communication (ISAC) networks are expected to reuse data payload signals for both communication and sensing. However, the inherent randomness of these signals can degrade ranging accuracy. To address this challenge, this paper studies power allocation (PA) strategies for CP-OFDM-based ISAC systems operating under random signaling, with the goal of reducing the sidelobe levels in the periodic auto-correlation function (P-ACF) of the ISAC signal. Specifically, we first derive closed-form expressions for the average squared P-ACF, and then formulate an optimization problem that minimizes the expected integrated sidelobe level (EISL) under arbitrary constellation mappings. We rigorously prove that, across all constellations, a uniform PA scheme yields the lowest ranging sidelobe levels, both in terms of the EISL and at each individual lag. Additionally, we extend our analysis to scenarios involving frequency-domain zero-padding. In such cases, we show that uniform PA no longer guarantees optimal sidelobe suppression. To address this, we propose a projected gradient descent (PGD) algorithm to find a locally optimal PA scheme that minimizes the EISL. Finally, our theoretical results are substantiated by extensive simulations, which confirm the effectiveness of the proposed PA methods in suppressing the ranging sidelobe levels of random OFDM signals.

The low-altitude economy has emerged as a critical focus for future economic development, emphasizing the urgent need for flight activity surveillance utilizing the existing sensing capabilities of mobile cellular networks. Traditional monostatic or localization-based sensing methods, however, encounter challenges in fusing sensing results and matching channel parameters. To address these challenges, we propose an innovative approach that directly draws the radio images of the low-altitude space, leveraging its inherent sparsity with compressed sensing (CS)based algorithms and the cooperation of multiple base stations. Furthermore, recognizing that unmanned aerial vehicles (UAVs) are randomly distributed in space, we introduce a physicsembedded learning method to overcome off-grid issues inherent in CS-based models. Additionally, an online hard example mining method is incorporated into the design of the loss function, enabling the network to adaptively concentrate on the samples bearing significant discrepancy with the ground truth, thereby enhancing its ability to detect the rare UAVs within the expansive low-altitude space. Simulation results demonstrate the effectiveness of the imaging-based low-altitude surveillance approach, with the proposed physics-embedded learning algorithm significantly outperforming traditional CS-based methods under off-grid conditions.

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for enhancing wireless coverage and transmission rates while reducing hardware costs and power consumption. This work addresses the limitations of separately optimizing RIS deployment and beamforming by proposing a unified joint deployment and beamforming framework tailored for multi-user multi-input single-output systems. By formulating RIS control as a Markov decision process, we develop a deep reinforcement learning framework that integrates a graph neural network to exploit the inherent topology of wireless communication networks. To reduce the action space and improve learning efficiency, the framework leverages discrete Fourier transform codebooks. Simulation results demonstrate that the proposed approach achieves up to a twofold improvement in weighted sum rate compared to fixed RIS deployment strategies, all while eliminating the need for explicit cascaded channel estimation and accurate channel model.

Reconfigurable intelligent surface (RIS) has gained significant attention as an innovative solution for enhancing communication system performance. This paper proposes a novel fluid RIS-assisted communication system with spatial adaptability, where the RIS is mounted on a slide rail. By leveraging both spatial flexibility and phase reconfigurability, the proposed system overcomes the limitations of conventional fixed-position RIS. To characterize the spatial variations introduced by RIS movement, we establish a novel channel modeling framework that combines near-field spherical wave propagation with practical visibility region functions. Experimental results reveal that dynamic RIS position yields received power gain variations ranging from 0.4 dB to 5.3 dB. This empirical evidence strongly validates the capability of fluid RIS to counteract channel non-stationarity.

Reconfigurable intelligent surface (RIS) is regarded as one of the promising technologies for the sixth-generation mobile communication systems (6G) due to its ability to reshape the wireless channel and improve the communication transmission rate. In this paper, we investigate the spatial characteristic of an RIS-assisted millimeter wave (mmWave) multiple-input multipleoutput (MIMO) channel through channel measurement in practical environment. The channel measurement campaigns are carried out in an indoor hotspot (InH) non-line-of-sight (NLOS) scenario at 35 GHz. We compare and analyze the channel parameters in three propagation modes, including intelligent reflection with RIS (IRWR), specular reflection with RIS (SRWR), and without RIS (WR). Different types of receiver (RX) antenna are compared, including the horn antenna and the omni-directional antenna. Signals with bandwidths of 300 MHz and 10 MHz are transmitted to compare the spatial characteristics of wideband and narrowband signals. The measured channel frequency response (CFR) reveals that the RIS coding scheme, antenna type, and signal bandwidth are significant factors influencing the spatial correlation of the wireless channel. The measured power azimuth spectrum (PAS), fitted by truncated Laplacian functions, demonstrates that the deployment of RIS with energy-focused phase configuration results in an amplification of the power in the primary signal propagation direction. Moreover, the analysis of the root mean square (RMS) angular spread shows that it increases with the growing distance between the RX and RIS.

This paper investigates the downlink channel estimation problem for frequency division duplex (FDD) multi-user massive multiple-input multiple-output (MIMO) systems. We model this problem within the sparse Bayesian learning (SBL) framework, where all unknowns are treated as random variables. Due to limited scattering at the base station, the channel exhibits sparsity in the angular domain. By introducing Gaussian mixture priors to characterize the user equipment internal sparsity and partially shared sparsity, we develop a Markov chain Monte Carlo (MCMC) method to implement Bayesian inference and accurately estimate all random variables in the model, including the channel matrix. Experimental results demonstrate that the MCMC-based SBL channel estimation algorithm outperforms existing approaches by over 5 dB in multi-user scenarios while reducing pilot overhead.

The superimposed pilot (SIP) transmission scheme shows great potential for improving spectral efficiency in MIMO-OFDM systems. However, it also introduces complex challenges for receiver design, particularly due to pilot contamination and data interference. To address these issues, the joint channel estimation, signal detection, and decoding (JCDD) framework has emerged as a promising solution, utilizing iterative refinement to enhance receiver performance. Despite this, existing JCDD methods either focus heavily on theoretical analysis, often neglecting practical application scenarios, or experience performance limitations due to inherent design flaws. In this paper, we propose an advanced iterative JCDD receiver that effectively mitigates the negative effects of pilot contamination and data interference. Our approach improves traditional linear minimum mean-square error (LMMSE) channel estimation by incorporating state-of-the-art techniques—specifically variational message passing (VMP) and deep learning (DL)—allowing for better adaptation to varying channel conditions. Extensive empirical evaluations demonstrate that our proposed SIP receiver not only surpasses the conventional orthogonal pilot (OP) scheme but also exhibits outstanding adaptability in mismatched channel environments, thanks to the VMP and DL-based improvements.

In the near-field region of extremely large-scale multi-input multi-output (XL-MIMO) systems, efficient channel estimation is a critical challenge, often demanding substantial computational resources. This issue becomes even more pressing in dynamic, time-varying environments where both users and scatterers are in motion, necessitating lower computational complexity for real-time channel estimation. In this paper, we propose an innovative solution for near-field XL-MIMO systems with time-varying channels, introducing a fast and accurate channel estimation and tracking scheme inspired by keypoint detection techniques from computer vision. First, we design and train a high-precision, anchor-free channel keypoint detector (CKDet) using a fine-grained orthogonal matching pursuit (OMP) framework as an effective channel estimator. Building on this, we present a novel conditional cascaded OMP-based channel tracking scheme that exploits spatial correlations between consecutive time slots to significantly reduce computational complexity. After obtaining the keypoint locations at all time slots, we apply the Hungarian algorithm to match users and scatterers across all time slots, enabling the construction of motion trajectories for use in environmental sensing applications. Experimental results validate the proposed channel tracking algorithm, showcasing its superior performance, speed, and resilience across a range of signal-to-noise ratios (SNR).

Reconfigurable intelligent surface (RIS) technology has gained wide recognition for its ability of enhancing the performance of wireless communication systems. This paper investigates the energy efficiency (EE) of a discrete phase shift RIS-assisted multi-cell communication system. Assuming that only statistical channel state information is available, we maximize the EE under a practical measured-based RIS power consumption model. Approximating the discrete measured-based RIS power consumption model by a continuous differentiable function, the problem is addressed by jointly optimizing the transmit beamforming at the base stations and the RIS phase shift matrix, by using fractional programming and complex circle manifold techniques. Simulation results demonstrate that the proposed algorithm can effectively achieve near-optimal EE performance and low RIS power consumption.

The radio frequency fingerprint identification (RFFI) is promising which exploits the hardware defects inherent to realize the zero-trust Internet of Things (IoT) security. Considering the scalability, the data imbalance and the training overhead for the current deep learning (DL) based RFFI, in this paper, we combine the large language model (LLM) and propose a BERT-LightRFFI framework, to enhance the zero-trust edge IoT security. Specifically, we pre-train a BERT model with the unlabeled data via self-supervised learning and obtain a powerful RFF feature extractor. Then, we use the knowledge distillation to inherit the BERT learn-gene to the lightweight BERT-Light model, and then fine-tune the BERT-Light model and a classifier with a few-shot labeled wireless data. In the experiments, we use a large-scale real-world LoRa dataset to evaluate the performances of the proposed framework and propose the interesting insights. The results prove the effectiveness of the proposed framework, achieving an accuracy of 97.52% with less model parameters and computation amount in the presence of multipath fading and Doppler effect, which is better than the benchmark methods.

In this paper, a wideband extremely large-scale multiple-input multiple-output (XL-MIMO) system is considered, and an efficient wideband near-field channel estimation algorithm is proposed. Due to the non-negligible near-field effect and beam squint effect in wideband XL-MIMO systems, the spatial domain sparsity of near-field channel matrix is broken, and thus traditional estimation algorithms become inapplicable. Meanwhile, the large number of antennas and wide bandwidth will lead to greatly high computational complexity. To decrease the computational complexity, the parametric symmetry of wideband XL-MIMO array is studied, and the extra freedom of beam squint effect in near-field region is utilized. Then, the parametric symmetry-based multiple-antenna joint (PSMJ) channel estimation algorithm is proposed to estimate the wideband near-field channel with low computational complexity. Simulation results proves that the proposed PSMJ algorithm has superior performance with lower complexity for wideband XL-MIMO systems.

This paper investigates the challenge of heterogeneous multi-agent reinforcement learning (MARL) algorithms in wireless local area networks (WLANs), where multiple stations utilize either value-based or policy-based reinforcement learning algorithms for channel access. Specifically, we propose a novel heterogeneous MARL training framework, named QPMIX, which adopts a centralized training with decentralized execution paradigm to enable heterogeneous agents to collaborate. Our method aims to maximize the network throughput and ensure fairness among stations, enhancing the overall performance of WLANs. Through the simulation results, we demonstrate that the proposed QPMIX algorithm achieves higher throughput, lower mean delay, reduced delay jitter, and decreased collision rates than conventional CSMA/CA in the saturated traffic scenario. Additionally, it can better promote cooperation between heterogeneous agents compared to independent learning.

Intelligent fault diagnosis (IFD) plays a crucial role in ensuring the safe operation of industrial machinery and improving production efficiency. However, traditional supervised deep learning methods require a large amount of training data and labels, which are often located in different clients. Additionally, the cost of data labeling is high, making labels difficult to acquire. Meanwhile, differences in data distribution among clients may also hinder the model’s performance. To tackle these challenges, this paper proposes a semi-supervised federated learning framework, SSFL-DCSL, which integrates dual contrastive loss and soft labeling to address data and label scarcity for distributed clients with few labeled samples while safeguarding user privacy. It enables representation learning using unlabeled data on the client side and facilitates joint learning among clients through prototypes, thereby achieving mutual knowledge sharing and preventing local model divergence. Specifically, first, a sample weighting function based on the Laplace distribution is designed to alleviate bias caused by low confidence in pseudo labels during the semi-supervised training process. Second, a dual contrastive loss is introduced to mitigate model divergence caused by different data distributions, comprising local contrastive loss and global contrastive loss. Third, local prototypes are aggregated on the server with weighted averaging and updated with momentum to share knowledge among clients. To evaluate the proposed SSFL-DCSL framework, experiments are conducted on two publicly available datasets and a dataset collected on motors from the factory. In the most challenging task, where only 10% of the data are labeled, the proposed SSFL-DCSL can improve accuracy by 1.15% to 7.85% over state-of-the-art methods.

The deployment of multiple reconfigurable intelligent surfaces (RISs) enhances the propagation environment by improving channel quality, but it also complicates channel estimation. Following the conventional wireless communication system design, which involves full channel state information (CSI) acquisition followed by RIS configuration, can reduce transmission efficiency due to substantial pilot overhead and computational complexity. This study introduces an innovative approach that integrates CSI acquisition and RIS configuration, leveraging the channel-altering capabilities of the RIS to reduce both the overhead and complexity of CSI acquisition. The focus is on multi-RIS-assisted systems, featuring both direct and reflected propagation paths. By applying a fast-varying reflection sequence during RIS configuration for channel training, the complex problem of channel estimation is decomposed into simpler, independent tasks. These fast-varying reflections effectively isolate transmit signals from different paths, streamlining the CSI acquisition process for both uplink and downlink communications with reduced complexity. In uplink scenarios, a positioning-based algorithm derives partial CSI, informing the adjustment of RIS parameters to create a sparse reflection channel, enabling precise reconstruction of the uplink channel. Downlink communication benefits from this strategically tailored reflection channel, allowing effective CSI acquisition with fewer pilot signals. Simulation results highlight the proposed methodology’s ability to accurately reconstruct the reflection channel with minimal impact on the normalized mean square error while simultaneously enhancing spectral efficiency.

Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under sudden attenuation. Since acknowledgment feedback is subject to long propagation delays when retransmission is unavoidable, a novel error detection method is proposed to roughly detect semantic errors at the regenerative satellite. With the proposed detectors at both the satellite and the gateway, the quality of the received images can be ensured. The simulation results demonstrate that the proposed method can significantly reduce bandwidth requirements, adapt to complex satellite scenarios, and protect semantic information with an acceptable transmission delay.

As a promising paradigm for the sixth-generation (6G) networks, task-oriented semantic communication significantly enhances transmission efficiency. However, it faces complex security challenges, particularly the risk of eavesdropping due to the open nature of wireless channels. To address this issue, we propose a secure semantic communication framework that integrates physical-layer secure beamforming (SBF) to safeguard semantic information from eavesdropping. Specifically, we design an SBF network to generate SBF vectors that focus signal beams on legitimate users to enhance signal power while directing designed artificial noise toward potential eavesdroppers to strengthen jamming. To further improve system adaptability across varying channel conditions, we introduce attention-based channel-aware modules that dynamically optimize the encoding, decoding, and beamforming processes based on perceived channel state information. Furthermore, task-oriented artificial noise is employed to degrade the task performance of eavesdroppers more effectively. Finally, a stepwise training strategy with task-specific loss functions is employed to jointly optimize the SBF and semantic modules, maximizing the performance gap in downstream tasks between legitimate users and eavesdroppers. The simulation results demonstrate that the proposed approach effectively maintains task performance for legitimate users while significantly suppressing eavesdroppers, outperforming conventional methods.

With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardware, the detection task is sometimes deployed on remote computing devices or the cloud to execute complex algorithms, which brings massive data transmission overhead. In response, this paper proposes an optical flow-driven semantic communication framework for the stereo-vision 3D object detection task. The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy. Specifically, we develop an optical flow-driven module to jointly extract and recover semantics from the left and right images to reduce the loss of the left-right photometric alignment semantic information and improve the accuracy of depth inference. Then, we design a 2D semantic extraction module to identify and extract semantic meaning around the objects to enhance the transmission of semantic information in the key areas. Finally, a fusion network is used to fuse the recovered semantics, and reconstruct the stereo-vision images for 3D detection. Simulation results show that the proposed method improves the detection accuracy by nearly 70% and outperforms the traditional method, especially for the low signal-to-noise ratio regime.

In the domain of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is crucial. This paper proposes RIS-CoCsiNet, a novel deep learning-based framework aimed at significantly enhancing feedback efficiency. The proposed method leverages the inherent correlation among neighboring user equipments (UEs) by categorizing RIS-UE CSI information into two parts: shared information among nearby UEs and unique information specific to each individual UE. By exploiting the correlation in RIS-UE CSI, redundant transmission of shared information can be substantially reduced, thereby minimizing the overhead associated with repeatedly feeding back this shared data. Unlike conventional autoencoder-based CSI feedback frameworks, our approach incorporates an additional decoder and a combination neural network (NN) at the base station. These components recover the shared information from the feedback CSI of two neighboring UEs and combine it with the individual information, respectively, without requiring any modifications at the UEs. Through end-to-end learning, the encoders at neighboring UEs are trained to collaboratively feedback shared information while independently feeding back the unique information. For UEs equipped with multiple antennas, a baseline NN architecture with long short-term memory (LSTM) modules is introduced to capture the correlation among nearby antennas. Additionally, since the RIS-UE CSI phase is not sparse, we propose magnitude-dependent phase feedback strategies that incorporate statistical or instantaneous CSI magnitude information into the phase feedback process. Extensive simulations across two diverse channel datasets validate the effectiveness of RIS-CoCsiNet.

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optimized once and applied over fixed sensing durations, limiting their ability to adapt to different targets and reducing sensing accuracy. To overcome these limitations, this study proposes an advanced wireless communication system that multiplexes downlink signals for environmental sensing and introduces an intelligent recognizer powered by deep learning techniques. Specifically, we design a novel neural network based on the long short-term memory architecture and the physical channel model. This network iteratively captures and fuses information from previous measurements, adaptively customizing RIS phases to gather the most relevant information for the recognition task at subsequent moments. These configurations are dynamically adjusted according to scene, task, target, and quantization priors. Furthermore, the recognizer includes a decision-making module that dynamically allocates different sensing durations, determining whether to continue or terminate the sensing process based on the collected measurements. This approach maximizes resource utilization efficiency. Simulation results demonstrate that the proposed method significantly outperforms state-of-the-art techniques while minimizing the impact on communication performance, even when sensing and communication occur simultaneously. Part of the source code for this paper can be accessed athttps://github.com/kiwi1944/CRISense.

Artificial intelligence (AI)-based channel state information (CSI) prediction, aimed at enhancing CSI accuracy and reducing overhead, has shown significant advancements over traditional model-based prediction methods. Despite these advantages, its practical deployment has been hindered by unreliable prediction performance due to AI instability. This study introduces a reliable AI-based CSI prediction framework by implementing a proxy-based performance monitoring mechanism. Specifically, we deploy a lightweight proxy at the user equipment (UE), trained via knowledge distillation to accommodate the UE’s limited capacities. This proxy mimics the output of the CSI prediction network at the base station (BS) side, enabling the UE to monitor the accuracy of the predicted CSI and prevent undesirable outcomes. To overcome the deployment challenges in operational systems, we detail the practical implementation procedures of our proposed method, covering both offline training and online operation phases. Simulation results show that our proxy-based monitor can achieve over 90% consistency with the CSI prediction network at the BS side and avoid over 85% of unsatisfactory prediction outcomes under various practical considerations, demonstrating remarkable generalization capabilities across different configurations.

The deployment of multiple reconfigurable intelligent surfaces (RIS) is a promising strategy to enhance wireless system performance. However, joint beamforming in multi-RIS assisted systems faces significant challenges due to the increased number of optimization variables, non-convex objective functions, and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and the successive convex approximation algorithm, maximizing the weighted sum rate in a double-RIS assisted downlink multi-user multiple-input single-output system. We also present a general framework for model-driven deep learning that addresses the limitations of existing methods, which often lack flexibility to different channels and suffer from a large training burden due to the high-dimensional action space of deep reinforcement learning (DRL). Initially, we configure the step size in the proposed algorithm as trainable, accelerating convergence. Then, a recurrent neural network generates the step size for iterations, allowing dynamic iteration extension in varying environmental conditions. We enhance the neural network’s self-adaptability by introducing a model-driven DRL algorithm, integrating expert knowledge into the DRL actor network’s design. Simulation results demonstrate up to 30% performance improvement over traditional algorithms, achieved by our model-driven framework. The proposed model-driven DRL shows higher capacity for dynamic extension and rapid adaptation to new environments.

Rainfall impacts daily activities and can lead to severe hazards such as flooding. Traditional rainfall measurement systems often lack granularity or require extensive infrastructure. While the attenuation of electromagnetic waves due to rainfall is well-documented for frequencies above 10 GHz, sub-6 GHz bands are typically assumed to experience negligible effects. However, recent studies suggest measurable attenuation even at these lower frequencies. This study presents the first channel state information (CSI)-based measurement and analysis of rainfall attenuation at 2.8 GHz. The results confirm the presence of rain-induced attenuation at this frequency, although classification remains challenging. The attenuation follows a power-law decay model, with the rate of attenuation decreasing as rainfall intensity increases. Additionally, rainfall onset significantly increases the delay spread, and slight Doppler effects were also observed following the onset of precipitation. Building on these insights, we propose RainGaugeNet, the first CSI-based rainfall classification model in the sub-6GHz band that leverages multipath and temporal features. Two variants are developed: RainGaugeNet-R using ResNet1D and RainGaugeNet-T using a Transformer encoder. Using only 20 seconds of CSI data, RainGaugeNet-R achieves up to 95% an average classification accuracy in line-of-sight(LoS) scenarios and 85% in non-line-of-sight(NLoS) conditions. RainGaugeNet-T attains 90% accuracy in LoS and 99% in NLoS settings, demonstrating superior robustness. Both models significantly outperform state-of-the-art baselines while maintaining low computational complexity.

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

In this paper, we investigate the channel estimation and user localization problems for multi-user integrated sensing and communication (ISAC) systems empowered by the reconfigurable intelligent surface (RIS) technology. In order to perceive environmental information more deeply, we propose a spherical RIS architecture with spherically arranged unit cells. Based on the principle of phase mode excitation, we customize the design of RIS profiles and recover the equivalent channel parameters via subspace estimation tools. By exploring the characteristics of RIS array manifold and free-space propagation, we develop a decoupling framework of three-dimensional channel parameters, which is not supported by conventional planar RIS topologies. Each user can achieve a self-localization by analyzing the signals transmitted from other active users. Simulation results indicate that the spherical RIS can enable joint channel estimation, user localization and data transmission with remarkable performance that approaches the theoretical Cramér-Rao bounds.

In this paper, we investigate the channel estimation and localization problems for integrated sensing and communication (ISAC) systems empowered by the reconfigurable intelligent surface (RIS) technology. We propose a cylindrical RIS architecture that arranges reflecting elements on a curved substrate, where the three-dimensional array manifold can not only offer a 360° coverage but also perceive the environmental information more deeply. The conformal RIS topology can fit the deployment scenarios more flexibly, which, however, incurs a potential issue of shadowing effect, i.e., signal waves from/to certain directions can only be observed by a part of reflectors due to the shielding of the substrate curvature, yielding different visibility regions (VRs) for multiple users on the RIS array manifold. In order to address this problem, we propose a tensorial channel estimation approach, where the cascaded channel is transformed into the beamspace domain and modeled as a canonical polyadic tensor. By leveraging the principle of tensor completion, we can eliminate the RIS training profiles to deconstruct the channel in the element domain. Then, we develop a VR detection strategy based on the sliding windows, retrieving equivalent channel parameters from the effective signal responses. Finally, by exploring the characteristics of the cylindrical RIS architecture, we develop a decoupling framework to uniquely recover the exact channel parameters, based on which each user can locate itself and other interacting ones. Simulation results indicate that the proposed cylindrical RIS can enable the channel estimation, user localization and data transmission simultaneously, exhibiting remarkable performance under the shadowing effect interference.

Deep learning has been widely applied to channel estimation (CE), yielding significant performance improvements. However, existing research primarily focuses on static channel scenarios, leading to substantial performance degradation in dynamic environments. Furthermore, the use of fixed pilot patterns fails to adequately capture channel dynamics, resulting in unnecessary pilot overhead. In this study, we propose a Vision Transformer-based joint pilot design (PD) and CE network (PD-CEViT) for orthogonal frequency division multiplexing (OFDM) systems. The PD module leverages maximum Doppler shift and delay spread information to determine pilot positions, effectively capturing channel variations in dynamic scenarios. To further improve CE accuracy and robustness across diverse environments, the coarse CE from the PD module is passed to a CE module that utilizes a Vision Transformer (ViT), forming the joint PD-CEViT structure. Additionally, we introduce a pilot number switch network, named SwitchPD-CEViT, which dynamically adjusts between different PD-CEViT configurations based on the current channel conditions. This strategy balances network performance and pilot overhead, accommodating varying pilot requirements across different scenarios. Simulation results demonstrate that our proposed structure more effectively tracks channel variations compared to fixed pilot patterns. Even under challenging conditions with large Doppler shifts and delay spreads, our method significantly outperforms traditional and deep learning approaches in terms of mean square error (MSE) performance. Moreover, the integration of channel information further enhances estimation performance and robustness. Meanwhile, SwitchPD-CEViT achieves superior CE performance with reduced pilot overhead by efficiently managing pilot utilization.

In this paper, we study the transmit and receive beamforming vectors in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) performs the downlink communication with user equipments (UEs) and active sensing tasks simultaneously. While reflected signals are utilized for passive sensing at the receive access points (RAPs). We adopt different fusion strategies based on the backhaul capacity between the RAPs and BS. Specifically, in the scenarios with unlimited backhaul capacity, the sensing signals received by the BS and RAPs are forwarded to the central controller (CC) for signal fusion. In contrast, in the scenarios with limited backhaul capacity, the BS and each RAP make independent decisions and transmit their binary inference results to the CC for result fusion. Furthermore, we explore two cases of the signal-to-interference-plus-noise ratio (SINR) with and without the sensing interference cancellation (Case-1 SINR and Case-2 SINR). By optimizing the beamforming vectors according to different fusion strategies, we aim to maximize sensing performance while ensuring the minimum SINR requirement for the UEs subject to the power budget at the BS. Finally, numerical results demonstrate that the proposed beamforming optimization schemes can reach the upper bound of performance under both fusion strategies. It is also shown that adding the sensing signals generally improve the sensing performance in Case-1 SINR.

To reduce the computational complexity of near-field channel estimation for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, the concept ofvirtual cell subarraysin subarray hybrid precoding architectures, is firstly given. Multiple subarrays with strong correlation can be flexibly and dynamically combined, to jointly extract relevant features of the channels. Based on this, an undersampling matching and oversampling refinement pursuit (UMORP) algorithm is proposed, which can detect the channel parameters of cell subarrays through an undersampling dictionary constructed by analog phase shifters. This approach facilitates the estimation of the channel of a single cell subarray with much fewer pilots and lower hardware capability requirement. Then, a multiple-path decoupled spatial extrapolation (MPDSE) scheme is proposed for fully dimensional channel reconstruction, which orthogonally decouples multiple paths from the received signal of a single cell subarray firstly, and then utilize the spatial correlation between adjacent cell subarrays to extrapolate the channels with low cost. Moreover, to further reduce the computational complexity in XL-MIMO systems, a spatial multiple cell subarrays joint extrapolation (SMCJE) scheme is also proposed. Based on the spatial geometry among cell subarrays, only several cell suabrrays are utilized to jointly estimate the distances and reconstruct the fully dimensional near-field channel, achieving a low level of computational complexity. Our simulation results verify that the proposed schemes perform better in XL-MIMO systems, while requiring fewer pilots and exhibiting much lower computational complexity.

In this paper, we propose a novel scheme to estimate the six-dimensional (6D) motion parameters of the dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of the dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of the dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target’s radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with a massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of the dynamic target.

Integrated sensing and communication (ISAC) in millimeter wave is a key enabler for next-generation networks, which leverages large bandwidth and extensive antenna arrays, benefiting both communication and sensing functionalities. The associated high costs can be mitigated by adopting a hybrid beamforming structure. However, the well-studied monostatic ISAC systems face challenges related to full-duplex operation. To address this issue, this paper focuses on a three-dimensional bistatic configuration that requires only half-duplex base stations. To intuitively evaluate the error bound of bistatic sensing using orthogonal frequency division multiplexing waveforms, we propose a positioning scheme that combines angle-of-arrival and time-of-arrival estimation, deriving the closed-form expression of the position error bound (PEB). Using this PEB, we develop two hybrid beamforming algorithms for joint waveform design, aimed at maximizing achievable spectral efficiency (SE) while ensuring a predefined PEB threshold. The first algorithm leverages a Riemannian trust-region approach, achieving superior performance in terms of SE and convergence speed compared to the conventional gradient-based methods, but with higher complexity. In contrast, the second algorithm, which employs orthogonal matching pursuit, offers a more computationally efficient solution, delivering reasonable SE while maintaining the PEB constraint. Numerical results are provided to validate the effectiveness of the proposed designs.

In this paper, we investigate the impact of random wobbling on the performance of unmanned aerial vehicle (UAV) communications. We consider two practical implementation forms of UAV-multiple input multiple output (MIMO) system, namely 1) centralized implementation, where a compact multi-antenna array is deployed on a single UAV platform and hence wobbling directly has impact on the entire array; 2) distributed implementation, where a virtual MIMO system is formed via cooperative UAV swarm, and wobbling occurs independently for every single transmit antenna. For both cases, we define and analyze the beamforming gain loss-factor as the performance metric to evaluate the adverse effect caused by wobbling. Based on derived analytical expressions, the centralized and distributed implementations are compared to show their preferable operating scenarios, respectively, considering different system and implementation parameters including the carrier frequency, wobbling variance, user position, antenna number, and inter-antenna spacing, etc. It is revealed that the distributed MIMO implementation could be more robust to wobbling, especially when the number of antennas is large. Simulations verify the accuracy of derived analytical expressions, and confirm the corresponding conclusions.

Simultaneous localization and mapping (SLAM) plays a critical role in integrated sensing and communication (ISAC) systems for sixth-generation (6G) millimeter-wave (mmWave) networks, enabling environmental awareness and precise user equipment (UE) positioning. While cooperative multi-user SLAM has demonstrated potential in leveraging distributed sensing, its application within multi-modal ISAC systems remains limited, particularly in terms of theoretical modeling and communication-layer integration. This paper proposes a novel multi-modal SLAM framework that addresses these limitations through three key contributions. First, a Bayesian estimation framework is developed for cooperative multi-user SLAM, along with a two-stage algorithm for robust radio map construction under dynamic and heterogeneous sensing conditions. Second, a multi-modal localization strategy is introduced, fusing SLAM results with camera-based multi-object tracking and inertial measurement unit (IMU) data via an error-aware model, significantly improving UE localization in multi-user scenarios. Third, a sensing-aided beam management scheme is proposed, utilizing global radio maps and localization data to generate UE-specific prior information for beam selection, thereby reducing inter-user interference and enhancing downlink spectral efficiency. Simulation results demonstrate that the proposed system improves radio map accuracy by up to 60%, enhances localization accuracy by 37.5%, and significantly outperforms traditional methods in both indoor and outdoor environments.

Device-to-device (D2D) spectrum sharing in wireless communications is a challenging non-convex combinatorial optimization problem, involving entangled link scheduling and power control in a large-scale network. The state-of-the-art methods, either from a model-based or a data-driven perspective, exhibit certain limitations such as the critical need for channel state information (CSI) and/or a large number of (solved) instances (e.g., network layouts) as training samples. To advance this line of research, we propose a novel hybrid model/data-driven spectrum sharing mechanism with graph reinforcement learning for link scheduling (GRLinQ), injecting information theoretical insights into machine learning models, in such a way that link scheduling and power control can be solved in an intelligent manner. Through an extensive set of experiments, GRLinQ demonstrates superior performance to the existing model-based and data-driven link scheduling and/or power control methods, with a relaxed requirement for CSI, a substantially reduced number of unsolved instances as training samples, a possible distributed deployment, reduced online/offline computational complexity, and more remarkably excellent scalability and generalizability over different network scenarios and system configurations.

The advance of topological interference management (TIM) has been one of the driving forces of recent developments in network information theory. However, state-of-the-art coding schemes for TIM are usually handcrafted for specific families of network topologies, relying critically on experts’ domain knowledge and sophisticated treatments. The lack of systematic and automatic generation of solutions inevitably restricts their potential wider applications to wireless communication systems, due to the limited generalizability of coding schemes to wider network configurations. To address such an issue, this work makes the first attempt to advocate revisiting topological interference alignment (IA) from a novel learning-to-code perspective. Specifically, we recast the one-to-one and subspace IA conditions as vector assignment policies and propose a unifying learning-to-code on graphs (LCG) framework by leveraging graph neural networks (GNNs) for capturing topological structures and reinforcement learning (RL) for decision-making of IA beamforming vector assignment. Interestingly, the proposed LCG framework is capable of recovering known one-to-one scalar/vector IA solutions for a significantly wider range of network topologies, and more remarkably of discovering new subspace IA coding schemes for multiple-antenna cases that are challenging to be handcrafted. The extensive experiments demonstrate that the LCG framework is an effective way to automatically produce systematic coding solutions to the TIM instances with arbitrary network topologies, and at the same time, the underlying learning algorithm is efficient with respect to online inference time and possesses excellent generalizability and transferability for practical deployment.

The increasing demands for spectral resources in emerging massive machine-type communication applications necessitate the implementation of massive grant-free transmission in the millimeter-wave (mmWave) band. This paper proposes two efficient receive analog beamforming design algorithms for mmWave massive grant-free transmission under hybrid beamforming architectures, intending to optimize spectral efficiency and access probability, respectively. Specifically, we first express the spectral efficiency of mmWave massive grant-free transmission systems and then derive an analytically tractable approximation using the random matrix theory. Following this, an alternating optimization method is employed to design the receive beamforming matrix efficiently. Additionally, we provide the formulation of access probability for mmWave massive grant-free transmission, whose explicit expression is approximately derived through the Gaussian approximation. Building upon this, we utilize a convex hull relaxation-based optimization method to optimize the beamforming matrix. The effectiveness of our proposed beamforming design algorithms in improving spectral efficiency and access probability is validated through extensive simulation experiments.

In pursuit of enhanced quality of service and higher transmission rates, communication within the mid-band spectrum, such as bands in the 6-15 GHz range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is considered a potential enabler for future communication systems. However, the characteristics introduced by mid-band XL-MIMO systems pose challenges for channel modeling and performance analysis. In this paper, we first analyze the potential characteristics of mid-band MIMO channels. Then, an analytical channel model incorporating novel channel characteristics is proposed, based on a review of classical analytical channel models. This model is convenient for theoretical analysis and compatible with other analytical channel models. Subsequently, based on the proposed channel model, we analyze key metrics of wireless communication, including the ergodic spectral efficiency (SE) and outage probability (OP) of MIMO maximal-ratio combining systems. Specifically, we derive closed-form approximations and performance bounds for two typical scenarios, aiming to illustrate the influence of mid-band XL-MIMO systems. Finally, comparisons between systems under different practical configurations are carried out through simulations. The theoretical analysis and simulations demonstrate that mid-band XL-MIMO systems excel in SE and OP due to the increased array elements, moderate large-scale fading, and enlarged transmission bandwidth.

Deploying an extremely large-scale reconfigurable intelligent surface (XL-RIS) can significantly improve the performance of a RIS-assisted communication system. However, the array aperture and deployment of the XL-RIS affects the radiated field region in which the base station (BS) and the user are located, which in turn affects the performance improvement. In this paper, we have jointly optimized a deployment scheme and phase-shift matrix in XL-RIS-aided communication system, aiming to maximize the user’s received signal-to-noise ratio (SNR). Firstly, we incorporate the far-field and near-field channel into a unified far- or near-field (FoN) model to simplify the SNR analysis and optimization on RIS deployments. Secondly, based on the FoN approach, we derive an expression for the user’s received SNR and formulate an optimization problem to jointly optimize the RIS deployment and phase-shift matrix in order to maximize the user’s received SNR. Thirdly, we summarize the relationship between the RIS array aperture and deployment and the radiated field region in which the BS and the user are located, and propose an optimized closed-form solution for the RIS deployment and phase-shift matrix. Finally, we validate the effectiveness of the proposed scheme through simulation results.

With the increasing demand for spectrum efficiency and energy efficiency, reconfigurable intelligent surfaces (RISs) have attracted massive attention due to its low-cost and capability of controlling wireless environment. However, there is still a lack of treatments to deal with the growth of the number of users and RIS elements, which may incur performance degradation or computational complexity explosion. In this paper, we investigate the joint optimization of user scheduling and precoding for distributed RIS-aided communication systems. Firstly, we propose an optimization-based numerical method to obtain suboptimal solutions with the aid of the approximation of ergodic sum rate. Secondly, to reduce the computational complexity caused by the high dimensionality, we propose a data-driven scalable and generalizable multi-agent deep reinforcement learning (MADRL) framework with the aim to maximize the ergodic sum rate approximation through the cooperation of all agents. Further, we propose a novel dynamic working process exploiting the trained MADRL algorithm, which enables distributed RISs to configure their own passive precoding independently. Simulation results show that our algorithm substantially reduces the computational complexity by a time reduction of three orders of magnitude at the cost of 3% performance degradation, compared with the optimization-based method, and achieves 6% performance improvement over the state-of-the-art MADRL algorithms.

This paper investigates a multiuser millimeter-wave (mmWave) uplink system in which each user is equipped with a multi-antenna fluid antenna system (FAS) while the base station (BS) has multiple fixed-position antennas. Our primary objective is to maximize the system capacity by optimizing the transmit covariance matrices and the antenna position vectors of the users jointly. To gain insights, we start by deriving upper bounds and approximations for the capacity. Then we delve into the capacity maximization problem. Beginning with the simple scenario of a single user equipped with a single-antenna FAS, we demonstrate that a closed-form optimal solution exists when there are only two propagation paths between the user and the BS. In the case where multiple propagation paths are present, a near-optimal solution can also be obtained through a one-dimensional search method. Expanding our focus to multiuser cases, in which users are equipped with either single- or multi-antenna FAS, we show that the original capacity maximization problems can be reformulated into distinct rank-one programmings. Then, we propose alternating optimization algorithms to deal with the transformed problems. Simulation results indicate that FAS can improve the capacity of the multiple access channel (MAC) greatly, and the proposed algorithms outperform all the benchmarks.

We define a graph-based rate optimization problem and consider its computation, which provides a unified approach to the computation of various theoretical limits, including the (conditional) graph entropy, rate-distortion functions and capacity-cost functions with side information. Compared with their classical counterparts, theoretical limits with side information are much more difficult to compute since their characterizations as optimization problems have larger and more complex feasible regions. Following the unified approach, we develop effective methods to resolve the difficulty. On the theoretical side, we derive graph characterizations for rate-distortion and capacity-cost functions with side information and simplify the characterizations in special cases by reducing the number of decision variables. On the computational side, we design an efficient alternating minimization algorithm for the graph-based problem, which deals with the inequality constraint by a flexible multiplier update strategy. Moreover, simplified graph characterizations are exploited and deflation techniques are introduced, so that the computing time is greatly reduced. Theoretical analysis shows that the algorithm converges to an optimal solution. By numerical experiments, the accuracy and efficiency of the algorithm are illustrated and its significant advantage over existing methods is demonstrated.

A novel beam tracking approach is proposed to realize reliable air-to-ground (A2G) transmissions with reduced pilot overhead and time delay. The proposed beam tracking strategy consists of two stages, namely the model-driven channel tracking and the model-data dual driven hybrid beamforming (HBF). For the model-driven channel tracking, the angle-of-arrivals/angle-of-departures (AoAs/AoDs) are predicted by leveraging the regularity of the three-dimensional flight track and attitude, as well as the A2G geometrical information with temporal correlations. Then, the high-dimensional channel matrix estimation problem is converted to the low-dimensional multipath components parameters estimation tasks, which substantially reduces the pilot overhead. The proposed model-data dual-driven HBF module unfolds the iterative HBF algorithms and introduces a set of trainable parameters, which brings in both low complexity and high interpretability. To further improve the HBF robustness against imperfect channel state information, the denoise neural network is employed to exploit spatial-domain channel correlations for improved channel accuracy. Numerical results unveil that: 1) the proposed model-driven channel tracking scheme achieves satisfying normalized mean square error of the tracked A2G channel with significantly reduced pilot overhead; and 2) the proposed model-data dual-driven HBF algorithm is superior to the conventional counterparts in terms of reliability and robustness.

Massive multiple-input multiple-output (MIMO) technology has significantly enhanced spectral and power efficiency in cellular communications and is expected to further evolve towards extra-large-scale MIMO. However, centralized processing for massive MIMO faces practical obstacles, including excessive computational complexity and a substantial volume of baseband data to be exchanged. To address these challenges, decentralized baseband processing has emerged as a promising solution. This approach involves partitioning the antenna array into clusters with dedicated computing hardware for parallel processing. In this paper, we investigate the gradient-based Markov chain Monte Carlo (MCMC) method—an advanced MIMO detection technique known for its near-optimal performance in centralized implementation—within the context of a decentralized baseband processing architecture. This decentralized design mitigates the computation burden at a single processing unit by utilizing computational resources in a distributed and parallel manner. Additionally, we integrate the mini-batch stochastic gradient descent method into the proposed decentralized detector, achieving remarkable performance with high efficiency. Simulation results demonstrate substantial performance gains of the proposed method over existing decentralized detectors across various scenarios. Moreover, complexity analysis reveals the advantages of the proposed decentralized strategy in terms of computation delay and interconnection bandwidth when compared to conventional centralized detectors.

In recent years, reconfigurable intelligent surfaces (RISs) have attracted significant attention from both academia and industry due to their ability to manipulate electromagnetic waves in wireless communication. In particular, the characteristics of wireless channels incorporating RISs offer substantial research value. Several previous studies have investigated the path loss characteristics of RIS-assisted wireless channels in practical scenarios. However, these pioneering studies ignore the effect of RIS array gain on path loss. This paper proposes a floating-intercept (FI) path loss model that includes the array gain of the RIS, the distance between the transmitter and the RIS, and the distance between the receiver and the RIS. In this RIS-FI model, the RIS array gain component is related to the reflection coefficients of all the unit cells of RIS. The proposed model can effectively characterize path loss in both the far-field and near-field regions of the RIS. A fabricated 512-element RIS and a vector network analyzer (VNA) are utilized to set up the channel measurement system. The proposed RIS-FI path loss model is validated by using measurement data collected from multiple scenarios, including outdoor square, indoor corridor, and classroom. Compared to other RIS path loss models, the experimental results show that the proposed model offers better accuracy in describing path loss in the near-field region of the RIS. The RIS-FI path loss model presented in this paper may pave the way for further research on RIS-assisted wireless channels.

Space-borne multi-angle polarization remote sensing is considered to be one of the most important tools to obtain global aerosol parameters in assessment of climate change. Accurate calibration is a prerequisite for quantitative polarization remote sensing. However, most research focuses on the polarization calibration methods and the monitoring of the calibration coefficient stability. Few studies investigate the polarization calibration verification of the subsequent new satellite sensors. The Directional Polarimetric Camera (DPC) onboard the Chinese Terrestrial Ecosystem Carbon Inventory Satellite (abbreviated as TECIS, with the Chinese name ”Gou Mang”) is a brand-new polarization sensor. To evaluate the polarization calibration of this sensor, we propose a verification scheme for polarization calibration, which can verify both the degree of polarization (DoP) and polarized reflectance. The accuracy of DoP is verified by using sunglint on the ocean. When detecting the sunglint, constraints such as observational geometry, cloud identification, and wind speed are introduced, and the polarized reflectance is atmospherically corrected according to the marine aerosol model and aerosol optical depth from MODIS. The accuracy of polarized reflectance at the Top of Atmosphere (TOA) is verified based on AERONET inversion products and the Bidirectional Polarization Distribution Functions (BPDF). Experiments show that the accuracies of DoP of the three polarization channels (490, 670, 865 nm) of DPC are 5.57%, 2.07%, and 1.97% respectively, and the average accuracy of the multi-angle polarized reflectance at the TOA of the 865nm channel is 0.209%.

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC):What is the optimal communication-centric ISAC waveform for ranging?Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

Stand-alone perception systems in autonomous driving suffer from limited sensing ranges and occlusions at extended distances, potentially resulting in catastrophic outcomes. To address this issue, collaborative perception is envisioned to improve perceptual accuracy by using vehicle-to-everything (V2X) communication to enable collaboration among connected and autonomous vehicles and roadside units. However, due to limited communication resources, it is impractical for all units to transmit sensing data such as point clouds or high-definition video. As a result, it is essential to optimize the scheduling of communication links to ensure efficient spectrum utilization for the exchange of perceptual data. In this work, we propose a deep reinforcement learning-based V2X user scheduling algorithm for collaborative perception. Given the challenges in acquiring perceptual labels, we reformulate the conventional label-dependent objective into a label-free goal, based on characteristics of 3D object detection. Incorporating both channel state information (CSI) and semantic information, we develop a double deep Q-Network (DDQN)-based user scheduling framework for collaborative perception, named SchedCP. Simulation results verify the effectiveness and robustness of SchedCP compared with traditional V2X scheduling methods. Finally, we present a case study to illustrate how our proposed algorithm adaptively modifies the scheduling decisions by taking both instantaneous CSI and perceptual semantics into account.

Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms have gained substantial popularity in FL to reduce the communication overhead, where each client conducts multiple localized iterations before communicating with a central server. In this paper, we focus on FL where the clients have diverse computation and/or communication capabilities. Under this circumstance, FedAvg can be less efficient since it requires all clients that participate in the global aggregation in a round to initiate iterations from thelatestglobal model, and thus the synchronization among fast clients andstraggler clientscan severely slow down the overall training process. To address this issue, we propose an efficient asynchronous federated learning (AFL) framework calledDelayed Federated Averaging (DeFedAvg). In DeFedAvg, the clients are allowed to perform local training with different stale global models at their own paces. Theoretical analyses demonstrate that DeFedAvg achieves asymptotic convergence rates that are on par with the results of FedAvg for solving nonconvex problems. More importantly, DeFedAvg is the first AFL algorithm that provably achieves the desirablelinear speedupproperty, which indicates its high scalability. Additionally, we carry out extensive numerical experiments using real datasets to validate the efficiency and scalability of our approach when training deep neural networks.

Reconfigurable intelligent surfaces (RISs) offer the unique capability to reshape the radio environment, thereby simplifying transmission schemes traditionally contingent on channel conditions. Joint spatial division and multiplexing (JSDM) emerges as a low-overhead transmission scheme for multi-user equipment (UE) scenarios, typically requiring complex matrix decomposition to achieve block-diagonalization of the effective channel matrix. In this study, we introduce an innovative JSDM design that leverages RISs to customize channels, thereby streamlining the overall procedures. By strategically positioning RISs at the discrete Fourier transform (DFT) directions of the base station (BS), we establish orthogonal line-of-sight links within the BS-RIS channel, enabling a straightforward pre-beamforming design. Based on UE grouping, we devise reflected beams of the RIS with optimized directions to mitigate inter-group interference in the RISs-UEs channel. An approximation of the channel cross-correlation coefficient is derived and serves as a foundation for the RISs-UEs association, further diminishing inter-group interference. Numerical results substantiate the efficacy of our RIS-customized JSDM in not only achieving effective channel block-diagonalization but also in significantly enhancing the sum spectral efficiency for multi-UE transmissions.

Thanks to the ability to customize the propagation of wireless signals, reconfigurable intelligent surfaces (RISs) have great potential in enhancing the performance of future wireless communication systems. While the majority of papers in the literature considers single-RIS scenarios, the potential deployment of multiple RISs, that offer ubiquitous connectivity for diverse user demands, calls for further investigation. This paper considers a downlink multi-group multicast system underpinned by multiple RISs and aims to maximize the sum spectral efficiency subject to an overall transmit power constraint. This optimization problem is highly challenging due to the non-convex, non-smooth, and non-differentiable properties of the objective function, as well as the non-convex unit modulus constraint. To address this complex problem, we propose a model-driven deep learning (DL) approach. This involves first solving the joint active and passive beamforming design through an alternating projected gradient (APG) algorithm with an approximate objective function. The APG algorithm is then unfolded into an iterative procedure using multiple layers with trainable parameters. A network training method is proposed to ensure that the performance improves with the number of iterations. Remarkably, our model is also nicely generalizable to the imperfect channel state information (CSI) scenario, without any change to the network architecture, by simply combining the recursive approximation method and adding some long/short-term trainable parameters to accommodate the two-timescale transmission protocol. Our simulation results demonstrate the superiority of our proposed DL method over existing algorithms in terms of both complexity and performance. Specifically, the proposed model-driven DL method reduces the runtime by approximately 80% compared to the APG algorithm and 99.97% compared to the majorization-minimization algorithm, while it also achieves comparable performance. Furthermore, our proposed method for imperfect CSI scenarios reduces the performance loss by 5%-10% compared to the proposed method without considering the influence of imperfect CSI.

Artificial intelligence (AI) has emerged as a promising tool in channel state information (CSI) feedback tasks. Although current research primarily focuses on improving feedback accuracy through innovative AI approaches, the reliability of these systems in real-world scenarios often goes overlooked. Specifically, a closer examination of the feedback accuracy of individual CSI samples reveals significant variations, underscoring the imperative need for performance monitoring of AI-based CSI feedback. Building upon this observation, we introduce a pragmatic framework for AI-based CSI feedback. This process involves assessing feedback accuracy (i.e., conducting performance monitoring) on the user side before transmitting the CSI codeword. In particular, this method utilizes a lightweight proxy decoder, trained via knowledge distillation, to emulate the mapping function of the original decoder at the base station. The goal is to generate, at the user end, CSI identical to that produced at the base station by the original, more powerful decoder, thus enable precise prediction of feedback accuracy. Simulation results demonstrate that our proposed performance monitoring method can precisely predict feedback accuracy with low complexity and accurately detect low-quality feedback samples with a detection rate of nearly 95%, ensuring reliable transmission.

Integrated sensing and communications (ISAC) has emerged as a transformative paradigm for next-generation wireless systems. In this paper, we present a novel ISAC scheme that leverages the diffusion Schr¨odinger bridge (DSB) to realize the sensing of electromagnetic (EM) property of a target as well as the reconstruction of the wireless channel. The DSB framework connects EM property sensing and channel reconstruction by establishing a bidirectional process: the forward process transforms the distribution of EM property into the channel distribution, while the reverse process reconstructs the EM property from the channel. To handle the difference in dimensionality between the high-dimensional sensing channel and the lower-dimensional EM property, we generate latent representations using an autoencoder network. The autoencoder compresses the sensing channel into a latent space that retains essential features, which incorporates positional embeddings to process spatial context. The simulation results demonstrate the effectiveness of the proposed DSB framework, which achieves superior reconstruction of the targets shape, relative permittivity, and conductivity. Moreover, the proposed method can also realize accurate channel reconstruction given the EM property of the target. The dual capability of accurately sensing the EM property and reconstructing the channel across various positions within the sensing area underscores the versatility and potential of the proposed approach for broad application in future ISAC systems.

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target’s EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target’s shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). By leveraging the Lippmann-Schwinger equation, we propose an EM property sensing method that reconstructs the RPCD using compressive sensing techniques. This approach exploits the joint sparsity of the EM property vector, which enables the proposed method to effectively handle the high dimensionality and ill-posed nature of the inverse scattering problem. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termedflexible reflector(FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement.

This paper introduces a novel smartphone-enabled localization technology for ambient Internet of Things (IoT) devices, leveraging the widespread use of smartphones. By utilizing the passive movement of a smartphone, we create a virtual large array that enables direct localization using only angle-of-arrival (AoA) information. Unlike traditional two-step localization methods, direct localization is unaffected by AoA estimation errors in the initial step, which are often caused by multipath channels and noise. However, direct localization methods typically require prior environmental knowledge to define the search space, with calculation time increasing as the search space expands. To address limitations in current direct localization methods, we propose a machine learning (ML)-based direct localization technique. This technique combines ML with an adaptive matching pursuit procedure, dynamically generating search spaces for precise source localization. The adaptive matching pursuit minimizes location errors despite potential accuracy fluctuations in ML across various training and testing environments. Additionally, by estimating the reflection source’s location, we reduce the effects of multipath channels, enhancing localization accuracy. Extensive three-dimensional ray-tracing simulations demonstrate that our proposed method outperforms current state-of-the-art direct localization techniques in computational efficiency and operates independently of prior environmental knowledge.

In ultra-dense autonomous aerial vehicle (AAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale AAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense AAV communication network, where the GUs’ service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel AAVs as a stochastic game, where each AAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the AAVs into a two-player game between a representative AAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs’ service demands exhibit higher temporal correlation or if the AAVs have wider observation capabilities over their nearby GUs.

In this paper, we propose a unified framework based on equivariance for the design of artificial intelligence (AI)-assisted technologies in multi-user multiple-input-multiple-output (MU-MIMO) systems. We first provide definitions of multidimensional equivariance, high-order equivariance, and multidimensional invariance (referred to collectively as tensor equivariance). On this basis, by investigating the design of precoding and user scheduling, which are key techniques in MU-MIMO systems, we delve deeper into revealing tensor equivariance of the mappings from channel information to optimal precoding tensors, precoding auxiliary tensors, and scheduling indicators, respectively. To model mappings with tensor equivariance, we propose a series of plug-and-play tensor equivariant neural network (TENN) modules, where the computation involving intricate parameter sharing patterns is transformed into concise tensor operations. Building upon TENN modules, we propose the unified tensor equivariance framework that can be applicable to various communication tasks, based on which we easily accomplish the design of corresponding AI-assisted precoding and user scheduling schemes. Simulation results show that the proposed methods achieve near-optimal performance with significantly lower complexity and strong generalization across multiple dimensions. For instance, the NN trained for precoding with 8 users provides satisfactory performance in a 10-user scenario. This validates the superiority of TENN modules and the unified framework.

Channel state information (CSI) acquisition is essential for the base station (BS) to fully reap the beamforming gain in intelligent reflecting surface (IRS)-aided downlink communication systems. Recently, Wang et al. (2020) revealed a strong correlation in different users’ cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property to reduce the overhead for both pilot and feedback transmission in IRS-aided downlink communication. Note that in the downlink, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users’ channels, which is in sharp contrast to the uplink counterpart considered in Wang et al. (2020). To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in frequency division duplex (FDD) IRS-aided downlink communication. Specifically, the users quantize and feed back their received pilot signals, instead of the estimated channels, to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging their correlation, similar to the uplink scenario. Under this protocol, we manage to propose efficient user-side quantization and BS-side channel estimation methods. Moreover, we analytically quantify the pilot and feedback transmission overhead to reveal the significant performance gain of our proposed scheme over the conventional “estimate-then-quantize” scheme.

Frequency mixing reconfigurable intelligent surface (FMx-RIS) is an innovative concept within the realm of RIS technology. It distinguishes itself from conventional RIS by continuously modifying the phase of incident electromagnetic waves, thereby inducing frequency shifts. By uniquely associating FMx-RIS elements with a specific frequency, this approach allows receivers to distinguish channels from each propagation path by detecting the frequency shifts. This decoupling feature enables channel estimation and augments diversity gain in the frequency domain. In this paper, we explore the architectural aspects of FMx-RIS in both single-carrier and multi-carrier systems to validate its practicality. For single-carrier systems, we derive closed-form lower bounds for achievable data rates under scenarios with perfect and imperfect channel state information, alongside the corresponding power scaling laws. For multi-carrier systems, our focus lies in the scheduling of reflective patterns for FMx-RISs, recognizing that frequency mixing operations introduce additional inter-carrier interference among users. We introduce two scheduling algorithms designed to maximize the minimum user signal-to-interference-plus-noise ratio. The numerical results confirm the analytical achievable rates and demonstrate that the FMx-RIS-aided system surpasses conventional RIS-aided systems. Furthermore, we assess the effectiveness of the proposed scheduling algorithms, where the ADMM-based scheme exhibits similar performance to the SoTA scheme.

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM aredelay pre-compensationandpath-based beamforming. However, existing research on DAM is mainly based on fully digital beamforming, which requires the number of radio frequency (RF) chains to be equal to the number of antennas. This paper proposes the hybrid analog/digital beamforming based DAM, including both fully and partially connected structures. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. While DAM was considered for the path-based channel model with integer delays in the previous work, this paper extends DAM to a more general tap-based model that accounts for fractional path delays. To further reduce the cost of channel estimation and improve the performance for wireless channels with fractional delays, DAM with codebook-based beam alignment and DAM-orthogonal frequency division multiplexing (DAM-OFDM) with hybrid beamforming are proposed. The effectiveness of the proposed techniques is verified by extensive simulation results.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

In low-altitude economy (LAE), the widespread use of various types of unmanned aerial vehicles (UAVs) could provide convenience and enhance efficiency. However, the existence of unauthorized or illegal UAVs would pose significant challenges to urban privacy and security. In this paper, we propose a networked integrated sensing and communications (ISAC) based UAV tracking and handover scheme towards LAE. We define avirtual sensing cell (VSC)where oneprimary base station (PBS)transmits sensing signals, while both the PBS and twosecondary base stations (SBS)receive echoes. Since the echoes contain the clutter of static environment, each base station (BS) would first filter out the clutter and then estimate the UAV’s horizontal angle, elevation angle, distance, and radial velocity with the multiple signal classification (MUSIC) algorithm. Next, we employ the centralized extended Kalman filter (EKF) to fuse the estimations from the three BSs and leverage the one-step prediction results of the EKF to distinguish and track multiple UAVs. When the UAV flies within the coverage of a VSC, we design aPBS handoverstrategy to select the optimal BS from three BSs as the new PBS in real-time. Moreover, we propose aVSC handoverstrategy to track the UAV continuously when it flies from one VSC to another. Simulation results demonstrate the effectiveness of the proposed scheme and provide valuable reference for UAV tracking and handover in LAE.

Along with the prosperity of generative artificial intelligence (AI), its potential for solving conventional challenges in wireless communications has also surfaced. Inspired by this trend, we investigate the application of the advanced diffusion models (DMs), a representative class of generative AI models, to high dimensional wireless channel estimation. By capturing the structure of multiple-input multiple-output (MIMO) wireless channels via a deep generative prior encoded by DMs, we develop a novel posterior inference method for channel reconstruction. We further adapt the proposed method to recover channel information from low-resolution quantized measurements. Additionally, to enhance the over-the-air viability, we integrate the DM with the unsupervised Stein’s unbiased risk estimator to enable learning from noisy observations and circumvent the requirements for ground truth channel data that is hardly available in practice. Results reveal that the proposed estimator achieves high-fidelity channel recovery while reducing estimation latency by a factor of 10 compared to state-of-the-art schemes, facilitating real-time implementation. Moreover, our method outperforms existing estimators while reducing the pilot overhead by half, showcasing its scalability to ultra-massive antenna arrays.

The Terahertz band (0.1-10 THz) is expected to meet Terabit-per-second (Tbps) data rate and high-precision sensing simultaneously, for which Terahertz integrated sensing and communication (THz ISAC) emerges as a promising technology for future wireless systems. However, fundamental challenges in the THz ISAC system include the severe Doppler effects due to the high carrier frequencies in the THz band. Delay-Doppler modulations such as orthogonal delay-Doppler division multiplexing (ODDM) modulation occur to overcome this challenge. In this paper, a general input/output relation of ODDM modulation is derived for the THz channel, which allows off-grid channel delay and Doppler shifts, and thus breaks the limit of sensing resolution imposed by the on-grid assumption in most existing studies. Then, a low-complexity multi-target estimation algorithm is proposed to achieve near optimal sensing accuracy. Simulation results show that the proposed algorithm is able to approach theoretical bounds and realize millimeter-level sensing. Moreover, ODDM has more reliable bit error rate performance than OFDM, especially in high Doppler spread scenarios.

Device-to-Device (D2D) link scheduling in wireless communications is a challenging non-convex combinatorial optimization problem. The state-of-the-art methods, either from a model-based or a data-driven perspective, exhibit certain limitations such as the critical need of Channel State Information (CSI) and a large number of instances or solved instances as training samples. To advance this line of research, we propose a novel hybrid model/data-driven approach with Graph Reinforcement Learning for Link Scheduling (GRLinQ), injecting information theoretical insights into machine leaning models. GRLinQ demonstrates superior performance to the existing model-based and data-driven link scheduling mechanisms, with a relaxed requirement of CSI, a smaller number of unsolved instances as training samples, a possible distributed deployment, and more remarkably an excellent generalization ability over different network scenarios and system configurations.

The advancement of millimeter-wave communication technology heralds new sensing capabilities. By leveraging channel multipath parameter estimates, we can harness simultaneous localization and mapping (SLAM) for precise user equipment (UE) localization and radio map construction in 6 G communication systems. Particularly in multi-UE scenarios, SLAM empowers base stations to amalgamate the local radio maps of various UEs efficiently. This study introduces a novel Bayesian framework specifically designed for multi-UE SLAM, complemented by a tailored factor graph. We also unveil a two-stage multi-UE SLAM algorithm. Our simulation results reveal that this algorithm substantially enhances radio map construction accuracy by $\mathbf{4 8. 5 \%}$ and UE localization accuracy by $13.5 \%$, outperforming single-UE cases. Moreover, the algorithm demonstrates remarkable adaptability to environmental changes, showcasing its potential for long-term evolution in dynamic settings.

In the near-field region of an extremely large-scale MIMO (XL MIMO) system, channel reconstruction can be solved by utilizing sparse parameter estimation after transforming the received pilots at the base station (BS) into Cartesian domain. However, the process of exhaustive search over the codebook consumes significant computational resources and running time, especially when dealing with a vast number of antennas. In this study, we visualize the sparse channel matrix in the Cartesian domain as an channel image and propose a deep neural network, i.e., channel keypoint detection network (CKNet), to locate the user and scatterers. Subsequently, we employ a straightforward Newton optimization module to fine-tune the estimations. Experimental results demonstrate that the CKNet-empowered channel reconstruction scheme substantially reduces computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction.

We consider the problem of channel estimation for multi-reconfigurable intelligence durface (RIS) assisted orthogo-nal frequency division multiplexing (OFDM) systems. To detect the higher-rank signals reflected by multiple RISs, we divide the total RISs into several groups and propose a training protocol for filtering the signal components belonging to different groups. By concatenating the training signals on different subcarriers, we construct a low-rank third-order tensor model and develop a canonical polyadic decomposition (CPD) method to estimate the channel parameters. Simulation results indicate that the proposed tensor-based method effectively improves the estimation accuracy. The RIS grouping strategy trades off the training overhead and algorithm performance.

This paper studies the beamformer design for an integrated sensing and communication system where simultaneous communication for multiple downlink users and bistatic sensing for a point target are realized. Firstly, to avoid self-interference in monostatic settings, we establish the bistatic sensing model where the sensing performance is measured by the Cramér-Rao bound (CRB) for the target's coordinates and the communication performance by the signal-to-interference-plus-noise ratio (SINR). We aim to minimize the CRB while ensuring a predefined SINR for each user. Then, we derive a closed-form beamformer for the single-user case that proves near-optimal. For the multi-user case, we introduce a beamformer design algorithm based on semidefinite relaxation and a suboptimal design algorithm based on successive convex approximation with reduced complexity. Finally, numerical results are provided to validate the solutions.

Reconfigurable intelligent surface (RIS)-assisted communications have received widespread attention recently, whose channel modeling is the cornerstone for its future practical application. Nevertheless, the RIS-related channel modeling stays at the level of either the theoretical free-space derivation or the end-to-end channel model, yet its general segment channel model applicable to real environment is still rarely reported. In this paper, we formulate a temporal segment channel model for RIS-assisted non-line-of-sight (NLOS) communications, which separates the global channel into three parts, i.e., transmitter (Tx)-RIS, RIS-receiver (Rx), and Tx-Rx. Then we bridge the relationship between the proposed model and the existing models. In addition, we illustrate the Ricean K–factor characteristics of the global channel and the sub-channel in the proposed model. The lower boundary for the Ricean K–factor of sub-channel is also deduced. Two low-complexity estimation algorithms for the Ricean K–factor of sub-channel are proposed, whose accuracy is verified by the measured channel data.

Traditional indoor location data sharing methods using centralized servers face issues like safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads, hampering the growth of personalized indoor services. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) data sharing framework for indoor positioning is presented. Then, we derive training latency and reward of the individual user, and formulate latency-limited resource allocation as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demon-strate that the proposed alternating iterative algorithm achieves rapid convergence. Furthermore, when confronted with model poisoning attacks, the MBFL method exhibits superior security performance compared to the traditional FL method.

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electro-magnetic (EM) fields for sensing the target and can thus help computational imaging in integrated sensing and communication (ISAC) systems. In this paper, we propose a near-field imaging scheme based on holograghic RIS. To mitigate the inherent ill conditioning of the inverse problem in the imaging system, we design the EM field patterns as masks that help translate the inverse problem into a forward problem. Next, we utilize RIS to generate different virtual EM masks on the target surface and calculate the cross-correlation between the mask patterns and the electric field strength at the receiver. We then provide a RIS design scheme for virtual EM masks by employing a regularization technique. Simulation results demonstrate that the proposed method can achieve high-quality imaging. Moreover, the imaging quality can be improved by generating more virtual EM masks, by increasing the signal-to-noise ratio (SNR) at the receiver, or by placing the target closer to the RIS.

Wi-Fi access point (AP) and IoT device localization are essential for smart home functionalities, including indoor localization and privacy protection. Yet, complex multipath channels in indoor settings often hinder precise localization. To overcome this, we introduce an Artificial Intelligence (AI) technique that amalgamates channel state information from proximate trajectory points, thus elevating the accuracy of line of sight (LoS) angle of arrival (AoA) estimation. Our methodology initiates with an AI-based anomaly detection system to eliminate questionable measurements. Thereafter, our AI-optimized LoS-AoA network proficiently identifies the primary LoS path from the several multipaths detected by the multipath estimation process and autonomously fine-tunes the LoS-AoA estimation. Using simulations in an indoor office environment with Wireless Insite, our results reveal that our approach considerably improves LoS-AoA estimations, even under challenging indoor scenarios. Notably, our technique enhanced AP positioning accuracy in 68% of instances, reducing a 2-meter error to 0.6 meters, and in 95% of instances, cutting down a 10-meter error to 2 meters when measured against top benchmarks.

Visibility region (VR) is known as a key channel characteristic appeared in extra-large massive MIMO (XL-MIMO) systems, which can be exploited to facilitate low-complexity transmission design. Existing VR recognition method requires an a priori location-Vrdataset, with which a user's VR can be estimated given its location. This dataset is constructed by selecting some beacon users (BUs) to estimate the VR at their locations via uplink training. Constrained by the available training resource and possible environmental variation, practical size of the dataset is usually limited; how to efficiently select BUs for better VR recognition accuracy is therefore important. To this end, we propose and compare three BU selection methods, including random selection, minimum spacing constrained (MSC) selection, and a more sophisticated method (denoted as dynamic boundary refining, DBR) which utilizes partial of BUs for exploring unknown environment, while selecting the other BUs for further refining already-estimated VR region boundaries. Simulation results show that with a small number of BUs, both MSC and DBR achieve similar VR recognition performance and outperform random selection; as the number of BUs becomes larger, DBR achieves the best recognition accuracy.

Abstract Collaborative transmission, comprising multiple devices owned by a single user, is progressively evolving into an essential strategy to meet the stringent demands of burgeoning collaborative scenarios in 5G‐advanced and 6G networks. This paper proposes three novel use cases for device collaboration, namely data duplication, data splitting and wireless backup, to address these requirements. To provide dynamic and efficient collaboration, both non‐transparent mode via the medium access control layer collaboration and transparent mode via the physical layer collaboration are proposed. The paper further introduces a comprehensive design framework including protocol stack design, user equipment capability reporting, user equipment pairing, scheduling mechanism and transmission mechanism for different collaborative use cases with different collaborative modes. Evaluation outcomes reveal that the recommended methods could decrease the resources consumed for data duplication while increasing the user perceived throughput for data duplication and data splitting. The proposed methods also augment transmission reliability for both data duplication and wireless backup.

In this paper, we propose a low‐complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high‐mobility scenario, OTFS is becoming a potential scheme for the sixth‐generation (6G) wireless communication system. However, the large size of the effective delay‐Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low‐complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log‐linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity.

Thanks to the great potential to alleviate the intercell interference issue, cell-free wireless network is regarded as one of the most promising networking architectures in the future. In the meantime, the dramatic increase in the number of wireless devices and their diversified communication rate requirements pose new challenges for cell-free networks. In this article, we integrate the new symbiotic radio (SR) transmission technique into cell-free networks. In particular, spectral- and energy-efficient passive backscatter communication in SR is achieved by passive reflective beamforming over multiple reconfigurable intelligent surfaces (RISs). On the one hand, distributed access points (APs) in cell-free network collaboratively perform active communication through direct links, and on the other hand, RISs reuse the spectrum and energy of active communication to passively backscatter its own information-bearing signal. Considering the coexistence of active and passive communication demands, we define the rate region of the RIS-based cell-free SR system as the union of all rate pairs achieved by the active and passive communication devices. To characterize the achievable rate region, we formulate an optimization problem to maximize the passive communication rate given a minimum active rate constraint, by jointly optimizing the active transmit beamforming and passive reflective beamforming. An efficient alternating algorithm is proposed to solve the formulated problem. Finally, simulation results are presented to show the rate region of RIS-based cell-free SR systems and demonstrate the effectiveness of the proposed joint beamforming algorithm.

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G New Radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing Simultaneous Localization and Mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM.

Internet of Things (IoT) device localization is fundamental to smart home functionalities, including indoor navigation and tracking of individuals. Traditional localization relies on relative methods utilizing the positions of anchors within a home environment, yet struggles with precision due to inherent inaccuracies in these anchor positions. In response, we introduce a cutting-edge smartphone-based localization system for IoT devices, leveraging the precise positioning capabilities of smartphones equipped with motion sensors. Our system employs artificial intelligence (AI) to merge channel state information from proximal trajectory points of a single smartphone, significantly enhancing Line of Sight (LoS) Angle of Arrival (AoA) estimation accuracy, particularly under severe multipath conditions. Additionally, we have developed an AI-based anomaly detection (AD) algorithm to further increase the reliability of LoS-AoA estimation. This algorithm improves measurement reliability by analyzing the correlation between the accuracy of reversed feature reconstruction and the LoS-AoA estimation. Utilizing a straightforward least squares algorithm in conjunction with accurate LoS-AoA estimation and smartphone positional data, our system efficiently identifies IoT device locations. Validated through extensive simulations and experimental tests with a receiving antenna array comprising just two patch antenna elements in the horizontal direction, our methodology has been shown to attain decimeter-level localization accuracy in nearly 90% of cases, demonstrating robust performance even in challenging real-world scenarios. Additionally, our proposed AD algorithm trained on Wi-Fi data can be directly applied to ultrawideband, also outperforming the most advanced techniques.

Federated learning (FL) based on the centralized design faces both challenges regarding the trust issue and a single point of failure. To alleviate these issues, blockchain-aided decentralized FL (BDFL) introduces the decentralized network architecture into the FL training process, which can effectively overcome the defects of centralized architecture. However, deploying BDFL in wireless networks usually encounters challenges, such as limited bandwidth, computing power, and energy consumption. Driven by these considerations, a dynamic stochastic optimization problem is formulated to minimize the average training delay by jointly optimizing the resource allocation and client selection under the constraints of limited energy budget and client participation. We solve the long-term mixed integer nonlinear programming problem by employing the tool of Lyapunov optimization and thereby propose the dynamic resource allocation and client scheduling BDFL (DRC-BDFL) algorithm. Furthermore, we analyse the learning performance of DRC-BDFL and derive an upper bound for convergence regarding the global loss function. Extensive experiments conducted on the SVHN and CIFAR-10 data sets demonstrate that the DRC-BDFL achieves comparable accuracy to the baseline algorithms while significantly reducing the training delay by 9.24% and 12.47%, respectively.

By decoupling the dedicated radio frequency (RF) chain into transmit RF (TX RF) chain and receive RF (RX RF) chain, the asymmetrical system can flexibly equip the downlink/uplink array with different number of TX/RX RF chain according to the practical demand in a massive multiple-input multiple-output Internet of Things (IoT) network. To reduce cost and power consumption, this paper maximizes the uplink resource efficiency (RE) under Weichselberger channel model by designing transmit covariance matrices and receive antenna selection (RAS). In IoT networks with multiple IoT nodes, we propose an alternate optimization algorithm to iteratively optimize transmit covariance matrices and RAS by exploiting statistical channel state information. Specifically, for correlated channels, we propose a penalty method-based algorithm for RAS which utilizes Dinkelbach’s transform and linear relaxation to tackle the intractable fractional function and binary constrain, respectively. Compared with greedy search, the proposed algorithm has lower complexity without much loss of performance. For independent identically distributed channels, we simplify the RE maximization problem and provide the necessary conditions of the optimal number of receive antennas and transmit power. Finally, the validness of our conclusions as well as the effectiveness of proposed algorithms are illustrated by numerical simulations.

Realizing the wireless environmental sensing is another desired function of reconfigurable intelligent surface (RIS), in addition to enhancing the performance of wireless communication systems. In this paper, we design a holographic RIS-aided computational imaging system, which consists of a transmitter, a holographic RIS, a rectangular target and a receiver. Here, the target is composed of a series of discrete segments, each of which possesses a constant scattering density. The sensing task of the proposed system is to estimate the scattering densities of the target, which corresponds to the termcomputational imaging. The termholographicmeans that the RIS is modeled as a physically continuous surface with a physically continuous phase shift pattern, which can be approximately considered as as having massive (possibly infinite) number of elements within a finite space. Both the RIS and the target are subject to the electromagnetic boundary conditions, whose scattered fields are computed by the equivalent current method and the physical equivalent. Based on the computed scattered fields of the target, we derive the pathloss of the proposed system. In order to perform the imaging, we alter the phase shift pattern of the RIS such that the main energy of its scattered fields is focused towards different segments of the target successively, which then produces multiple measurements of the scattering densities and simultaneously ensures a low pathloss. After all measurements are completed, the scattering densities of the target can be estimated with the observed measurement vector and the reconstructed sensing channel, i.e., the computational imaging is accomplished. Simulation results show that the proposed imaging strategy performs well if the system parameters are designed properly.

Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips and tricks on realizing such performance gains when training AI/ML models. In particular, we discuss in detail the use of AI/ML to revolutionize key physical layer and lower medium access control (MAC) layer functionalities in traditional wireless communication systems. In addition, we provide a comprehensive overview of the AI/ML-enabled semantic communication systems, including key techniques from data generation to transmission. We also investigate the role of AI/ML as an optimization tool to facilitate the design of efficient resource allocation algorithms in wireless communication networks at both bit and semantic levels. Finally, we analyze major challenges and roadblocks in applying AI/ML in practical wireless system design and share our thoughts and insights on potential solutions.

In “Channel Estimation for Massive MIMO-OTFS System in Asymmetrical Architecture,” by Chen et al., a two-stage channel estimation scheme is proposed based on the input-output relationship of orthogonal time frequency space (OTFS) modulation in asymmetrical architecture. This correspondence provides some comments and corrections to the derivation of the OTFS input-output relationship published in [1].

Deep learning has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher computational complexity. Pursuing better performance-complexity tradeoffs is crucial to facilitate practical deployment, especially on computation-limited devices, which may have to use lightweight autoencoder with unfavorable performance. To achieve this goal, this paper introduces knowledge distillation (KD) to achieve better tradeoffs, where knowledge from a complicated teacher autoencoder is transferred to a lightweight student autoencoder for performance improvement. Specifically, two methods are proposed for implementation. Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder KD-based method is proposed to reduce training overhead by performing KD only on the student encoder. Additionally, a variant of encoder KD is introduced to protect user equipment and base station vendor intellectual property. Numerical simulations demonstrate that the proposed methods can significantly improve the student autoencoder’s performance, while reducing the number of floating point operations and inference time to 3.05%–5.28% and 13.80%–14.76% of the teacher network, respectively. Furthermore, the variant encoder KD method effectively enhances the student autoencoder’s generalization capability across different scenarios, environments, and bandwidths.

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To fully exploit the inherent propagation similarity within dual-polarized channels, we propose a disentangled representation neural network (NN) for CSI feedback, referred to as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three components: polarization-shared information, vertical polarization-specific information, and horizontal polarization-specific information. This disentanglement of dual-polarized CSI enables the minimization of information redundancy caused by the polarization correlation and improves the performance of CSI compression and recovery. Additionally, flexible quantization and network extension schemes are designed. Consequently, our method provides a pragmatic solution for CSI feedback to harness the physical MIMO polarization as a priori information. Our experimental results show that the performance of our proposed DiReNet surpasses that of existing DL-based networks, while also effectively reducing the number of network parameters by nearly one third.

Retrieving range information in three-dimensional (3D) radio imaging is particularly challenging due to the limited communication bandwidth and pilot resources. To address this issue, we consider a reconfigurable intelligent surface (RIS)-aided uplink communication scenario, generating multiple measurements through RIS phase adjustment. This study successfully realizes 3D single-frequency imaging by exploiting the near-field multi-view image correlations deduced from user mobility. We first highlight the significance of considering anisotropy in multi-view image formation by investigating radar cross-section properties and diffraction resolution limits. We then propose a novel model for joint multi-view 3D imaging that incorporates occlusion effects and anisotropic scattering. These factors lead to slow image support variation and smooth coefficient evolution, which are mathematically modeled as Markov processes. Based on this model, we employ the Expectation Maximization-Turbo-Generalized Approximate Message Passing algorithm for joint multi-view single-frequency 3D imaging with limited measurements. Simulation results reveal the superiority of joint multi-view imaging in terms of enhanced imaging ranges, accuracies, and anisotropy characterization compared to single-view imaging. Combining adjacent observations for joint multi-view imaging enables a reduction in the measurement overhead by 80%.

Semantic communication, through the interpretation of the semantic meaning of transmitted data, effectively reduces the required bandwidth. However, current deep learning-based methods face limitations due to their reliance on joint source-channel coding and end-to-end training, hindering adaptability to new channels and user demands. In this study, we introduce the Reconfigurable Intelligent Surface-Semantic Communication (RIS-SC) framework as a solution. This framework dynamically allocates semantic content, leveraging varying degrees of RIS assistance to cater to the evolving needs of users. It takes into account factors such as user mobility and obstacles in the line of sight, enabling the RIS resource to preserve essential semantics even in challenging channel conditions. While this ensures the preservation of core semantics in difficult channel conditions, it may also lead to the loss of some non-essential semantic details under extreme conditions. To counteract this, we have incorporated a reconstruction method that deduces the missing semantic elements, thereby enhancing visual understanding. The RIS-SC framework stands out for its adaptability, ensuring optimal resource distribution for users under favorable conditions and maintaining visual clarity in challenging scenarios. Simulations validate the effectiveness and adaptability of our approach in diverse channel conditions and user demands.

In this paper, an efficient near-field channel estimation algorithm, and a novel cascade channel reconstruction scheme, with significantly reduced pilot overhead and computational complexity, are proposed for hybrid extra large-scale reconfigurable intelligent surface (XL-RIS)-aided multi-input multi-output (MIMO) systems. A unique hybrid XL-RIS architecture is devised, in which the elements at the designed central subarray, and many specially selected discrete elements, are active, while others are passive. Meanwhile, a damped Newtonized orthogonal matching pursuit algorithm combining the planar and spherical wave models (DNOMP-CPSW) is proposed, in which the angle and distance parameters of multipaths are estimated through the received signals of the central subarray and the discrete active elements respectively, and the near-field channel can be reconstructed accurately with low pilot overhead and computational complexity. Moreover, to decrease the cost of cascade channel reconstruction in the considered system, a separate channel estimation scheme based on the decoupling operation (SCEDO) is proposed, which estimates the two separate channels with only 3 pilots and reduced computational complexity, and then reconstruct the cascade channel. Furthermore, the phase shift strategy of the XL-RIS with 2-bits quantization is devised, which can increase the energy of the received signal and maintain the set order to estimate multipaths in different stages of the SCEDO scheme, to improve the accuracy of the estimates, and enhance the reliability of the separate channel estimation. Simulation results verify that the proposed DNOMP-CPSW algorithm and the hybrid XL-RIS phase shift strategy can enhance the performance of the considered system. Compared with other schemes, the SCEDO scheme can reconstruct the cascade channel efficiently, with much reduced pilot overhead and computational complexity.

In this paper, a deterministic sampling decoding strategy for multiple-input multiple output (MIMO) systems is studied, which performs probabilistic searching according to a probability threshold in the lattice Gaussian distribution. Motivated by model probabilistic twin (MPT), the randomness in obtaining the target decoding solution is overcome by the proposed probabilistic searching decoding (PSD) algorithm, which brings considerable decoding gains in both performance and complexity. Specifically, the decoding radius of PSD is derived while the decoding complexity in terms of the number of visited nodes during the searching is also upper bounded, leading to an explicit decoding trade-off. Meanwhile, we generalize PSD by the mechanism of candidate protection so that it enjoys a flexible performance between the suboptimal successive interference cancelation (SIC) decoding and the optimal maximum likelihood (ML) decoding by adjusting the initial search size$K$. Methods for further optimization and complexity reduction of the proposed PSD algorithm are also given. Finally, simulation results based on MIMO detection are presented to confirm the tractable and flexible decoding trade-off of the proposed PSD algorithm.

The reconfigurable intelligent surface (RIS) has received a lot of interest because of its capacity to reconfigure the wireless communication environment in a cost- and energy-efficient way. However, the realistic power consumption modeling and measurement validation of RIS has received far too little attention. Therefore, in this work, we model the power consumption of RIS and conduct measurement validations using various RISs to fill this vacancy. Firstly, we propose a practical power consumption model of RIS. The RIS hardware is divided into three basic parts: the FPGA control board, the drive circuits, and the RIS unit cells. The power consumption of the first two parts is modeled asPstaticand that of the last part is modeled asPunits. Expressions ofPstaticandPunitsvary amongst different types of RISs. Secondly, we conduct measurements on various RISs to validate the proposed model. Five different RISs including the PIN diode, varactor diode, and RF switch types are measured, and measurement results validate the generality and applicability of the proposed power consumption model of RIS. Finally, we summarize the measurement results and discuss the approaches to achieve the low-power-consumption design of RIS-assisted wireless communication systems.

To harness the benefits of both reconfigurable intelligent surface (RIS) and dynamic metasurface antenna (DMA), we consider the hybrid RIS and DMA assisted multiple-input multiple-output (MIMO) multiple-access channel (MAC) over spatially correlated Rician fading, in which multiple multi-antenna users send the transmitted signals to the DMA-based base station (BS) with the assistance of a RIS. The objective is to maximize the achievable ergodic sum-rate by jointly designing the transmit covariance matrix of users, the phase shift matrix of RIS, and the DMA weight matrix at BS only with statistical channel state information. By capitalizing on large random matrix theory, a closed-form asymptotic ergodic sum-rate is first obtained. Then, we propose a modified water-filling algorithm to design the optimal transmit covariance matrix under the power consumption and specific absorption rate constraints. Next, we design the phase shift matrix of RIS via the projected gradient ascent algorithm, subject to the non-convex unit-modular constraint. To find the constrained DMA weight matrix, we further resort to the optimal solution of the unconstrained DMA problem and adopt the alternating optimization method. The proposed algorithm is numerically shown to improve the sum-rate compared to the baseline schemes, verifying the effectiveness of the proposed schemes.

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multi-user XL-MIMO scenario, where only partial antennas can receive users’ signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

We develop a new integrated communications and security (ICAS) design paradigm by leveraging the concept of reconfigurable intelligent surfaces (RISs). In particular, we propose RIS-assisted simultaneous transmission and secret key generation by sharing the RIS for these two tasks. Specifically, the legitimate transceivers intend to jointly optimize the data transmission rate and the key generation rate by configuring the phase-shift of the RIS in the presence of a smart attacker. We first derive the key generation rate of the RIS-assisted physical layer key generation (PLKG). Then, to obtain the optimal RIS configuration, we formulate the problem as a secure transmission (ST) game and prove the existence of the Nash equilibrium (NE), and then derive the NE point of the static game. For the dynamic ST game, we model the problem as a finite Markov decision process and propose a model-free reinforcement learning approach to obtain the NE point. Particularly, considering that the legitimate transceivers cannot obtain the channel state information (CSI) of the attacker in real-world conditions, we develop a deep recurrent Q-network (DRQN) based dynamic ST strategy to learn the optimal RIS configuration. The details of the algorithm are provided, and then, the system complexity is analyzed. Our simulation results show that the proposed DRQN based dynamic ST strategy has a better performance than the benchmarks even with a partial observation information, and achieves “one time pad” communication by allocating a suitable weight factor for data transmission and PLKG.

The conventional enhancement-and-perturbation approach to establishing Gaussian extremal inequalities is refined via a novel monotone path argument in the product probability space. This refined approach is illustrated with simplified/corrected proofs of the Liu-Viswanath extremal inequality and a vector generalization of Costa’s entropy power inequality. The power of this refinement is further demonstrated by characterizing two information-theoretic limits, namely, the capacity region of the multiple-input multiple-output (MIMO) Gaussian broadcast channel with private and common messages and the rate-distortion-equivocation function of vector Gaussian secure source coding, which have previously resisted the attack of the conventional approach.

Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms.

In decentralized federated learning (DFL), clients exchange their models with each other for global aggregation. Due to a lack of centralized supervision, a client may easily duplicate shared models to save its computing resources. Generally, this plagiarism behavior is hard to detect, while it is harmful to model training performance. To address this issue, we propose an anti-plagiarism DFL framework to efficiently detect plagiarism misconduct. Specifically, we first design a method for detecting plagiarism by adding a time-shift pseudo-noise (PN) sequence to each client's local model before broadcasting. Second, we develop an upper bound of the loss function of DFL with the proposed PN sequence detection method, which is proved to be the convex function of both the amplitude of PN sequences ($\alpha$) and the detection threshold ($\lambda$). Next, we propose an adaptive plagiarism detection (APD) algorithm by jointly optimizing$\alpha$and$\lambda$to enhance the learning performance. Finally, we conduct extensive experiments on MNIST, Adult, Cifar-10, and SVHN datasets to demonstrate that our analytical bounds are consistent with the experimental results. Remarkably, the proposed framework can recover up to a 10% classification accuracy loss in the presence of 40% plagiaristic clients.

Deep learning (DL)-based channel state information (CSI) feedback has garnered significant research attention in recent years. However, previous research has overlooked the potential privacy disclosure problem caused by transmitting CSI datasets during the training process. In this study, we introduce a federated edge learning (FEEL)-based training framework for DL-based CSI feedback. This approach differs from the conventional centralized learning (CL)-based framework, where the CSI datasets are collected at the base station (BS) before training. Instead, each user equipment (UE) trains a local autoencoder network and exchanges model parameters with the BS. This approach provides better protection for data privacy compared to CL. To further reduce communication overhead in FEEL, we quantize the uplink and downlink model transmission into different bits based on their influence on feedback performance. Additionally, since the heterogeneity of CSI datasets among different UEs can degrade the performance of the FEEL-based framework, we introduce a personalization strategy to enhance feedback performance. This strategy allows for local fine-tuning to adapt the global model to the channel characteristics of each UE. Simulation results indicate that the proposed personalized FEEL-based training framework can significantly improve the performance of DL-based CSI feedback while reducing communication overhead.

Intelligent reflecting surface (IRS) is an emerging technology for wireless communications, thanks to its powerful capability to engineer the radio environment. However, in practice, this benefit is attainable only when the passive IRS is of sufficiently large size, for which the conventional uniform plane wave (UPW)-based far-field model may become invalid. In this paper, we pursue a near-field modelling and performance analysis for wireless communications with extremely large-scale IRS (XL-IRS). By taking into account the directional gain pattern of IRS’s reflecting elements and the variations in signal amplitude across them, we derive both the lower- and upper-bounds of the resulting signal-to-noise ratio (SNR) for the generic uniform planar array (UPA)-based XL-IRS. Our results reveal that, instead of scaling quadratically and unboundedly with the number of reflecting elementsMas in the conventional UPW-based model, the SNR under the new non-uniform spherical wave (NUSW)-based model increases withMwith a diminishing return and eventually converges to a certain limit. To gain more insights, we further study the special case of uniform linear array (ULA)-based XL-IRS, for which a closed-form SNR expression in terms of the IRS size and locations of the base station (BS) and the user is derived. Our result shows that the SNR is mainly determined by the two geometric angles formed by the BS/user locations with the IRS, as well as the dimension of the IRS. Numerical results validate our analysis and demonstrate the necessity of proper near-field modelling for wireless communications aided by XL-IRS.

As one of the sensing tasks for integrated sensing and communications (ISAC), location distinction based specific emitter identification (SEI) plays an important role in location based services. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted SEI system, in which the legitimate emitter installs an RIS to customize the wireless link signature by controlling the ON-OFF state of RIS. Specifically, we consider the worst-case that the legitimate and a suspicious emitter are in the same spatial location. The received signal strength (RSS) of the specific emitter is adopted to analyze the feasibility of the proposed system. Then, we derive the statistical properties of this wireless link signature, and find the interesting insights about the phase-shift matrix configuration and the signal-to-noise-rate (SNR) gain, which showcase the huge potential of the proposed system on the integrated communications and security (ICAS) design in the near future. Afterwards, we derive the optimal detection threshold in the context of the presented metrics. Next, considering the acquisition difficulty of the RSS samples of the suspicious emitter, we use a one-class support vector machine (OC-SVM) to identify the specific emitter. Finally, the actual feasibility of the proposed system is verified via proof-of-concept experiments. The experiment results show that there are 76% and 99% performance improvements for the test statistic based and the OC-SVM based RIS-assisted SEI, respectively.

Most learning-based channel state information (CSI) feedback efforts concentrate on enhancing feedback accuracy through innovative neural network (NN) designs and exploiting correlations. This paper introduces an integrated learning framework for CSI feedback and localization designed to synergistically improve both tasks. We present a novel unified approach for CSI feedback and downlink CSI-based localization, where feedback is facilitated by an autoencoder, and the downlink CSI-based localization uses the feedback codeword directly without requiring reconstruction. The goal is to simultaneously minimize feedback and localization errors. Additionally, for users with access to coarse position data, we propose a refined framework that integrates this information into both the feedback mechanism and localization processes. This coarse positional knowledge is incorporated into the encoding and decoding stages to reduce feedback errors and is inputted into the localization NN to enhance localization accuracy. The improved framework is refined through an end-to-end training strategy, focusing on concurrently reducing feedback and localization errors. Simulation results using ray tracing channel datasets demonstrate that our proposed method not only enables feedback and localization tasks to mutually benefit but also shows that incorporating coarse positional data significantly increases the accuracy of both CSI feedback and CSI-based localization.

This paper investigates the joint beam alignment and Doppler estimation (BADE) for fast time-varying wideband millimeter-wave channels, which is essential for subsequent data transmission. In such scenarios, the non-negligible Doppler frequencies significantly impact the beam alignment performance and reference signal overhead, calling for accurate time variation modeling and efficient transceiver design. Toward this end, we leverage the angle, Doppler frequency, and delay sparsity, thus formulating the joint BADE problem as a sparse signal recovery problem in the angle-Doppler-delay domain. The feasibility of the formulated problem strongly depends on the transmitter codebook and the receiver algorithm, which motivates our design. For the transmitter codebook, we characterize the design criterion aiming at maximal identifiable paths, which facilitates precise path parameter estimations and is not satisfied by existing deterministic codebooks. Following this, we provide a new deterministic codebook generation algorithm to meet the necessary conditions of the proposed criterion. For the receiver algorithm, we propose the greedy-based multi-path parameter extraction algorithm. In the proposed algorithm, the hierarchical refinement dictionaries with extended refinement range are employed, balancing the BADE performance and computational complexity. The numerical simulations demonstrate the superiority of the proposed transceiver over benchmarks on the joint BADE problem.

Radio imaging is rapidly gaining prominence in the design of future communication systems, with the potential to utilize reconfigurable intelligent surfaces (RISs) as imaging apertures. Although the sparsity of targets in three-dimensional (3D) space has led most research to adopt compressed sensing (CS)-based imaging algorithms, these often require substantial computational and memory burdens. Drawing inspiration from conventional Fourier transform (FT)-based imaging methods, our research seeks to accelerate radio imaging in RIS-aided communication systems. To begin, we introduce a two-stage wavenumber domain 3D imaging technique: first, we modify RIS phase shifts to recover the equivalent channel response from the user equipment to the RIS array, subsequently employing traditional FT-based wavenumber domain methods to produce target images. We also determine the diffraction resolution limits of the system through k-space analysis, taking into account factors including system bandwidth, transmission direction, operating frequency, and the angle subtended by the RIS. Addressing the challenge of limited pilots in communication systems, we unveil an innovative algorithm that merges the strengths of both FT- and CS-based techniques by substituting the expansive sensing matrix with FT-based operators. Our simulation outcomes confirm that our proposed FT-based methods achieve high-quality images while demanding few time, memory, and communication resources.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals in ISAC systems to sense the electromagnetic (EM) property of the target and thus also identify the material of the target. Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization. We then model the relative permittivity and conductivity distribution (RPCD) within a specified detection region. Based on the sensing model, we introduce a multi-frequency-based EM property sensing method by which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. To improve the sensing accuracy, we design a beamforming strategy from the communications transmitter based on the Born approximation that can minimize the mutual coherence of the sensing matrix. The optimization problem is cast in terms of the Gram matrix and is solved iteratively to obtain the optimal beamforming matrix. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification. Furthermore, improvements in RPCD reconstruction quality and material classification accuracy are observed with increased signal-to-noise ratio (SNR) or reduced target-transmitter distance.

Deep learning has been introduced to improve implicit channel state information (CSI) feedback, and it significantly outperforms codebook-based feedback methods used in existing systems. This study proposes a multi-domain correlation-aided implicit CSI feedback framework that uses deep learning. This framework retains the existing implicit feedback mechanism while introducing the aid of the multi-domain correlation property of CSI matrices to the feedback process for performance improvement. First, a time correlation-aided implicit feedback framework is proposed, where the correlation among adjacent CSI matrices is exploited to improve the CSI reconstruction accuracy. Second, to utilize the correlation between the uplink and downlink channel, the uplink channel magnitude is introduced into the CSI reconstruction process at the base station. Additionally, the framework combines the aid of time and bidirectional channel correlation properties to further enhance performance. Simulation results show that, with the aid of the multi-domain correlation property, the feedback overhead can be reduced by 75% and 85% compared to approaches without correlation utilization and Type II codebook, respectively.

Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with the beamforming vector at the access point is challenging due to the non-convex objective function and constraints. In this study, we propose an algorithm based on weighted minimum mean square error optimization and power iteration to maximize the weighted sum rate (WSR) of a RIS-assisted downlink multi-user multiple-input single-output system. To further improve performance, a model-driven deep learning (DL) approach is designed, where trainable variables and graph neural networks are introduced to accelerate the convergence of the proposed algorithm. We also extend the proposed method to include beamforming with imperfect channel state information and derive a two-timescale stochastic optimization algorithm. Simulation results show that the proposed algorithm outperforms state-of-the-art algorithms in terms of complexity and WSR. Specifically, the model-driven DL approach has a runtime that is approximately 3% of the state-of-the-art algorithm to achieve the same performance. Additionally, the proposed algorithm with 2-bit phase shifters outperforms the compared algorithm with continuous phase shift.

In this paper, we investigate multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, where modular extremely large-scale uniform linear array (XL-ULA) is deployed at the base station (BS) to serve multiple single-antenna users. By exploiting the unique modular array architecture and considering the potential near-field propagation, we develop sub-array based uniform spherical wave (USW) models for distinct versus common angles of arrival/departure (AoAs/AoDs) with respect to different sub-arrays/modules, respectively. Under such USW models, we analyze the beam focusing patterns at the near-field observation location by using near-field beamforming. The analysis reveals that compared to the conventional XL-MIMO with collocated antenna elements, modular XL-MIMO can provide better spatial resolution by benefiting from its larger array aperture. However, it also incurs undesired grating lobes due to the large inter-module separation. Moreover, it is found that for multi-user modular XL-MIMO communications, the achievable signal-to-interference-plus-noise ratio (SINR) for users may be degraded by the grating lobes of the beam focusing pattern. To address this issue, an efficient user grouping method is proposed for multi-user transmission scheduling, so that users located within the grating lobes of each other are not allocated to the same time-frequency resource block (RB) for their communications. Numerical results are presented to verify the effectiveness of the proposed user grouping method, as well as the superior performance of modular XL-MIMO over its collocated counterpart with densely distributed users.

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional codebook-based channel state information (CSI) feedback techniques. Confronted with the influx of CSI data from simulations and real-world environments, leveraging neural networks to mine valuable insights poses significant training costs and technical challenges for base station (BS) manufacturers. To address this, we propose a third-party platform serving as a CSI knowledge repository and feedback model hub, reducing training expenses and addressing technical issues for various BS manufacturers. However, tailoring training for each manufacturer’s model may lead to proprietary information leaks and inefficient resource utilization. In response, we present “CSI Meta-knowledge Support”, a cutting-edge CSI feedback network deployment strategy using Learngene, enabling seamless transfer of CSI meta-knowledge across heterogeneous networks. This method captures a Learngene unit enriched with vital CSI meta-knowledge during comprehensive training sessions, serving as a plug-and-play prior to facilitate swift convergence and efficient local fine-tuning for manufacturers. The approach introduces adaptable and scalable CSI feedback network configurations, emphasizing reusability, cost-effectiveness, and resource management while safeguarding intellectual property. Our tests demonstrate enhanced performance, reduced training sample demands, and faster convergence relative to conventional techniques.

Deep learning has brought about a revolution in the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process, and manual design can be prohibitively expensive for customized NNs tailored to different scenarios. To tackle this challenge, this paper proposes the use of neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved, requiring less expert experience and design time, thus lowering the design threshold. The proposed approach leverages implicit scene knowledge and integrates it into the scenario customization process in a data-driven manner, fully exploiting the potential of deep learning in a given scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, further enhancing the proposed scheme. The experimental results demonstrate that the generated architecture, known as Auto-CsiNet, outperforms manually-designed models in terms of reconstruction performance (achieving approximately 14% improvement) and complexity (reducing by approximately 50%), highlighting the effectiveness of the NAS-based automatic scheme. Furthermore, the paper analyzes the impact of the scenario on the NN architecture and capacity.

This paper investigates a simultaneous wireless information and power transfer-enabled hybrid-powered multicell communication network with a nonlinear energy harvesting model. In this multicell environment, information interaction and energy trading are carried out among base stations (BSs), and each BS powered by hybrid sources simultaneously provides information/energy to its user equipments (UEs) over the downlinks. A novel global utility function is proposed by comprehensively considering the incomes from information and energy transmission, energy trading, and the costs from the electricity companies. To maximize this goal, a nonconvex problem that jointly optimizing energy procurement, power allocation, and power splitting ratios is formulated to deal with the imbalance between energy supply and demand at BSs. Considering the nonconvexity of the problem and the strong coupling among the optimization variables, the formulated problem is difficult to solve directly with conventional convex optimization methods. To overcome these obstacles, a two-step solution combining alternating optimization, distributed optimization, and successive convex approximation is designed, in which the BS layer scheme and the UE layer scheme are performed alternately. Different from the existing centralized schemes, the proposed distributed scheme makes local optimal decisions independently at each BS only resorting to the information of neighbor BSs, which brings great advantages in reducing signaling and computational overheads. Moreover, the convergence and advantages of the proposed distributed scheme are verified by rigorous analyses and simulations.

Integrated sensing and communication (ISAC) is emerging as a key enabler to address the increasing demands of spectrum and throughput for ubiquitous sensing and communication. Hereafter, we consider the channel estimation and localization for multi-user ISAC systems assisted by the reconfigurable intelligent surface (RIS) technology. In order to acquire precise environmental information, we propose a novel circular RIS architecture with circularly arranged reflecting unit cells. By modeling the training signal as a low-rank third-order canonical polyadic tensor, we transform the channel estimation problem into a tensor deconstruction task. By leveraging the phase mode excitation principle, we develop a customized RIS training pattern, and retrieve the equivalent channel parameters by subspace estimation algorithms. By exploring the characteristics of RIS array manifolds and free-space propagation, we implement a unique decoupling of channel parameters for user localization, which cannot be supported by traditional linear RIS topologies. Moreover, the design degrees of freedom in the spatial and frequency dimensions are also exploited to further enhance the proposed algorithms. Simulation results indicate that the circular RIS-enabled channel estimation schemes can recover the propagation information with remarkable accuracy, thereby offering a high-level resolution of localization.

In this paper, an efficient near-field channel reconstruction and user equipment (UE) localization scheme is proposed for extremely large antenna array (ELAA) systems using a subarray hybrid precoding architecture. Considering the non-negligible signal amplitude and phase variations across the different receive antennas, a more realistic channel model is adopted. The channel environment, with an approximate smooth ground surface, is modeled. In fact, the channel can be divided into a line-of-sight (LoS) path, a reflection path and some non-LoS (NLoS) paths. Based on the sparsity of the channel in the spatial domain, the damped Newtonized orthogonal matching pursuit (DNOMP) algorithm is also proposed to accurately estimate the multipaths, and reconstruct the channel. Then, a UE localization algorithm is proposed, which can detect the existence of the LoS path and locate the UE. A joint localization algorithm is also devised to further increase the positioning reliability. Simulation results verify that the DNOMP algorithm can reconstruct the channel with better NMSE performance than other schemes. The localization algorithm can locate the UE with low error whenever the LoS path exists or not, with an accuracy close enough to the position error bound (PEB), while the joint localization algorithm can further enhance the positioning reliability.

Delay alignment modulation (DAM) is a promising technology to achieve inter-symbol interference (ISI)-free single-carrier communication, by leveragingdelay compensationandpath-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM is able to effectively align different propagation delays and achieve their constructive superposition, thus especially appealing for intelligent reflecting surfaces (IRSs)-aided communications with controllable multi-paths. In this paper, we apply single-carrier DAM to multi-IRS aided communication and study its design and achievable performance. We first provide an asymptotic analysis showing that when the number of base station (BS) antennas is much larger than the number of IRSs, an ISI-free channel can be established from the BS to the user with appropriate delay pre-compensation and the simple path-based maximal-ratio transmission (MRT) beamforming. We then consider the general system setup and study the problem of joint path-based beamforming design at the BS and phase shifts design at the IRSs for DAM transmission, by considering the three classical beamforming techniques on a per-path basis, namely the low-complexity path-based MRT beamforming to maximize the desired signal power, the path-based zero-forcing (ZF) beamforming for ISI-free DAM communication, and the optimal path-based minimum mean-square error (MMSE) beamforming to maximize the signal-to-interference-plus-noise ratio (SINR). As a comparison, orthogonal frequency-division multiplexing (OFDM)-based multi-IRS aided communication is considered for benchmarking. Simulation results are provided which demonstrate the significant performance gain of DAM over OFDM, in terms of spectral efficiency and bit error rate (BER), as well as its lower peak-to-average-power ratio (PAPR).

Reconfigurable intelligent surface (RIS)-empowered communication, has been considered widely as one of the revolutionary technologies for next generation networks. However, due to the novel propagation characteristics of RISs, underlying RIS channel modeling and measurement research is still in its infancy and not fully investigated. In this paper, we conduct multi-scenario broadband channel measurements and modeling for RIS-assisted communications at the sub-6 GHz band. The measurements are carried out in three scenarios covering outdoor, indoor, and outdoor-to-indoor (O2I) environments, which suffer from non-line-of-sight (NLOS) propagation inherently. Three propagation modes including intelligent reflection with RIS, specular reflection with RIS and the mode without RIS, are taken into account in each scenario. In addition, considering the cascaded characteristics of RIS-assisted channel by nature, two modified empirical models including floating-intercept (FI) and close-in (CI) are proposed, which cover distance and angle domains. The measurement results rooted in 2096 channel acquisitions verify the prediction accuracy of these proposed models. Moreover, the propagation characteristics for RIS-assisted channels, including path loss (PL) gain, PL exponent, spatial consistency, time dispersion, frequency stationarity, etc., are compared and analyzed comprehensively. These channel measurement and modeling results may lay the groundwork for future applications of RIS-assisted communication systems in practice.

Accurate channel estimation is an essential prerequisite for achieving significant performance gains in intelligent reflecting surface (IRS)-aided communication systems. Recent studies have shown that deep neural network-based channel estimation holds promise as a competitive alternative to conventional methods. However, existing neural network-based approaches typically involve manual design of network architectures through a trial-and-error process, demanding extensive domain knowledge and human resources. In this paper, we propose an automatic approach to construct a high-performance neural network architecture for channel estimation. Our method, called the channel estimation neural network architecture search (CENAS), utilizes a truncated back-propagation optimization search strategy to explore a neural network tailored for channel estimation. By carefully designing a search space tailored to channel estimation tasks, the automatically constructed network surpasses both conventional and deep learning-based channel estimation algorithms. The convergence of our framework’s network construction process is comprehensively analyzed, providing formal evidence of its convergence properties. Additionally, the proposed framework exhibits good generalization and applicability by allowing flexible adjustment of hyperparameters to generate networks with varying scales. Empirical results show the stability and the improved performance of CENAS framework, validating its effectiveness and desirability.

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cramér-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, namedPO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through thecommunication distortionandglobal update variance. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.

A reconfigurable intelligent surface (RIS) with active elements is an augmented version of an RIS. By equipping all or part of RIS elements with signal processing capabilities, the channel estimation and the design of RIS phases can be further extended, yielding an improvement in the spectral efficiency (SE). In this paper, we first present a novel sensing RIS structure which is efficient for hardware implementation. Unlike partial active elements in previous structures, all elements are available to the RF chains via switches, which enables the traditional channel estimation methods and channel extrapolation to be implemented. Moreover, we make a comprehensive analysis and comparison with other RIS structures from the perspective of channel state information (CSI) acquisition. Considering the large-scale of RIS and base station (BS) array, we model the channel between the user and the RIS, the RIS and the BS using a near-field channel model. Based on the structured channel model, we propose a low-overhead channel reconstruction protocol through a parameter-extracting method, while the training overhead and complexity are also analyzed. In addition, we investigate the RIS elements’ activation strategy to further reduce the training overhead. Finally, numerical results demonstrate that the proposed scheme achieves accurate channel estimation with low overhead, which can also enhance the achievable SE.

In this paper, we consider symbol-level precoding (SLP) in channel-coded multiuser multi-input single-output (MISO) systems. It is observed that the received SLP signals do not always follow Gaussian distribution, rendering the conventional soft demodulation with the Gaussian assumption unsuitable for the coded SLP systems. It, therefore, calls for novel soft demodulator designs for non-Gaussian distributed SLP signals with accurate log-likelihood ratio (LLR) calculation. To this end, we first investigate the non-Gaussian characteristics of both phase-shift keying (PSK) and quadrature amplitude modulation (QAM) received signals with existing SLP schemes and categorize the signals into two distinct types. The first type exhibits an approximate-Gaussian distribution with the outliers extending along the constructive interference region (CIR). In contrast, the second type follows some distribution that significantly deviates from the Gaussian distribution. To obtain accurate LLR, we propose the modified Gaussian soft demodulator and Gaussian mixture model (GMM)-expectation-maximization (EM) soft demodulators to deal with two types of signals respectively. Subsequently, to further reduce the computational complexity and pilot overhead, we put forward a novel neural network named pilot feature extraction network (PFEN) to replace the EM algorithm, leveraging the transformer mechanism in deep learning. Simulation results show that the proposed soft demodulators dramatically improve the throughput of existing SLPs for both PSK and QAM transmission in coded systems.

We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is “transparent” to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.

This paper investigates the robust design of symbol-level precoding (SLP) for multiuser multiple-input multiple-output (MIMO) downlink transmission with imperfect channel state information (CSI) caused by channel aging. By utilizing thea posteriorichannel model based on the widely adopted jointly correlated channel model, the imperfect CSI is modeled as the statistical CSI incorporating the channel mean and channel variance information with spatial correlation. With the signal model in the presence of channel aging, we formulate the signal-to-noise-plus-interference ratio (SINR) balancing and minimum mean square error (MMSE) problems for robust SLP design. The former targets to maximize the minimum SINR across users, while the latter minimizes the mean square error between the received signal and the target constellation point. When it comes to massive MIMO scenarios, the increment in the number of antennas poses a computational complexity challenge, limiting the deployment of SLP schemes. To address such a challenge, we simplify the objective function of the SINR balancing problem and further derive a closed-form SLP scheme. Besides, by approximating the matrix involved in the computation, we modify the proposed algorithm and develop an MMSE-based SLP scheme with lower computation complexity. Simulation results confirm the superiority of the proposed schemes over the state-of-the-art SLP schemes.

The emergence of fluid antenna systems (FAS) offers a novel technique for obtaining spatial diversity and leveraging interference fades for spectrum sharing in multiuser scenarios—a paradigm referred to as fluid antenna multiple access (FAMA). Nevertheless, as the number of users increases, the interference mitigation capability diminishes. To overcome this, opportunistic scheduling that prioritizes robust users proves to be an effective method for enhancing FAMA. This paper introduces a resilient decentralized reinforcement learning (RL) approach for opportunistic FAMA (O-FAMA), to autonomously select robust users and the port of each chosen user’s FAS jointly to maximize the network sum-rate. In order to enhance learning efficiency in this multi-agent environment, we propose a novel team-theoretic RL framework that includes a derivative network guiding the multi-agent learning of each solution’s policy networks. Our simulation results confirm the effectiveness of the proposed methodology.

Hybrid analog/digital beamforming is a promising technique to realize millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems cost-effectively. However, existing hybrid beamforming designs mainly rely on real-time channel training or beam sweeping to find the desired beams, which incurs prohibitive overhead due to a large number of antennas at both the transmitter and receiver with only limited radio frequency (RF) chains. To resolve this challenging issue, in this paper, we propose a newenvironment-awarehybrid beamforming technique that requires only light real-time training, by leveraging the useful tool of channel knowledge map (CKM) with the user’s location information. CKM is a site-specific database, which offers location-specific channel-relevant information to facilitate or even obviate the acquisition of real-time channel state information (CSI). Two specific types of CKM are proposed in this paper for hybrid beamforming design in mmWave massive MIMO systems, namelychannel angle map(CAM) andbeam index map(BIM). It is shown that compared with existing environment-unaware schemes, the proposed environment-aware hybrid beamforming scheme based on CKM can drastically improve the effective communication rate, even under moderate user location errors, thanks to its great saving of the prohibitive real-time training overhead.

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.

Channel state information (CSI) compression and feedback is a common way of acquiring the CSI at the transmitter in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems due to the lack of channel reciprocity. However, implicit reciprocity potentially exists in the bi-directional channels of an FDD system because they in fact share physically the same propagation paths. We propose to leverage this implicit reciprocity in FDD mMIMO systems to minimize the feedback overhead and enhance the CSI recovery with uplink channel information at the transmitter. To achieve this, we develop a disentangled representation (DR) learning enabled neural network (NN), named DrCsiNet, to realize the selective CSI compression feedback with the assistance of uplink CSI. The proposed DrCsiNet successfully extracts the information of reciprocity implicitly shared between the downlink and uplink channels in FDD mMIMO, while it simultaneously extracts selective information from the downlink CSI excluding the implicit reciprocity component for compression feedback. We conduct extensive simulations to evaluate the performance of the proposed DrCsiNet against existing methods under various setups. Results demonstrate remarkable performance gains of DrCsiNet for CSI recovery and evidence a strong generalization ability across various network structures. These findings validate the efficacy of disentangling implicit CSI reciprocity embedded in uplink CSI for enhancing the downlink CSI recovery in FDD mMIMO.

Fluid antenna system (FAS) represents all forms of movable and non-movable position-flexible antenna system, and opens up the possibility of a new form of modulation schemes. In this paper, we investigate the design of position index modulation (PIM) for FAS for decreasing the bit error rate (BER) while taking advantage of the rate gain in index modulation. We further derive the BER and data rate expressions to assess the achievable performance of PIM. Simulation results are provided to illustrate the performance and some insights are drawn into the impact of both channel estimation accuracy and transmission power.

The application of distributed reconfigurable intelligent surface (RIS) to massive access multiple-input single-output (MISO) is significant to extend the communication coverage. In this paper, a novel framework is proposed in distributed RIS-aided massive access MISO systems with supporting non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) transmissions simultaneously, where a joint active and passive beamforming scheme is designed to fully eliminate the inter-cluster interference and improve the channel gains of prioritized users, respectively. Based on the proposed framework, firstly, we derive two exact channel statistics to characterize the equivalent channel gains of prioritized and non-prioritized users, respectively. Then, by taking into account the influence of imperfect channel state information (CSI) and successive interference cancellation (SIC), the approximate expressions of outage probability and ergodic rate for all users of one cluster under MISO-NOMA and MISO-OMA transmissions are analyzed to obtain their corresponding system throughput. Moreover, by utilizing the above results, we also determine the diversity order and high slope of these users to attain more viewpoints. Finally, simulation results prove our analyses and reveal that: 1) enhancing the estimated accuracy of CSI and the ability of SIC process can remarkably enhance the system performance; 2) the performance of priority users will be significantly improved with the increase of the number of reflecting elements and Rician factor; 3) heterogeneous quality of service requirements and deployment behaviors of users are beneficial for NOMA, while homogenous settings are competitive for OMA.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

Accurately detecting symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is crucial in realizing the benefits of MIMO techniques. However, optimal MIMO detection is associated with a complexity that grows exponentially with the MIMO dimensions and quickly becomes impractical. Recently, stochastic sampling-based Bayesian inference techniques, such as Markov chain Monte Carlo (MCMC), have been combined with the gradient descent (GD) method to provide a promising framework for MIMO detection. In this work, we propose to efficiently approach optimal detection by exploring the discrete search space via MCMC random walk accelerated by Nesterov’s gradient method. Nesterov’s GD guides MCMC to make efficient searches without the computationally expensive matrix inversion and line search. Our proposed method operates using multiple GDs per random walk, achieving sufficient descent towards important regions of the search space before adding random perturbations, guaranteeing high sampling efficiency. To provide augmented exploration, extra samples are derived through the trajectory of Nesterov’s GD by simple operations, effectively supplementing the sample list for statistical inference and boosting the overall MIMO detection performance. Furthermore, we design an early stopping tactic to terminate unnecessary further searches, remarkably reducing the complexity. Simulation results and complexity analysis reveal that the proposed method achieves exceptional performance in both uncoded and coded MIMO systems, adapts to realistic channel models, and scales well to large MIMO dimensions.

For extremely large-scale array (XL-array) communications, the scatterers and/or user equipments (UEs) may be located in the near-field region and only visible to some portions of the XL-array. This paper studies the near-field spatial correlation function (S-CF) of multi-path XL-array communications with mixed line-of-sight (LoS) and non-LoS (NLoS) links. The generic near-field non-uniform spherical wave (NUSW) characteristic and the partial visibility property are considered. For the LoS link, a novel near-field S-CF is derived, which is in terms of the correlation of UE's visibility and location. It is found that the near-field S-CF depends on the UE's angle of arrival (AoA) and distance, which differs from the far-field result that only depends on the UE's AoA. For the NLoS links, we derive a novel integral expression for the near-field S-CF in terms of the correlation of the scatterers' visibility and location distribution. The near-field result is shown to depend on the scatterers' partial visibility and the power location spectrum (PLS) characterized by the AoAs and distances of scatterers, in contrast to the far-field model, which relies on the power angular spectrum (PAS). The result demonstrates that the near-field S-CF of the LoS/NLoS component no longer exhibits spatial wide-sense stationarity (SWSS) and is more generic than the far-field model. To gain further insights, we consider a specific scatterer's location distribution, namely the multi-ring scatterer model. Numerical results show the necessity of modeling near-field S-CF for XL-array communications with partial visibility.

Semantic communication can significantly reduce transmission payload by sending only semantic information related to the task. However, existing end-to-end trained semantic studies degrade under extreme channel environments, while reconfigurable intelligent surface (RIS) technology offers a potential solution for realizing channel customization. In this work, we propose a reconfigurable RIS-enhanced semantic communication framework called RIS-SC. This framework allows for customization of the channel environment based on the user's requirements for different semantic parts, rather than relying solely on the conventional bit error rate requirement. Using reinforcement learning, the RIS controller interacts with varying channels to meet the user's different requirements. The RIS controller adaptively protects important semantic parts by adjusting the channel conditions. Simulation results demonstrate that the proposed RIS-SC framework can adapt to different channel environments and improve task performance under varying requirements, such as vertical semantic or true image reconstruction.

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.

Deep learning (DL) has been widely utilized for channel estimation and has resulted in significant performance improvements. However, most existing research only performs training and testing in relatively static scenarios, leading to a serious deterioration in dynamic scenarios. In this paper, we propose a robust channel estimator for orthogonal frequency-division multiplexing (OFDM) systems in dynamic scenarios called channel estimator Vision Transformer (CE-ViT) based on attention mechanism. We perform a patch embedding operation to process data in both the time and frequency domains, addressing the limitations of the attention mechanism in extracting 2D correlations. Additionally, we introduce tokens that reflect channel characteristics into the network to enhance the robustness. Experimental results show that CE-ViT outperforms the state-of-the-art DL-based methods. Moreover, the addition of tokens significantly improves the performance of CE-ViT in dynamic channel conditions.

For intelligent reflecting surface (IRS) aided down-link communication in frequency division duplex (FDD) systems, the overhead for the base station (BS) to acquire channel state information (CSI) is extremely high under the conventional “estimate-then-quantize” scheme, where the users first estimate and then feed back their channels to the BS. Recently, [1] revealed a strong correlation in different users' cascaded channels stemming from their common BS-IRS channel component, and leveraged such a correlation to significantly reduce the pilot transmission overhead in IRS-aided uplink communication. In this paper, we aim to exploit the above channel property for reducing the overhead of both pilot transmission and feedback transmission in IRS-aided downlink communication. Different from the uplink counterpart where the BS possesses the pilot signals containing the CSI of all the users, in downlink communication, the distributed users merely receive the pilot signals containing their own CSI and cannot leverage the correlation in different users' channels revealed in [1]. To tackle this challenge, this paper proposes a novel “quantize-then-estimate” protocol in FDD IRS-aided downlink communication. Specifically, the users first quantize their received pilot signals, instead of the channels estimated from the pilot signals, and then transmit the quantization bits to the BS. After de-quantizing the pilot signals received by all the users, the BS estimates all the cascaded channels by leveraging the correlation embedded in them, similar to the uplink scenario. Under this protocol, we propose efficient methods for quantization at the user side and channel estimation at the BS side. Furthermore, we manage to show both analytically and numerically the great overhead reduction in pilot transmission and feedback transmission arising from our proposed “quantize-then-estimate” protocol.

Reconfigurable intelligent surfaces (RISs) have received extensive concern to improve the performance of wireless communication systems. In this paper, a subarray-based scheme is investigated in terms of its effects on ergodic spectral efficiency (SE) and energy efficiency (EE) in RIS-assisted systems. In this scheme, the adjacent elements divided into a subarray are controlled by one signal and share the same reflection coefficient. An upper bound of ergodic SE is derived and an optimal phase shift design is proposed for the subarray-based RIS. Based on the upper bound and optimal design, we obtain the maximum of the upper bound. In particular, we analytically evaluate the effect of the subarray-based RIS on EE since it reduces SE and power consumption simultaneously. Numerical results verify the tightness of the upper bound, demonstrate the effectiveness of the optimal phase shift design for the subarray-based RIS, and reveal the effects of the subarray-based scheme on SE and EE.

Optimal detection of symbols transmitted over multiple-input multiple-output (MIMO) wireless channels is known to entail exponentially increasing complexity with MIMO dimensions, making it impractical for large-scale systems. Recently, Markov chain Monte Carlo (MCMC) has been combined with the gradient descent (GD) method to create a promising machine learning solution to this issue. This paper proposes a novel algorithm for approaching optimal detection via MCMC random walk accelerated by Nesterov's gradient method, efficiently exploring the discrete search space for MIMO detection. Our proposed method utilizes multiple GDs per random walk and guarantees high sampling efficiency while mitigating the complexity associated with matrix inversions. Simulation results and complexity analysis reveal that the proposed method achieves near-optimal performance and scales effectively to large MIMO dimensions.

Deep learning has been introduced to implicit channel state information (CSI) feedback and considerably outperforms codebook-based feedback methods adopted by existing systems. This work proposes a time correlation-aided deep learning-based implicit CSI feedback framework named Tbi-ImCsiNet. The long short-term memory network is introduced into the implicit CSI compression side and reconstruction side to extract and utilize the time correlation property among CSI matrices and improve the framework performance. Simulation results show that the proposed Tbi-ImCsiNet reduces approximately 58.3% of the feedback overhead compared with the method without time correlation utilization.

We propose a novel two-phase separate channel estimation scheme in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Based on the sparsity of the channel, the parameters in the user equipment (UE)-RIS channel and RIS-base station (BS) channel can be estimated in two phases and then utilized for channel reconstruction. Different from the cascaded estimation, the proposed method can achieve separate channel estimation and, thus, has higher practicability and creates room for more ingenious transceiver design. Moreover, a new pilot protocol for the RIS phase shift matrix configuration is proposed, such that new users will need only limited pilot resources. Through simulations, we prove that the proposed scheme can achieve precise channel reconstruction with low pilot overhead.

Deep learning (DL) has shown great potential in channel state information (CSI) feedback and localization. In this paper, a DL-based integrated CSI feedback and localization framework called FLnet, in which the feedback and localization tasks complement each other, is proposed. Specifically, unlike the existing works that sequentially realize the above two tasks, FLnet jointly designs the autoencoder-based feedback and deep neural networks (DNN)-based localization tasks. The encoder at the user equipment (UE) compresses and quantizes the downlink CSI. Then, the decoder and the DNN at the base station reconstruct the downlink CSI and predict the location of the UE based on the feedback information, respectively. The feedback and localization modules are trained together by an end-to-end approach. Simulation results show that the localization error of FLnet is reduced by 30% compared with that of the separate design while the feedback performance is comparable or even improved.

In this paper, we investigate a distributed reconfigurable intelligent surface aided massive access in multipleinput single-output non-orthogonal multiple access system with imperfect channel state information (CSI) and successive interference cancellation (SIC). In particular, a novel active and passive beamforming scheme are designed to fully eliminate the intercluster interference and improve the effective channel gain of the prioritized users, respectively. To study the performance of the proposed scheme, the exact channel statistics are derived to further analyze the outage probability of each user within a cluster. Finally, simulation results are presented to prove our theoretical analyses and reveal that: 1) enhancing the estimated accuracy of CSI and the ability of SIC process can significantly enhance the outage performance; 2) the proposed zero-forcing based scheme can obtain a higher system throughput compared to previous designs.

Wireless communication using fully passive metal reflectors is a promising technique for coverage expansion, signal enhancement, rank improvement and blind-zone compensation, thanks to its appealing features including zero energy consumption, ultra low cost, signaling- and maintenance-free, easy deployment and full compatibility with existing and future wireless systems. However, a prevalent understanding for reflection by metal plates is based on Snell's Law, i.e., signal can only be received when the observation angle equals to the incident angle, which is valid only when the electrical dimension of the metal plate is extremely large. In this paper, we rigorously derive a general reflection model that is applicable to metal reflectors of any size, any orientation, and any linear polarization. The derived model is given compactly in terms of the radar cross section (RCS) of the metal plate, as a function of its physical dimensions and orientation vectors, as well as the wave polarization and the wave deflection vector, i.e., the change of direction from the incident wave direction to the observation direction. Furthermore, experimental results based on actual field measurements are provided to validate the accuracy of our developed model and demonstrate the great potential of communications using metal reflectors.

The state-of-the-art coding schemes for topological interference management (TIM) problems are usually handcrafted for specific families of network topologies, relying critically on experts' domain knowledge. This inevitably restricts the potential wider applications to wireless communication systems, due to the limited generalizability. This work makes the first attempt to advocate a novel intelligent coding approach to mimic topological interference alignment via local graph coloring algorithms, leveraging the new advances of graph neural networks (GNNs) and reinforcement learning (RL). The extensive experiments demonstrate the excellent generalizability and transferability of the proposed approach, where the parameterized GNNs trained by small size TIM instances are able to work well on new unseen network topologies with larger size.

Channel reciprocity has been commonly assumed in time division duplex (TDD) massive multiple-input multiple-output (MIMO) communications, when acquiring downlink channel state information (CSI) at the base station through the uplink channel estimation. However, such channel reciprocity suffers from severe impairments when low-resolution analog-to-digital converters (ADCs) are introduced to reduce hardware cost and power consumption in practical systems. To compensate for such impairments, in this paper, we propose an efficient calibration state diagnosis scheme built upon compressive sensing techniques, leveraging the sparsity property of the calibration operations at the BS antennas under an additive quantization noise model. Compared with the traditional pilot-based approaches, our proposed scheme achieves comparable accuracy performance with 1-2 quantization bits and hence significantly reduces pilot overhead therein.

Reconfigurable intelligent surface (RIS)-empowered communication has recently drawn significant attention due to its superior capability in manipulating the wireless propagation environment. However, the channel modeling and measurement of RIS-assisted wireless communication systems in real environment has not been adequately studied. In this paper, we construct a channel measurement system using vector network analyzer (VNA) is used to investigate RIS-assisted wireless communication channel in outdoor scenarios at 2.6 GHz. New path loss (PL) models including angle domain information are proposed by refining the traditional close-in (CI) and floating-intercept (FI) models. In the proposed models, both influences of the distance from transmitter (TX) to RIS and the distance from receiver (RX) to RIS on the PL, are taken into account. In addition, the value of root mean square (RMS) delay spread of RIS-assisted wireless communication is found to be much smaller than that of the traditional non line-of-sight (NLOS) case, implying that RIS provides a virtual line-of-sight (LOS) link.

Deep learning has revolutionized the design of channel state information (CSI) feedback modules in wireless communication. However, designing an optimal neural network (NN) architecture for CSI feedback can be laborious and time-consuming, especially for customized networks targeting different scenarios. To address this challenge, this paper proposes the use of Neural Architecture Search (NAS) to automatically generate scenario-specific CSI feedback neural network architectures. By employing automated machine learning and gradient-based NAS, an efficient and cost-effective architecture design process is achieved with reduced reliance on expert knowledge and design time, thus lowering the design threshold. This approach leverages implicit scenario knowledge and integrates it into the scenario customization process in a data-driven manner, fully harnessing the potential of deep learning in a given scenario. Experimental results demonstrate that the generated architecture called Auto-CsiNet outperforms manually designed models in terms of reconstruction performance (improvement by approximately 14%) and complexity reduction (approximately 50%), highlighting the effectiveness of NAS-based automated solutions.

Extra large-scale multiple-input multiple-output (MIMO) has been recognized as one of the potential development directions of massive MIMO. By employing even more antennas than massive MIMO in the fifth-generation era, extra large-scale MIMO can further exploit the spatial domain resources and enable ultra high data rates, low latency communications as well as emerging applications, such as sensing and localization, in sixth-generation mobile communication systems. However, with the increase of the size of the antenna array, and the decrease of the distance between a user and the array, new channel properties, that did not manifest in conventional massive MIMO, start to kick in. Most importantly, existing research strategies pertaining to massive MIMO cannot be directly applied or simply extended to fit the extra large-scale MIMO case. Moreover, increasing the number of antennas will inevitably boost the total cost, which refers to not only the high hardware cost, but also the burden of vast processing and computations as well as the substantial training overhead. In this paper, we make a survey on the state-of-the-art on the new channel properties of and low-cost designs for extra large-scale MIMO systems. Particularly, we pursue a mathematical analysis to explain why the new features appear and illustrate how they affect the system model. Furthermore, we summarize and compare the low-cost designs from various perspectives and give our suggestions from a practical deployment point of view.

With the advances of the Internet of Things and mobile connectivity, location-based services are becoming increasingly popular and continue to enhance our experience. Multiple antennas have been pivotal in providing reliable wireless communications and high-resolution localization. If the antennas of the array are isotropic, then the simplified array manifold determined by the array geometry can be used to estimate the angle of arrival (AOA). However, in the real world, mobile handsets tend to have very limited space, where the practical antennas are equipped on the same ground plane, and the array geometry hardly obeys the rule of half-wavelength spacing. Therefore, a practical antenna couple signals from other antennas, causing a mutual coupling effect. Complex array manifolds are produced on an antenna even if the received signal is propagated through a single path channel. In addition, the irregular radiation pattern of each antenna further impairs the AOA estimation capability. Given the above effects, the simplified array manifold determined by the array geometry can no longer provide precise localization. In this article, we propose a generic array manifold model for both isotropic and practical antennas. We also present an efficient algorithm to enable AOA estimation on practical antennas on the basis of the proposed model and implement it on a 5G phone at a mid-band spectrum with a 100-MHz channel bandwidth. Results reveal the promising performance of the proposed model, with the AOA estimation errors lower than 10° in over 90% of the scenarios.

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G new radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28-GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160-MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve submeter-level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

The performance of transmission schemes is heavily influenced by the wireless channel, which is typically considered an uncontrollable factor. However, the introduction of reconfigurable intelligent surfaces (RISs) to wireless communications enables the customization of a preferred channel for adopted transmissions by reshaping electromagnetic waves. In this study, we propose multi-timescale channel customization for RIS-assisted multiple-input multiple-output systems to facilitate transmission design. Specifically, we customize a high-rank channel for spatial multiplexing (SM) transmission and a highly correlated rank-1 channel for beamforming (BF) transmission by designing the phase shifters of the RIS with statistical channel state information in the angle-coherent time to improve spectral efficiency (SE). We derive closed-form expressions for the approximation and upper bound of the ergodic SE and compare them to investigate the relative SE performance of SM and BF transmissions. In terms of reliability enhancement, we customize a fast-changing channel in the symbol timescale to achieve more diversity gain for SM and BF transmissions. Extensive numerical results demonstrate that flexible customization of channel characteristics for a specific transmission scheme can achieve a tradeoff between SE and bit error ratio performance.

Video conferencing has become a popular mode of meeting despite consuming considerable communication resources. Conventional video compression causes resolution reduction under a limited bandwidth. Semantic video conferencing (SVC) maintains a high resolution by transmitting some keypoints to represent the motions because the background is almost static, and the speakers do not change often. However, the study on the influence of transmission errors on keypoints is limited. In this paper, an SVC network based on keypoint transmission is established, which dramatically reduces transmission resources while only losing detailed expressions. Transmission errors in SVC only lead to a changed expression, whereas those in the conventional methods directly destroy pixels. However, the conventional error detector, such as cyclic redundancy check, cannot reflect the degree of expression changes. To overcome this issue, an incremental redundancy hybrid automatic repeat-request framework for varying channels (SVC-HARQ) incorporating a novel semantic error detector is developed. SVC-HARQ has flexibility in bit consumption and achieves a good performance. In addition, SVC-channel state information (CSI) is designed for CSI feedback to allocate the keypoint transmission and enhance the performance dramatically. Simulation shows that the proposed wireless semantic communication system can remarkably improve transmission efficiency.

智能超表面(RIS)是一种具有可重构电磁特性的二维人工材料. 通过改变嵌入RIS元件中的可调谐器件的控制信号, 可独立地调整每个元件表面电磁波的相位、振幅、偏振和频率响应, 因此能够以可编程的方式重塑空间电磁波的波前. RIS提供了强大的能力来控制无线传播环境, 提高无线通信网络的性能, 同时具有低复杂性、 结构简单、 低成本的优点, 在无线覆盖扩展、 无线覆盖增强以及无线系统容量的提高方面具有很好的优势. 目前, RIS辅助无线通信技术的发展聚焦以下几个关键点: 目前, RIS在无线通信中的未来应用仍面临多重机遇与挑战. 为此, 中国工程院院刊《信息与电子工程前沿(英文)》组织了本期专题. 经严格评审, 选出12篇论文, 包括2篇综述和10篇研究, 涵盖了物理实现、 算法设计和标准化等热点话题.

Reconfigurable intelligent surface (RIS) is widely accepted as a potential technology to assist in communication between base stations (BSs) and users in edge areas. We study the energy efficiency of a RIS-assisted multi-cell communication system with a realistic RIS power consumption model. With the goal of maximizing the energy efficiency of the system, we optimize the transmit beamforming vectors at the BS and the RIS phase shift matrix by a proposed alternative optimization algorithm. First, the transmit beamforming vector is optimized by solving the transformed weighted minimum mean square error (WMMSE) problem. Subsequently, to solve the inconvenience incurred by the discrete relationship between the RIS reflecting unit power consumption and its discrete phase shift, we use a continuous function to approximate their relationship. With this approximation, we can use the majorization minimization (MM) technique to optimize the continuous RIS phase shifts, and then quantize the obtained phase shifts to discrete ones. Simulation results demonstrate that the energy efficiency of the system is effectively optimized by the proposed algorithm.

The orthogonal time frequency space (OTFS) is poised to become a pivotal technology for the next generation of mobile communications, due to its inherent robustness against Doppler shift. By combining OTFS technology with massive multiple-input multiple-output (MIMO) technology, users can experience high-quality communication services even in highly mobile scenarios. In this letter, we extend the massive MIMO-OTFS system to an asymmetrical architecture with unequal number of transceiver radio frequency chains. To overcome the channel inconsistency and recover the downlink channel by partial uplink channel, we utilize coprime patterns and propose a channel estimation algorithm that firstly extracts the angle parameters from the virtual array and then estimates the remaining channel parameters, which effectively reduces the three-dimensional search space to two dimensions. Our numerical simulations demonstrate that the proposed algorithm enhances the accuracy of channel estimation with much lower complexity.

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

The use of a hybrid analog-digital architecture that connects one RF chain to multiple antennas through phase shifters is an energy-efficient solution for multiuser multiple-input multiple-output (MU-MIMO) systems. However, designing the hybrid precoder is challenging due to its nonconvex objective functions and constraints. Existing algorithms struggle with high computational complexity or poor performance, which often result from slow or no convergence. This study proposes a solution that leverages model-driven deep learning (DL) to maximize the spectral efficiency of MU-MIMO systems through hybrid precoding. The optimization problem is first transformed into a weighted minimum mean square error optimization. Then, it is combined with manifold optimization and DL to improve performance and simplify the process. The algorithm is designed to be robust in changing environments and utilizes DL to address imperfect channel state information. Simulation results show that the proposed method outperforms existing algorithms, is robust in changing system parameters, and can even outperforms fully digital precoding with the same number of antennas.

For frequency division duplex (FDD) systems, downlink channel state information (CSI) feedback is essential. Deep learning-based auto-encoder (AE) structures have shown promise in reducing feedback overhead. However, designing a super-large AE network to handle the CSI of all scenarios is not practical. A more practical approach is to divide the CSI dataset by region/scenario and use multiple simple AE networks. However, this method requires high memory capacity, making it unsuitable for low-end user equipment (UE). In this paper, we propose a new UE-friendly framework based on multi-tasking mode. Our framework, called single-encoder-to-multiple-decoders (S-to-M), uses multi-task-learning to design multiple independent AEs into a joint architecture with a shared encoder that corresponds to multiple task-specific decoders. We also integrate GateNet as a classifier to enable the base station to autonomously select the right task-specific decoder for the subregion. Our experiments on a simulated multi-scenario CSI dataset show that our proposed S-to-M framework outperforms other benchmark modes by significantly reducing model complexity and UE memory consumption.

This work considers a wireless powered communication network (WPCN), in which wireless nodes store the energy from an energy access point in their batteries for subsequent data transmission. An online energy consumption optimization strategy is proposed for adaptively determining the beamforming vector, data routing, network operation mode and transmitted power based only on the current state of WPCN. In most existing results, the energy/data transmission of WPCNs is based on the ideal battery models and the energy storage efficiencies therein are always assumed to be non-zero constants. Since the energy storage efficiency of batteries may be affected by the ambient environment or aging in real-time, this work considers a WPCN with a time-varying energy storage efficiencies sequence and correspondingly develops an improved Lyapunov optimization strategy to offset the impact of the time-varying energy storage efficiencies. More importantly, a distributed strategy is proposed to optimize the cooperation of wireless nodes over unrestricted numbers of hops, and thus the energy access point does not require channel state information of all data links and the data backlog queues of all nodes during the solving process. Accordingly, the computational burden at the energy access point is greatly reduced due to the use of this distributed strategy. Under this strategy, the time-averaged expected energy consumption of WPCN can be within a bounded gap of the minimum energy required to maintain stability of the network. Finally, the theoretical analysis is further corroborated by simulation results.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

The millimeter-wave (mmWave) communication technology, which employs large-scale antenna arrays, enables inherent sensing capabilities. Simultaneous localization and mapping (SLAM) can utilize channel multipath angle estimates to realize integrated sensing and communication design in 6G communication systems. However, existing works have ignored the significant overhead required by the mmWave beam management when implementing SLAM with angle estimates. This study proposes a joint beam management and SLAM design that utilizes the strong coupling between the radio map and channel multipath for simultaneous beam management, localization, and mapping. In this approach, we first propose a hierarchical sweeping and sensing service design. The path angles are estimated in the hierarchical sweeping, enabling angle-based SLAM with the aid of an inertial measurement unit (IMU) to realize sensing service. Then, feature-aided tracking is proposed that utilizes prior angle information generated from the radio map and IMU. Finally, a switching module is introduced to enable flexible switching between hierarchical sweeping and feature-aided tracking. Simulations show that the proposed joint design can achieve sub-meter level localization and mapping accuracy (with an error < 0.5 m). Moreover, the beam management overhead can be reduced by approximately 40% in different wireless environments.

Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem in millimeter wave (mmWave) communications. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Considering that the currently available RIS hardware cannot arbitrarily switch between the active (reflection with configurable phase response) and deactivate (absorption) modes, we suggest a new beam training method for RIS-assisted mmWave communication systems in this study. First, a predefined hierarchical codebook is created using the pattern synthesis method. Then, we provide a novel hierarchical beam training method using two multi-mainlobe codewords in each layer of the hierarchical codebook for beam sweeping. Combining the results of the beam identification in all the layers will yield the ultimate ideal beam direction. Theoretical analyses demonstrate that the suggested approach can effectively reduce training overhead while ensuring successful beam alignment. Simulation results show that the practical codebook can be created successfully, and the suggested method can achieve accurate beam alignment with reduced training overhead.

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein’s unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have a greater potential to assist in localization due to their ability to maintain high RCSs and create strong NLOS links. A one-stage linear weighted least squares estimator is proposed to simultaneously determine user equipment (UE) locations, velocities, and scatterer (or RIS) locations using line-of-sight (LOS) and NLOS paths. The estimator supports environment sensing and UE localization even using only NLOS paths. A second-stage estimator is also introduced to improve environment sensing accuracy by considering the nonlinear relationship between UE and scatterer locations. Simulation results demonstrate the effectiveness of the proposed estimators in rich scattering environments and the benefits of using NLOS paths for improving UE location accuracy and assisting in environment sensing. The effects of RIS number, size, and deployment on localization performance are also analyzed.

Massive multiple-input multiple-output (MIMO) systems have suffered from extremely high hardware complexity and cost because of the introduction of a tremendous number of antennas. Recently, one way to alleviate this is by considering the unequal uplink and downlink data transmission requirements and employing an asymmetrical transceiver. Such asymmetrical transceiver architecture, however, also brings out channel dimension inconsistency between the uplink and downlink. Thus, to well achieve the large array gain and fully exploit the potentials of asymmetrical transceiver-based massive MIMO systems, accurately recovering the full-dimensional downlink channel state information (CSI) based on the obtained small-dimensional uplink CSI is necessary. Nevertheless, the CSI at different antennas plays a different role in the CSI recovery due to the spatial correlation. Therefore, investigating appropriate antenna selection for asymmetrical transceiver-based massive MIMO systems is valuable and essential. To address this, we first formulate the antenna selection problem to minimize the mean-square recovery error of the full-dimensional downlink CSI in this paper. Then, two receive antenna selection algorithms are proposed by exploiting the low-rank property of the spatial correlation matrices under single-user scenarios. We also extend these algorithms to multi-user scenarios, and semi-closed-form optimal selection coefficients are derived. Numerical results demonstrate that, with the aid of the proposed antenna selection algorithms, the full-dimensional downlink CSI can be well recovered, which thus paves the way for asymmetrical transceiver-based massive MIMO systems to achieve their excellent downlink transmission performance with a much lower overall system hardware complexity and cost.

In this paper, we investigate a hybrid caching strategy maximizing the success transmission probability (STP) in a millimeter wave (mmWave) cache-enabled network. First, we derive theoretical expressions of the STP by utilizing stochastic geometry, then we consider the maximization of the STP by optimizing the design parameters. Considering the optimality structure of the NP-hard problem, the original problem is transformed into a multi-choice knapsack problem (MCKP). Finally, we investigate the impact of key network parameters on the STP. Numerical results demonstrate the superiority of the proposed caching strategy over the conventional caching strategies in the mmWave cache-enabled networks.

Reconfigurable intelligent surface (RIS) and index modulation (IM) are two emerging technologies, which show great potentials to achieve green and clean wireless communications, attracting extensive attention in recent years. This paper designs and implements an RIS-based IM transmission scheme that effectively integrates the two techniques. By utilizing the characteristics of RIS to realize flexible control of electromagnetic waves in a reconfigurable manner, IM wireless transmission can be directly realized without conventional radio frequency chains. The proposed approach is validated through the prototype system which is set up based on a fabricated phase-programmable RIS operating in the sub-6GHz frequency band. The experimental results convincingly verify the feasibility of the proposed scheme and suggest that RISs offer a cost-effective hardware architecture to realize IM with massive transmitting antennas.

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surfaces (RIS) an attractive solution for enabling the intelligent electromagnetic environment, but it brings the challenge of near-field propagation channels. In this paper, we consider the propagation feature of the spherical wavefront in the near field of the millimeter-wave/sub Terahertz (mmWave/subTHz) localization system with the assistance of a RIS. The localization problem is investigated based on the derived second-order Fresnel approximation of the near-field channel model. In addition, the RIS training phase shifts and pilots are carefully designed to increase the channel rank so that the channel covariance matrix can be efficiently estimated. Simulation results validate the proposed near-field channel approximation and the localization algorithm.

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

To address the inherent channel deficiency in strong line-of-sight (LoS) millimeter-wave (mmWave) wireless systems, distributed reconfigurable intelligent surfaces (RISs) are introduced in this study to customize the wireless channel. Capitalizing on the ability of the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. The number of elements needed for each RIS to combat the path loss is also considered in this research. Numerical results show that the effective channel rank can be flexibly and accurately customized according to the required number of data streams. Moreover, utilizing our proposal, every corner in the interested coverage can build the well-conditioned channel with small truncated condition number.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs) system with non-orthogonal-multiple access (NOMA), where the transmit signals from multiple UAVs to ground users are strengthened through a RIS. An innovative framework is designed to minimize the total power consumption of the system, by jointly optimizing the position of UAVs, RIS reflection coefficients, active beamforming vectors and decoding order. To solve this problem, we first consider the sub-solution of the UAV’s location which can be achieved via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we then yield the closed-form solution for RIS phase coefficients. Subsequently, the transmit power is obtained by the standard convex optimization methods. Finally, a dynamic-order decoding scheme is proposed to optimize the decoding order. Simulation results show that the resource allocation scheme can obviously reduce the total power consumption compared to the benchmark schemes.

The downlink channel state information (CSI) feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems with deep learning. The key idea of this work is to hide downlink CSI within the transmitted messages (e.g., images) with no transmission resource occupation and few effects on the message semantic. We propose a novel neural network framework, in which the user extracts and hides the CSI features within the images by networks, and the base station recovers the CSI from the transmitted images. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the transmitted images, including the image quality and classification accuracy.

The extremal inequality approach plays a key role in network information theory problems. In this paper, we propose a novel monotone path construction in product probability space. The optimality of Gaussian distribution is then established by standard perturbation arguments.

In recent years, optimization of the successful transmission probability (STP) in wireless cache-enabled networks has been studied extensively. However, few works have examined the real-time performance of the cache-enabled networks. In this paper, we investigate the performance of the cache-enabled networks with real-time guarantees by adopting age of information (AoI) as the metric to characterize the timeliness of the delivered information. We establish a spatial-temporal model by utilizing stochastic geometry and queueing theory which captures both the temporal traffic dynamics and the interferers’ geographic distribution. Under the random caching framework, we achieve the closed-form expression of AoI by adopting the maximum average received power criterion for the user association. Finally, we formulate a convex optimization problem for the minimization of the Peak AoI(PAoI) and obtain the optimal caching probabilities by utilizing the Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the random caching strategy is a better choice than both the most popular caching (MPC) and uniform caching (UC) strategies when it comes to improving the real-time performance for the cached files as well as maintaining the file diversity.

In this paper, we investigate the resource allocation problem in a dynamic vehicular environment, where multiple vehicle-to-vehicle links attempt to reuse the spectrum of vehicle-to-infrastructure links. It is modeled as a deep reinforcement learning problem that is subject to proximal policy optimization. Training a well-performing policy usually requires a massive amount of interactions with the environment for a long time and thus is typically performed on a simulator. However, an agent well trained in a simulated environment may still fail when deployed in a live network, due to inevitable difference between the two environments, termed reality gap. We make preliminary efforts to address this issue by leveraging meta reinforcement learning that allows the learning agent to quickly adapt to a new environment with minimal interactions after being trained across a variety of similar tasks. We demonstrate that only a few episodes are required for the meta trained policy to adapt to a new environment and the proposed method is shown to achieve near-optimal performance and exhibit rapid convergence.

Integrated sensing and communication (ISAC) is considered as a promising technique to provide mutually enhanced performance in future millimeter-wave communication systems. However, the non-line-of-sight (NLOS) components are usually treated as interference for radio-based localization in the existing literature, although they are proved to capture certain information about the radio propagation environment. In this study, we focus on the simultaneous estimation of location and velocity for user equipment (UE) as well as locations for scatterers by harnessing NLOS path measurements. Specifically, we integrate LOS and NLOS path measurements into a onestage linear weighted least squares estimator, where NLOS paths contribute to the estimation of scatterers (environment sensing), and also assist the localization of UE. We have also proved that the estimator is capable of localization in terrible situations when all the LOS paths are blocked. Comprehensive simulation results show that the estimator can attain the Cramer-Rao lower bound under small noise levels and outperform the state-of-the-art methods.

With the large-scale deployment and commercialization of 5G, the traditional coverage enhancement technology is facing severe challenges due to high hardware cost and power consumption. At the same time, the emerging reconfigurable intelligent surface (RIS) technology, which can flexibly regulate incident waves and optimize the transmission path of wireless signals, is considered to be one of the most potential 6G key enabling technologies. This paper presents a 5G commercial network coverage enhancement prototype system based on RIS, which directly uses the base station in China Mobile 5G commercial network as transmitter, 5G network signal test terminal as receiver, and RIS code optimization algorithm is employed. The whole system forms a closed optimization loop of “obtaining data-analyzing data-feedback code” and is tested in multiple scenarios. In selected scenarios, the prototype system achieves a received power gain of about 6 dB, which is significant for the research of RIS in 5G commercial network.

Recently, task-oriented semantic communication has received increasing attention due to its potential to transform the communication landscape by going beyond the Shannon paradigm. While most existing researches focus on a single task, we propose a multi-task semantic communication system for text tasks, inspired by the impressive results of multitask learning and bidirectional encoder representations from transformers (BERT). Based on BERT, the proposed system extracts semantic information from the text at the transmitter and then transmits it to the receiver, which aims to accomplish a series of different tasks. The transmitter and receiver would be trained jointly in an end-to-end manner. Compared with the traditional communication system, the proposed model is more robust to distortion caused by physical channels and exhibits better performance, especially in the low signal-to-noise ratio regime. Moreover, we investigate the relationship between the number of tasks and the required length of transmitted symbols in a multi-task setting.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for the next generation networks. By utilizing tools from stochastic geometry, we develop a meta distributed-based analytical framework to study the effect of the large-scale deployment of the RIS on the performance of a millimeter wave (mmWave) cellular network. Specifically, the locations of the base stations (BSs) are modeled as Poisson point processes (PPPs). In addition, the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. By considering the randomness of the locations and orientations of the RISs and the particular characteristics of mmWave communications, we provide a statistical characterization of the path loss for the BSs and RISs and derive the analytical expressions for the k-th moment of the conditional success probability, the area spectral efficiency and the energy efficiency. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs.

A fine-grained analysis of network performance is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as a Poisson point process (PPP). By utilizing stochastic geometry and queueing theory, we characterize the spatial and temporal randomness while the special characteristics of mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming, are incorporated into the analysis. We derive the moments of the conditional successful transmission probability (STP). By taking the temporal random arrival of traffic into consideration, an equation on the meta distribution is formulated and the meta distribution can be obtained in a recursive manner. The numerical results reveal the impact of the key network parameters, such as the SINR threshold and the blockage parameter, on the network performance.

Recently, unmanned aerial vehicle (UAV) swarm communication has drawn much attention in search and rescue (SAR) missions owing to its wide wireless coverage and increasing autonomy in navigation. In this article, we consider maximizing the downlink wireless coverage of a UAV swarm in an unknown mission area by controlling the quasistationary deployments of UAVs. Particularly, the stochastic wireless link failures caused by channel fading and noise in UAV-to-UAV communication links are considered in coverage control. Specifically, due to delay sensitivity and onboard energy limitation of UAV-enabled SAR networks, we study a distributed control strategy where swarm UAVs can address the coverage problem by exchanging only the local information. In this case, the wireless coverage problem is divided into several distributed optimization subproblems. However, due to the integer variable and nonlinear constraints, each subproblem is nonconvex and mutually coupling, which makes it difficult to solve via standard convex optimization solvers. Thus, we model the UAV swarm network as an undirected random graph and then solve the optimization subproblems by formulating a UAV swarm wireless coverage game. As per the designed utility function and potential function of the formulated game, existence of the pure Nash equilibrium is discussed and a distributed algorithm is developed to achieve the best Nash equilibrium. We analyze the convergence property and computational complexity of the proposed algorithm. Meanwhile, we analyze effects of the initial learning rate and step size on algorithm performance from both theoretical and simulation results. Simulation results show that the proposed algorithm improves the coverage by around 58% when compared with initial performance.

To support more Internet-of-Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macrocell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.

Channel state information (CSI) plays a crucial role in the capacity of multiple-input and multiple-output systems, but CSI feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems through deep learning. The key idea is to hide/superimpose CSI in transmitted messages (e.g., images) with no transmission resource occupation and few effects on message semantics. Concretely, we introduce a novel neural network framework in which the user extracts and hides CSI features in images, and the base station recovers the CSI from the transmitted images. However, the essential source coding (e.g., JPEG compression) before data transmission causes two problems in the proposed EliCsiNet framework when applied in practical systems. First, the compression inevitably disturbs the information of the hidden CSI in images and affects the CSI reconstruction accuracy. Therefore, a two-stage separable training strategy, which includes coding-free end-to-end and coding-aware decoder-only training, is adopted to reduce these effects. Second, the bit length of the images coded via JPEG is unpredictable and uncontrollable, and CSI superimposition may lead to an increase in the bit length of the coded images. To avoid this issue, we divide a full image into several sub-blocks and select the one with the smallest length increment. Image entropy is also introduced to accelerate block selection. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the features properties of transmitted images, including image quality and bit length.

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client’s differential privacy (DP) requirement. We solve this problem in a multi-agent multi-armed bandit (MAMAB) framework to deal with the situation where there are multiple clients confronting different unknown transmission environments, e.g., channel fading and interference. Specifically, we first transform long-term constraints on both training performance and each client’s DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., a modified Hungarian algorithm and greedy matching with a better alternative (GMBA), of which the former can achieve the optimal solution with high complexity while the latter approaches a better trade-off by enabling verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed.

Integrating sensing functions into future mobile equipment has become an important trend. Realizing different types of sensing and achieving mutual enhancement under the existing communication hardware architecture is a crucial challenge in realizing the deep integration of sensing and communication. In the 5G New Radio context, active sensing can be performed through uplink beam sweeping on the user equipment (UE) side to observe the surrounding environment. In addition, the UE can perform passive sensing through downlink channel estimation to measure the multipath component (MPC) information. This study is the first to develop a hybrid simultaneous localization and mapping (SLAM) mechanism that combines active and passive sensing, in whichmutual enhancementbetween the two sensing modes is realized in communication systems. Specifically, we first establish a common feature associated with the reflective surface to bridge active and passive sensing, thus enabling information fusion. Based on the common feature, we can attain physical anchor initialization through MPC with the assistance of active sensing. Then, we extend the classic probabilistic data association SLAM mechanism to achieve UE localization and continuously refine the physical anchor and target reflections through the subsequent passive sensing. Numerical results show that the proposed hybrid active and passive sensing-based SLAM mechanism can work successfully in tricky scenarios without any prior information on the floor plan, anchors, or agents. Moreover, the proposed algorithm demonstrates significant performance gains compared with active or passive sensing only mechanisms.

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

Reconfigurable intelligent surfaces (RISs) offer an entirely new route to alter the propagation properties of electromagnetic waves and thus control their reflection, refraction, and scattering features in arbitrary manners. Such physical attributes are perceived to bring about fundamental influence on the modern wireless communication system due to the possibilities to establish artificial and controllable propagation environments for radio signals, which no longer rely on the complex encoding, decoding, and other signal processing techniques. Recent studies reveal that the wave manipulation is not the only skill of the RISs. With the rapid developments of space–time digital metasurface and information metasurface, there has been increasing attention focused on the information manipulation via these artificial surfaces. In this article, we provide an overview of the theoretical models of the space–time digital metasurface and information metasurface, the mechanisms of wavefront shaping, and the signal modulations in space and time domains during the wave–matter interactions. We will also address some practical issues during implementations of the reconfigurable intelligent metasurfaces and the associated hardware architectures at microwave frequencies to realize simplified radio frequency transmitters. Several modulation schemes and the corresponding demonstration systems are introduced to illustrate the powerful abilities of the reconfigurable intelligent metasurfaces. Potential research directions of this technique are briefly discussed for their potential applications in future wireless networks.

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% – 40.0% of overhead compared with the Type I codebook under different antenna configurations. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with the Type II codebook when ignoring and considering extracting subband correlation, respectively.

The autoencoder empowered by artificial intelligence has shown considerable potential in solving channel state information (CSI) feedback problems in frequency-division duplexing systems. However, this method needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. This paper proposes an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to feedback process through neural networks (NNs) at the base station. Only an NN-based refining operation is added after the common standardized feedback approach. The NNs learn to automatically extract environment features and utilize the channel statistics through large volumes of recorded data. The NNs also use the partial correlation between bidirectional channels to further improve feedback performance. In addition, to deal with downlink channel estimation errors, we propose two strategies to reduce their effects using an NN-based denoise module. The proposed framework can be easily embedded in most existing codebook-based feedback methods, such as random vector quantization. Two channel datasets generated by QuaDRiGa and measured in practical systems are adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline codebook because of more accurate feedback.

In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overhead. In this work, we propose an uplink-aided downlink channel acquisition framework using deep learning to reduce such overhead. We consider the entire downlink CSI acquisition process, including the downlink pilot design, channel estimation, and feedback. First, we propose an adaptive pilot design module by exploiting the correlation in magnitude among bidirectional channels in the angular domain to improve channel estimation. Second, to avoid the bit allocation problem during the feedback module, we concatenate the complex channel and embed the uplink channel magnitude to the channel reconstruction at the base station. Finally, we combine the two modules and compare two popular uplink-aided downlink channel acquisition frameworks. One framework estimates and subsequently feeds back the channel at the user equipment. In the other framework, the user equipment directly feeds back the received pilot signals to the base station. Results reveal that with the help of the uplink channel, directly feeding back pilot signals can save approximately 20% of feedback bits. This work thus provides a guideline for future research.

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user equipment) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including bitstream generation, multirate feedback, imperfect feedback, NN complexity, training dataset collection, online training, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

Recently, semantic communication has been brought to the forefront because deep learning (DL)-based methods, such as Transformer, have achieved great success in semantic extraction. Although semantic communication has been successfully applied in sentence transmission to reduce semantic errors, the existing architecture is usually fixed in terms of codeword length and inefficient and inflexible for varying sentence lengths. In this study, we exploit hybrid automatic repeat request (HARQ) to reduce the semantic transmission error further. We combine semantic coding (SC) with Reed-Solomon (RS) channel coding and HARQ (called SC-RS-HARQ). SC-RS-HARQ exploits the superiority of SC and the reliability of conventional methods successfully. Although SC-RS-HARQ can be easily applied in existing HARQ systems, we also develop an end-to-end architecture called SCHARQ to pursue enhanced performance. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for semantic sentence transmission and the sentence error rate. We also attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve wrong sentences with similar semantic information and conserve transmission resources.

We consider the channel estimation and environment mapping problems in multiple-input multiple-output orthogonal frequency division multiplexing systems empowered by intelligent reconfigurable surfaces (IRSs). In order to acquire more in-depth environmental information, as well as, to flexibly take into account existing real-life infrastructure, we propose a novel three-dimensional conformal IRS architecture consisting of reflective unit cells distributed on curved surfaces. We model the training signal as a third-order canonical polyadic tensor and construct a tensor factorization problem. Given specific conditions on the allocated temporal-frequency training resources, we develop four channel estimation approaches, i.e., least squares, direct, wideband direct and wideband subspace methods, by leveraging tensor techniques and nonlinear system solvers. By fully exploiting the characteristics of conformal IRSs, we propose two decoupling modes to precisely recover the multipath parameters without ambiguities, which cannot be supported by the traditional IRS planar topologies. We implement scatterer mapping and user positioning tasks based on precise parameter estimates. Simulation results indicate that the proposed conformal IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of environment mapping.

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna’s directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for millimeter wave (mmWave) networks. In this paper, we utilize tools from stochastic geometry to study the performance of a RIS-assisted mmWave cellular network. Specifically, the locations of the base stations (BSs) and the midpoints of the blockage are modeled as two independent Poisson point processes (PPPs), where the blockages are modeled by a Boolean model and a fraction of the blockages are coated with RISs. The particular characteristics of mmWave communications, i.e., directional beamforming and different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) propagation, are incorporated into our analysis. We derive analytical expressions for the success probability and the area spectral efficiency. The success probability under the special case where the blockage parameter is sufficiently small is also derived. Numerical results demonstrate that better coverage performance and higher energy efficiency can be achieved by a large-scale deployment of RISs. In addition, the tradeoff between the BS and RIS densities is investigated and the results show that the RISs can indeed enable the traditional networks to improve the success probability, especially for the cell-edge region, with limited power consumption.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

In frequency division duplexing systems, the base station (BS) acquires downlink channel state information (CSI) via channel feedback, which has not been adequately investigated in the presence of RIS. In this study, we examine the limited channel feedback scheme by proposing a novel cascaded codebook and an adaptive bit partitioning strategy. The RIS segments the channel between the BS and mobile station into two sub-channels, each with line-of-sight (LoS) and non-LoS (NLoS) paths. To quantize the path gains, the cascaded codebook is proposed to be synthesized by two sub-codebooks whose codeword is cascaded by LoS and NLoS components. This enables the proposed cascaded codebook to cater the different distributions of LoS and NLoS path gains by flexibly using different feedback bits to design the codeword structure. On the basis of the proposed cascaded codebook, we derive an upper bound on ergodic rate loss with maximum ratio transmission and show that the rate loss can be cut down by optimizing the feedback bit allocation during codebook generation. To minimize the upper bound, we propose a bit partitioning strategy that is adaptive to diverse environment and system parameters. Extensive simulations are presented to show the superiority and robustness of the cascaded codebook and the efficiency of the adaptive bit partitioning scheme.

Reconfigurable intelligent surface (RIS) has drawn worldwide attention because of its attractive capability to improve the electromagnetic propagation environment and surprising advantages of low cost and low power consumption. Recently, RIS is further advanced to realize the independent control of two orthogonal polarizations in real time. In this paper, we study the dual-polarized RIS-assisted mobile communication system and investigate its performance under practical polarization imperfections. Specifically, we provide a tight upper bound of the ergodic spectrum efficiency. Through the bound, we find that the performance is greatly dependent on the polarization imperfections and RIS phase shifts of the RIS-assisted link, when the number of base station antennas is large enough and the deterministic component exists in the RIS-assisted link. On basis of this tight bound, we propose an approximate optimal RIS phase shift design to maximize the ergodic spectrum efficiency upper bound and emphasize the necessity to make phase adjustment between two polarizations of the RIS. Theoretical analysis further gives guidance for the configuration of RIS. Numerical results demonstrate that the upper bound is very tight and the proposed phase shift design with adjustment is approximate optimal.

We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers’ location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the “double count” and “data reliability” problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference ratio for the cache-enabled networks where the locations of the base stations are modeled as a Poisson point process. With the application of the random caching and the random discontinuous transmission schemes, we derive the moments of the conditional successful transmission probability, the exact meta distribution and its beta approximation by utilizing stochastic geometry. The closed-form expressions of the mean and variance of the local delay (i.e., the jitter) are also derived. We then consider the maximization of the mean successful transmission probability and the minimization of the average system transmission delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of successful transmission probability, successful transmission probability variance, meta distribution, mean local delay and jitter.

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.

Mobile edge computing (MEC) has recently emerged as an enabling technology to support computation-intensive and delay-critical applications for energy-constrained and computation-limited Internet of Things (IoT). Due to the time-varying channels and dynamic task patterns, there exist many challenges to make efficient and effective computation offloading decisions, especially in the multi-server multi-user IoT networks, where the decisions involve both continuous and discrete actions. In this paper, we investigate computation task offloading in a dynamic environment and formulate a task offloading problem to minimize the average long-term service cost in terms of power consumption and buffering delay. To enhance the estimation of the long-term cost, we propose a deep reinforcement learning based algorithm, where deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Simulation results validate that the proposed DDPG-D3QN algorithm exhibits better stability and faster convergence than the existing methods, and the average system service cost is decreased obviously.

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage thresholding algorithm-based channel estimator (LISTA-CE) based on deep learning. The proposed channel estimator can learn to transform the beam-frequency mmWave channel into the domain with sparse features through training data. The transform domain enables us to adopt a simple denoiser with few trainable parameters. We further enhance the adaptivity of the estimator by introducing hypernetwork to automatically generate learnable parameters for LISTA-CE online. Simulation results show that the proposed approach can significantly outperform the state-of-the-art deep learning-based algorithms with lower complexity and fewer parameters and adapt to new scenarios rapidly.

In the blind areas of current fifth generation (5G) networks, e.g. the remote areas, unmanned aerial vehicles (UAVs) can be used to provide on-demand connectivity. To efficiently serve the sparsely distributed users in these areas, non-orthogonal multiple access (NOMA) could be adopted to exploit the user distinguish ability in the power domain. In this paper, we consider a NOMA-based multi-UAV-aided network, where a swarm of coordinated UAVs transmit messages to unevenly distributed users through a virtual multiple-input-multiple-output (MIMO) channel. We formulate a power allocation problem to maximize the minimum user rate to assure fairness in the transmission. Different from existing studies, we use only the large-scale channel state information (CSI) in the transmission design, which characterizes the basic channel feature, and can be obtained using the location information of UAVs/users. By leveraging the random matrix theory and successive convex optimization tools, we propose an iterative algorithm to solve the problem after a series of problem transformation. Simulation results show that the proposed power allocation scheme outperforms existing methods, which shows the potential of multi-UAV-aided NOMA communications for coverage enhancement in remote areas.

In this paper, we focus on the local delay for the cache-enabled networks where the locations of the base stations (BSs) are modeled as a Poisson point process (PPP). With the application of the random caching and the random discontinuous transmission (DTX) schemes, we derive the closed-form expression of the mean local delay. We then consider the minimization of the mean local delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of mean local delay.

Reconfigurable intelligent surface (RIS) is a low-cost device envisioned to achieve substantial promotion in both spectrum and energy efficiency in the future wireless communication systems. In this paper, we investigate the downlink transmission of the RIS-enabled orthogonal frequency division multiplexing (OFDM) system, and propose a low-complexity passive beamforming optimization algorithm to maximize the achievable sum-rate over all subcarriers. The passive beam-forming optimization is a NP-hard problem due to the RIS-induced non-convex unit modulus constraints. We conquer this difficulty by exploiting a fractional programming (FP) based approach combined with an efficient manifold optimization (MO) method. With the proposed low-complexity passive beamforming optimization algorithms and water-filling power allocation, the achievable sum rate can then be maximized through alternating optimization (AO). Simulation results indicate that the proposed AO based algorithm performs well in achieving high average sum-rate with a fast convergence rate.

In frequency-division duplexing systems, the downlink channel state information (CSI) should be fed back through an uplink transmission to reap the benefits of the massive multiple-input and multiple-output system, thereby leading to a large feedback overhead. The autoencoder-based architecture empowered by artificial intelligence has shown considerable potential in solving the CSI feedback problem. This method, however, needs to completely change the existing feedback schemes, which is difficult to deploy in the next few years. In this paper, we propose an environment knowledge-aided codebook-based CSI feedback framework, which retains the existent codebook-based scheme while introducing environment knowledge to the feedback process through neural networks (NNs) at the base station. The NNs learn to automatically extract the environment features and utilize the channel statistics through large volumes of recorded data. The channel dataset, which is generated by QuaDRiGa software, is adopted to evaluate the proposed methods. Results show that the proposed method offers over 100% increase in the throughput compared with the baseline feedback codebook because of the more accurate CSI feedback.

This paper investigates the downlink OFDM transmission assisted by reconfigurable intelligent surface (RIS). With single antenna implemented at both the base station (BS) and each user, we focus on the design of the phase shifts for the RIS, as well as power allocation on each subcarrier to improve the spectrum efficiency. To reduce the computation delay, we propose a deep reinforcement learning (DRL) based algorithm to optimize the RIS phase shift parameters, while allocating power on each subcarrier via water filling. Numerical results reveal that the proposed DRL-based framework can achieve a performance almost the same with that of successive convex approximation (SCA), while the computation delay can be greatly reduced.

In massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) is required by the base station (BS) to achieve high-performance gains. In frequency division duplexing (FDD) systems, the downlink CSI matrix should be sent back to the BS; unfortunately, the computational and overhead cost of this task is inherently high. Recently, deep learning has been increasingly applied in the space of CSI feedback. However, neural networks entail extra memory and computational requirements, which undermines the deployment of CSI feedback neural networks at the user equipment (UE) side. The conventional lightweight methods such as pruning and quantization requires heavy workload of experiments and difficulty of individually designing training methods for each neural network (NN). In this paper, a novel network lightweight method utilizing knowledge distillation as a training method is introduced to lighten the computation burden of the encoder at the UEs. Knowledge distillation (KD) aims at transferring knowledge from a complex network to a simple network and improving the performance of the simple network close to the complex network. Our numerical experiments demonstrate that the performance of the proposed network can be improved with KD.

In this paper, we investigate a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, mobile users associate with the serving base stations (BSs) and offload their computation-intensive proof-of-work mining tasks to the mobile edge computing server of the macro-cell BS. We initialize the user association strategy by using the traditional nearest BS association algorithm in the blockchain-enabled HetNet. Then, we formulate the computation offloading problem and design an alternating iterative algorithm to attain the computing demand strategies for all users. Based on obtained computing demand strategies, we propose a modified user association algorithm in order to improve the data congestion of BSs. Analytical results show that the blockchain-enabled HetNet can serve more users, and the proposed algorithms can effectively obtain strategies of user association and computation offloading. Numerical results demonstrate that the proposed alternating iterative algorithm for computation offloading has fast convergence and good stability, and the proposed modified user association algorithm can avoid data congestion better than the traditional nearest BS association algorithm.

Dual-polarized antenna arrays are widely used to reduce the array aperture and expand the channel capacity in multiple-input multiple-output (MIMO) systems. However, a challenge for doubling the number of antennas is how to reconstruct the large-dimensional channel with low complexity. In this paper, we prove the similarity of the delays, AOAs, and the number of the paths between channels in different polarizations, which is the property of polarization-independency, and verify it through both theoretical analysis and experiments. On basis of the similarity among polarizations, we propose a dual-polarized channel extrapolation scheme with low pilot cost and low computational complexity. Simulations and experiments are conducted to examine the performance of the proposed extrapolation scheme. Results show that the proposed dual-polarized channel extrapolation scheme is feasible in practice and can achieve a good channel reconstruction performance with reduced computational complexity.

In this article, we develop a framework for the analysis of massive multiple-input-multiple-output (MIMO) systems where multiple types of devices with different configurations and requirements co-exist, by taking into account the randomness of spatial locations and temporal traffic. A tight closed-form approximation of the spatial mean packet throughput, which denotes the average number of packets that are successfully transmitted at any unit time slot and area is derived, by using tools from the stochastic geometry and queuing theory, which captures all the key features of the devices in the Internet of Things (IoT). Based on the analysis, we investigate the optimal scheduling number for each type of devices that maximizes the spatial mean packet throughput while meeting devices' delay constraints. It is found that when the base station (BS) has excessive number of antennas ( M), the BS should schedule all devices under its coverage, regardless of devices' variances on spatiotemporal configurations and demands. However, when M is limited, the BS should have a bias on scheduling devices with heavier traffic, lower decoding threshold, or higher transmit power. On this basis, if the delay constraint of one device becomes stricter, it will be scheduled more often to access the radio channel, which acts more significantly when the ratio of M to the deployment density of devices gets smaller.

To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

The potentials of massive multiple-input multiple-output (MIMO) are all based on the available instantaneous channel state information (CSI) at the base station (BS). Therefore, the user in frequency-division duplexing (FDD) systems has to keep on feeding back the CSI to the BS, thereby occupying large uplink transmission resources. Recently, deep learning (DL) has achieved great success in the CSI feedback. However, the existing works just focus on improving the feedback accuracy and ignore the effects on the following modules, e.g., beamforming (BF). In this paper, we propose a DL-based CSI feedback framework for BF design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the BF performance gain rather than the feedback accuracy. We apply it to two representative scenarios: single- and multi-cell systems. The CsiFBnet-s in the single-cell system is based on the autoencoder architecture, where the encoder at the user compresses the CSI and the decoder at the BS generates the BF vector. The CsiFBnet-m in the multi-cell system has to feed back two kinds of CSI: the desired and the interfering CSI. The entire neural networks are trained by an unsupervised learning strategy. Simulation results show the great performance improvement and complexity reduction of the CsiFBnet compared with the conventional DL-based CSI feedback methods.

Future wireless communication networks are expected to fulfill the unprecedented performance requirements to support our highly digitized and globally data-driven society. Various technological challenges must be overcome to achieve our goal. Among many potential technologies, reconfigurable intelligent surface (RIS) and artificial intelligence (AI) have attracted extensive attention, thereby leading to a proliferation of studies for utilizing them in wireless communication systems. The RIS-based wireless communication frameworks and AI-enabled technologies, two of the promising technologies for the sixth-generation networks, interact and promote with each other, striving to collaboratively create a controllable, intelligent, reconfigurable, and programmable wireless propagation environment. This paper explores the road to implementing the combination of RIS and AI, more specifically, integrating AI-enabled technologies into RIS-based frameworks for maximizing the practicality of RIS to facilitate the realization of smart radio propagation environments, elaborated from shallow to deep insights. We begin with the basic concept and fundamental characteristics of RIS, followed by the overview of the research status of RIS. Then, we analyze the inevitable trend of RIS to be combined with AI. In particular, we focus on recent research about RIS-based architectures embedded with AI, elucidating from the intelligent structures and systems of metamaterials to the AI-embedded RIS-assisted wireless communication systems. Finally, the challenges and potential of the topic are discussed.

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

Multiband operation in millimeter wave (mmWave) will obtain lots of performance gain by offering larger bandwidth. However, the prerequisite is the acquisition of accurate channel state information (CSI), which is a knotty task with hybrid analog/digital architecture. This study devotes to efficiently reconstruct and track the time drifting multiband channel to keep CSI precise in time division duplex (TDD) mmWave multiple-input-multiple-output system with hybrid analog/digital architecture. Utilizing spatial reciprocity, an efficient multiband channel reconstruction scheme is introduced, which elaborately estimates the central sub-band channel and then reconstructs side sub-band channels from the central one. To this end, a beam training-based Newtonized orthogonal matching pursuit (BT-NOMP) algorithm is proposed to estimate the central sub-band channel. With the help of frequency-independent parameters extracted from BT-NOMP, side sub-bands channel can be well reconstructed with additional low-complexity path gains estimation process. Furthermore, to avoid frequent channel reconstruction in a slightly drifting channel meanwhile keep the CSI accurate, a rotated beam-based channel tracking algorithm is developed using historical observations of channel parameters. Numerical results prove the efficiency of the proposed multiband channel reconstruction scheme and the accuracy of the channel tracking algorithm.

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.

In adversarial scenarios, it is crucial to timely monitor what tactical messages that opponent transmitters are sending to intended receiver(s), and disrupt the transmissions immediately if in need. The issue becomes more challenging in face of an intelligent transmitter. To address the above-stated issue, a full-duplex (FD) technique is utilized to enable simultaneous jamming and eavesdropping (SJE) at a friendly jammer node. In particular, the “Two Birds with One Stone” strategy is utilized at the jammer node to realize effective rate degradation and information eavesdropping. A confrontation game between an intelligence-empowered FD jammer and its opponent is investigated. Specifically, to capture their adversarial relationship in an environment with incomplete information, a power-domain Bayesian-Stackelberg game is proposed. The existence of a Stackelberg equilibrium (SE) power solution is proved. The semi-closed-form solutions of SE are derived, which are proved to be asymptotically optimal (have a gap of less than 1% with the exact utility), and improves the jammer node 10% utility compared with the Nash equilibrium. Additionally, the SJE strategy outperforms the half-duplex (HD) and other benchmark schemes.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) techniques plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We present a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure. We then exploit previous results in task-based quantization to show characterized the achievable OFDM recovery accuracy for a given DMA configuration in the presence of bit-constrained ADCs, and propose methods for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that by properly exploiting the spectral diversity of DMAs, notable performance gains are obtained over existing designs of conventional hybrid architectures, demonstrating the potential of DMAs for realizing high performance massive antenna arrays of reduced cost and power consumption.

Emergence of various types of wireless applications has brought about fast growing and diversified traffic in cellular networks. To gain a comprehensive understanding of the influences caused by the differentiated and dynamic traffic is vital for the design of the next-generation wireless networks. In this paper, we develop a mathematical framework for meta-distribution analysis by utilizing queueing theory and stochastic geometry to capture the spatial (geographical location) and temporal randomness (queue status) of traffic. We derive the${k}$-th moment of the conditional successful transmission probability (STP) and the closed-form expressions of the meta distribution for the cell-center users (CCUs) and the cell-edge users (CEUs), respectively. The results are further extended to obtain the analytical expression of the meta distribution by taking the temporal random arrival of traffic into consideration. Moreover, the mean local delays for the CCUs and CEUs are derived. Finally, the impact of key network parameters on the meta distribution and the corresponding mean local delay is investigated.

The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits their further improvement and extension. In this paper, we present preliminary theoretical analysis on DL based channel estimation for single-input multiple-output (SIMO) systems to understand and interpret its internal mechanisms. As deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and asymptotically approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.

We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.

Intelligent reflecting surface (IRS) is a promising technology to reconfigure wireless channels, which brings a new degree of freedom for the design of future wireless networks. This article proposes a new three-dimensional (3D) wireless system architecture enabled by aerial IRS (AIRS). Compared to the conventional terrestrial IRS, AIRS enjoys more deployment flexibility as well as wider-view signal reflection, thanks to its high altitude and thus more likelihood of establishing line-of-sight (LoS) links with ground source/destination nodes. We aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in a target area by jointly optimizing the transmit beamforming for the source node, as well as the placement and 3D passive beamforming for the AIRS. The formulated problem is non-convex and difficult to solve. To gain useful insights, we first consider the special case of maximizing the SNR at a given target location, for which the optimal solution is obtained in closed-form. The result shows that the optimal horizontal AIRS placement only depends on the ratio between the source-destination distance and the AIRS altitude. Then for the general case of AIRS-enabled area coverage, we propose an efficient solution by decoupling the AIRS passive beamforming design to maximize the worst-case array gain, from its placement optimization by balancing the resulting angular span and the cascaded channel path loss. Our proposed solution is based on a novel 3D beam broadening and flattening technique, where the passive array of the AIRS is divided into sub-arrays of appropriate size, and their phase shifts are designed to form a flattened beam pattern with adjustable beamwidth catering to the size of the coverage area. Both uniform linear array (ULA)-based and uniform planar array (UPA)-based AIRSs are considered in our design, which enable two-dimensional (2D) and 3D passive beamforming, respectively. Numerical results show that the proposed designs achieve significant performance gains over the benchmark schemes.

Many operators have been bullish on the role of millimeter-wave (mmWave) communications in fifth-generation (5G) mobile broadband because of its capability of delivering extreme data speeds and capacity. However, mmWave comes with challenges related to significantly high path loss and susceptibility to blockage. Particularly, when mmWave communication is applied to a mobile terminal device, communication can be frequently broken because of rampant hand blockage. Although a number of mobile phone companies have suggested configuring multiple sets of antenna modules at different locations on a mobile phone to circumvent this problem, identifying an optimal antenna module and a beam pair by simultaneously opening multiple sets of antenna modules causes the problem of excessive power consumption and device costs. In this study, a fast antenna and beam switching method termed Fast-ABS is proposed. In this method, only one antenna module is used for the reception to predict the best beam of other antenna modules. As such, unmasked antenna modules and their corresponding beam pairs can be rapidly selected for switching to avoid the problem of poor quality or disconnection of communications caused by hand blockage. Thorough analysis and extensive simulations, which include the derivation of relevant Cramér-Rao lower bounds, show that the performance of Fast-ABS is close to that of an oracle solution that can instantaneously identify the best beam of other antenna modules even in complex multipath scenarios. Furthermore, Fast-ABS is implemented on a software defined radio and integrated into a 5G New Radio physical layer. Over-the-air experiments reveal that Fast-ABS can achieve efficient and seamless connectivity despite hand blockage.

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RISs, which may pave the way for further theoretical studies and practical applications in this field.

In application scenarios such as Internet of Things, a large number of energy receivers (ERs) exist and line-of-sight (LOS) propagation could be common. Considering this, we investigate wireless energy transfer (WET) in extra-large massive MIMO Rician channels. We derive analytical expressions of the received net energy for different schemes, including 1) training-based WET, where the ER sends beacon signal for channel training and the energy transmitter (ET) uses the channel estimate for energy beamforming, 2) LOS beamforming, where the ET transmits to the LOS direction of the ER, and 3) energy harvesting, which allows an ER to harvest the training energy from the other ERs. We derive a path loss threshold for switching between training and LOS beamforming-based WET. We further show that the WET scheme selection of one ER is not affected by the other ERs, and the energy harvested from training is minimal in practice. With these insights, we propose an algorithm for the multi-ER scenario, which minimizes the power consumption by iteratively updating the WET scheme selection and power allocation for all ERs. Simulations show that the proposed algorithm achieves near-optimal performance as compared to exhaustive searching, while with much lower implementation complexity.

Millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate cooperative localization, in which large bandwidths and antenna arrays and increased densities of base stations enhance the delay and angular resolution. This study considers the joint location and velocity estimation of user equipment (UE) and scatterers in a three-dimensional mmWave CRAN architecture. Several existing works have achieved satisfactory results by using neural networks (NNs) for localization. However, the black box NN localization method has limited robustness and accuracy and relies on a prohibitive amount of training data to increase localization accuracy. Thus, we propose a model-based learning network for localization to address these problems. In comparison with the black box NN, we combine NNs with geometric models. Specifically, we first develop an unbiased weighted least squares (WLS) estimator by utilizing hybrid delay and angular measurements, which determine the location and velocity of the UE in only one estimator, and can obtain the location and velocity of scatterers further. The proposed estimator can achieve the Cramér-Rao lower bound under small measurement noise and outperforms other state-of-the-art methods. Second, we establish a NN-assisted localization method called NN-WLS by replacing the linear approximations in the proposed WLS localization model with NNs to learn the higher-order error components, thereby enhancing the performance of the estimator, especially in a large noise environment. The solution possesses the powerful learning ability of the NN and the robustness of the proposed geometric model. Moreover, the ensemble learning is applied to improve the localization accuracy further. Comprehensive simulations show that the proposed NN-WLS is superior to the benchmark methods in terms of localization accuracy, robustness, and required time resources.

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an extremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramér-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory to avoid the cellular BSs' coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.

Deep neural networks (NNs) have exhibited considerable potential for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing NN-based MIMO detectors are difficult to be deployed in practical systems because of their slow convergence speed and low robustness in new environments. To address these issues systematically, we propose a receiver framework that enables efficient online training by leveraging the following simple observation: although NN parameters should adapt to channels, not all of them are channel-sensitive. In particular, we use a deep unfolded NN structure that represents iterative algorithms in signal detection and channel decoding modules as multi layer deep feed forward networks. An expectation propagation (EP) module, called EPNet, is established for signal detection by unfolding the EP algorithm and rendering the damping factors trainable. An unfolded turbo decoding module, called TurboNet, is used for channel decoding. This component decodes the turbo code, where trainable NN units are integrated into the traditional max-log-maximuma posterioridecoding procedure. We demonstrate that TurboNet is robust for channels and requires only one off-line training. Therefore, only a few damping factors in EPNet must be re-optimized online. An online training mechanism based on meta learning is then developed. Here, the optimizer, which is implemented by long short-term memory NNs, is trained to update damping factors efficiently by using a small training set such that they can quickly adapt to new environments. Simulation results indicate that the proposed receiver significantly outperforms traditional receivers and that the online learning mechanism can quickly adapt to new environments. Furthermore, an over-the-air platform is presented to demonstrate the significant robustness of the proposed receiver in practical deployment.

Reconfigurable intelligent surface (RIS) is an emerging technology to enhance wireless communication in terms of energy cost and system performance by equipping a considerable quantity of nearly passive reflecting elements. This study focuses on a downlink RIS-assisted multiple-input multiple-output (MIMO) wireless communication system that comprises three communication links of Rician channel, including base station (BS) to RIS, RIS to user, and BS to user. The objective is to design an optimal transmit covariance matrix at BS and diagonal phase-shifting matrix at RIS to maximize the achievable ergodic rate by exploiting the statistical channel state information at BS. Therefore, a large-system approximation of the achievable ergodic rate is derived using the replica method in large dimension random matrix theory. This large-system approximation enables the identification of asymptotic-optimal transmit covariance and diagonal phase-shifting matrices using an alternating optimization algorithm. Simulation results show that the large-system results are consistent with the achievable ergodic rate calculated by Monte-Carlo averaging. The results verify that the proposed algorithm can significantly enhance the RIS-assisted MIMO system performance.

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

Recently, reconfigurable intelligent surfaces (RISs) have drawn intensive attention to enhance the coverage of millimeter wave (mmWave) communication systems. However, existing works mainly consider the RIS as a whole uniform plane, which may be unrealistic to be installed on the facade of buildings when the RIS is extreme large. To address this problem, in this paper, we propose a sparse array of sub-surface (SAoS) architecture for RIS, which contains several rectangle shaped sub-surfaces termed as RIS tiles that can be sparsely deployed. An approximated ergodic spectral efficiency of the SAoS aided system is derived and the performance impact of the SAoS design is evaluated. Based on the approximated ergodic spectral efficiency, we obtain an optimal reflection coefficient design for each RIS tile. Analytical results show that the received signal-to-noise ratios can grow quadratically and linearly to the number of RIS elements under strong and weak LoS scenarios, respectively. Furthermore, we consider the visible region (VR) phenomenon in the SAoS aided mmWave system and find that the optimal distance between RIS tiles is supposed to yield a total SAoS VR nearly covering the whole blind coverage area. The numerical results verify the tightness of the approximated ergodic spectral efficiency and demonstrate the great system performance.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.

Signal recovery through coarse quantization of a linear transform output has many applications in engineering, such as channel estimation and signal detection in massive MIMO systems. A recently proposed scheme, known as generalized expectation consistent signal recovery (GEC-SR), can achieve Bayesian inference and exhibit better robustness than many existing methods. However, recovering signals with large transform matrices continue to present a computational burden for GEC-SR. In this study, we develop a novel decentralized architecture by leveraging the core framework of GEC-SR called "deGEC-SR." deGEC-SR offers excellent performance as GEC-SR and runs tens of times faster than GEC-SR. We derive the theoretical state evolution of deGEC-SR and demonstrate its accuracy using numerical results.

Millimeter-wave (mmWave) communication has become a promising option for meeting the multi-fold increase in demand for mobile data in the fifth-generation (5G) mobile broadband. However, when mmWave is applied to a mobile terminal device, communication can be frequently broken due to rampant hand blockage. Although this problem can be overcome by configuring multiple sets of subarrays at different locations, developing a fast and efficient operation that can find the best subarray and beam direction with power, complexity, and latency constraints is extremely challenging. In this study, we propose a fast antenna and beam switching method termed `Fast-ABS' that uses only one antenna module for the reception to predict the best beam of other subarrays. Through extensive simulations, we demonstrate that Fast-ABS achieves efficient and seamless connectivity under hand blockage. In addition, we implement Fast-ABS on software radios and integrate it into the 5G New Radio physical layer. Our experiments show that the performance of the proposed beam switching method is close to that of an “Oracle” solution that can instantaneously identify the best beam of other subarrays even in complex non-line-of-sight scenarios.

The spatial reciprocity enables the downlink channel reconstruction in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems by obtaining the frequency-independent parameters in the uplink. However, the algorithms to estimate these parameters are typically complex and time-consuming. In this paper, we regard the channel as an image and utilize you only look once (YOLO), an advanced deep learning-based object detection network, to locate the bright spots in the channel image, then the frequency-independent parameters can be estimated rapidly. Superior to the traditional algorithm that iteratively extracts the paths, YOLO can detect all the path simultaneously. Experimental results show that YOLO can greatly deplete the running time to obtain the frequency-independent parameters and reconstruct the FDD massive MIMO downlink channel with satisfactory accuracy.

Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input-multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time.

Millimeter wave (mmWave) band, or high frequencies such as THz, has large undeveloped band of spectrum. However, wireless channels over the mmWave band usually have one or two paths only due to the severe attenuation. The channel property restricts its development in the multiple-input multiple-output (MIMO) system, which can improve throughput by increasing the spectral efficiency. Recent development in reconfigurable intelligent surface (RIS) provides new opportunities to mmWave communications. In this study, we propose a mmWave system, which used low-precision analog-to-digital converters (ADCs), with the aid of several RIS arrays. Moreover, each RIS array has many reflectors with discrete phase shift. By employing the linear spatial processing, these arrays form a synthetic channel with increased spatial diversity and power gain, which can support MIMO transmission. We develop a MIMO detector according to the characteristics of the synthetic channel. RIS arrays can provide spatial diversity to support MIMO transmission, however, different number, antenna configuration, and deployment of RIS arrays affect the bit error rate (BER) performance. We present state evolution (SE) equations to evaluate the BER of the proposed MIMO detector in the different cases. The BER performance of indoor system is studied extensively through leveraging by the SE equations. We reveal numerous insights about the RIS effects and discuss the appropriate system settings. In addition, our results demonstrate that the low-cost hardware, such as the 3-bit ADCs of the receiver side and the 2-bit uniform discrete phase shift of the RIS arrays, only moderately degenerate the system performance.

Phase retrieval algorithms are now an important component of many modern computational imaging systems. A recently proposed scheme called generalized expectation consistent signal recovery (GEC-SR) shows better accuracy, speed, and robustness than numerous existing methods. Decentralized GEC-SR (deGEC-SR) addresses the scalability issue in high-resolution images. However, the convergence speed and stability of these algorithms heavily rely on the settings of several handcrafted tuning factors with inefficient turning process. In this work, we propose deGEC-SR-Net by unfolding the iterative deGEC-SR algorithm into a learning network architecture with trainable parameters. The parameters of deGEC-SR-Net are determined by data-driven training. Numerical results show that deGEC-SR-Net provides substantially faster convergence than deGEC-SR and exhibits superior robustness to noise and prior mis-specifications.

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximuma posteriori(MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.

We consider the channel estimation problem in millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with hybrid analog-digital architectures. Leveraging the spatial- and frequency-wideband (dual-wideband) effects in massive MIMO scenarios, we derive a spatial-frequency channel model with dual-wideband effects that incorporates the multipath parameters, i.e., time delay, complex gain, angle of departure/arrival. We adopt a successive beam training scheme and formulate the training OFDM signal as a third-order low-rank tensor fitting a canonical polyadic (CP) model with factor matrices containing the channel parameters. Exploiting the Vandermonde nature of factor matrices, we propose a structured CP decomposition-based channel estimation strategy aided by the spatial smoothing method, where two dedicated algorithms with particular tensor modeling and parameter recovery operations are developed. The proposed scheme leverages standard linear algebra, and, hence, avoids the random initialization problem and iterative procedure. An analysis of the uniqueness condition of CP decomposition is also pursued. Simulation results indicate that the proposed strategy achieves enhanced estimation performance, which outperforms the traditional approaches in terms of accuracy, robustness and complexity.

Although uplink (UL) downlink (DL) decoupling (DUDe) brings significant gains in the UL throughput of decoupled user equipments (DeUEs) in heterogeneous networks, channel estimation and DL performance of DeUEs are worse than the coupled UEs due to the DUDe property and the cell edge effect. To address these fundamental problems, we propose a transmission scheme with data-aided (DA) minimum mean square error (MMSE) channel estimator and zero-forcing (ZF) interference nulling (IN) precoding for a two-tier multi-cell HetNet with DUDe. We first present a method to estimate the bit error rate (BER) of UL data, then, derive the form of DA MMSE estimator, which utilizes decoded UL data, estimated BER and known UL training sequences to jointly estimate the DL channels of DeUEs. ZF IN precoding uses the estimated channels of DeUEs to cancel the nearest DL interference without any cooperation and message transmission. Also, we derive a tight approximation to the achievable DL rate of DeUEs and analyze the benefits of the DA estimator and ZF IN precoding. Our simulations show that DA MMSE estimator outperforms the conventional MMSE counterpart, while the proposed scheme improves the DL performance of both DeUEs and macro UEs, though the rate gain may be degraded by pilot contamination and inter-cell interference.

To boost energy saving for the general delay-tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware N-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes.

Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number$N$of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a$K$-client random scheduling strategy, where$K$($1\leq K< N$) clients are randomly selected from the$N$overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the$K$-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal$K$that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions.

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational complexity and excess of baseband data to be transferred and processed, prohibit the use of centralized processing. A promising solution is to distribute baseband data from disjoint subsets of antennas into parallel processing procedures coordinated by a central processing unit. This solution is called subarray-based architecture. In this work, we extend the application of expectation propagation (EP) principle, which effectively balances performance and practical feasibility in conventional centralized MIMO detector design, to fit the subarray-based architecture. Analytical results confirm the convergence of the proposed iterative procedure and that the proposed detector asymptotically approximates Bayesian optimal performance under certain conditions. The proposed subarray-based EP detector is reduced to centralized EP detector when only one subarray exists. In addition, we propose additional strategies for further reducing the complexity and overhead of the information exchange between parallel subarrays and the central processing unit to facilitate the practical implementation of the proposed detector. Simulation results demonstrate that the proposed detector achieves numerical stability within few iterations and outperforms its counterparts.

Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

Employing low-resolution analog-to-digital converters (ADCs) coupled with large antenna arrays at the receivers has drawn considerable interests in the millimeter wave (mm-wave) system. Since mm-wave channels are sparse in angular dimensions, exploiting the structure could reduce the number of measurements while achieving acceptable performance at the same time. Motivated by the variational Bayesian line spectral estimation (VALSE) algorithm which treats the angles as random parameters, in contrast to previous works which confine the estimate to the set of grid angle points and induce grid mismatch, this paper proposes the grid-less quantized variational Bayesian channel estimation (GL-QVBCE) algorithm for antenna array systems with low resolution ADCs. Numerical results show the near optimal performance of GL-QVBCE by comparing with the Cramèr Rao bound (CRB) and the state-of-art methods.

Recent years have witnessed the rapid proliferation of low- power backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such low-power backscatter tags is crucial for IoT-based smart services. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, increasing the deployment cost. To empower universal localization service, this paper presents Rover, an indoor localization system that simultaneously localizes multiple backscatter tags with zero start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing WiFi-based positioning measurements with inertial measurements to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues such as the interference among multiple tags and the real- time processing for solving the SLAM problem. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAM-P, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.

5G millimeter-wave (mmWave) cloud radio access networks (CRANs) provide new opportunities for accurate multilateration: large bandwidth, large antenna arrays, and increased densities of base stations allow for unparalleled delay and angular resolution. However, combining localization into communications and designing joint position and velocity estimation algorithms are challenging problems. This paper considers the joint estimation in three-dimensional (3-D) lens antenna array based mmWave CRAN architecture. We embed multilateration into communications and explain its benefits for the initial access and beam training stages. We propose a closed-form solution for the joint estimation problem by forming the pseudo-linear matrix representation and designing the weighted least squares estimator with hybrid measurements. The proposed method is proven asymptotically unbiased and confirmed by simulations to achieve the Cramer- Rao lower bound and attain the desired sub-decimeter level accuracy.

In this paper, we investigate the optimal caching policy in a K-tier millimeter wave (mmWave) cache- enabled heterogeneous network. In order to mitigate interferences, we incorporate base station (BS) idling into our analysis. Under the random caching framework, we derive the association probability for each tier as well as the successful transmission probability (STP) by utilizing stochastic geometry and taking the blockage effect into account. In addition, we adopt the gradient projection method to obtain the locally optimal caching probabilities and propose a two-stage scheme to obtain the globally optimal caching probabilities under the special case where the LOS ranges for K tiers are sufficiently large. Numerical results demonstrate the superiority of the proposed method over the conventional caching strategies such as Most Popular Content (MPC) and Uniform Caching (UC) schemes.

Compressive scanning (CS) has exhibited its potential in improving the path tracking efficiency of millimeter wave (mmWave) systems. However, its practical performance is significantly degenerated by hardware imperfections, such as carrier frequency offset (CFO). Conventional CFO estimation methods that compare the phases of two measurements cannot be applied in CS straightforwardly because the two successive beacons are different. To overcome these problems, we propose a novel CFO-robust compressive path-tracking algorithm by introducing a two-stage CFO estimation procedure before performing coherent CS detection. Unlike conventional CFO estimates, the CFO estimate in the proposed algorithm can be obtained from the signal strength value of the received signal at the cost of a small amount of additional computation complexity. Numerical results demonstrate the superiority of the proposed algorithm in both single-path and multipath scenarios.

To enhance downlink (DL) performance of decoupled user equipments (DeUEs) in decoupled heterogeneous networks, a transmission scheme with data-aided (DA) channel estimation and zero-forcing (ZF) interference-nulling (IN) precoding is proposed. In the scheme, DL base station (BS) uses decoded uplink (UL) data and estimated UL bit error ratio (BER) combined with known training sequences to perform DA channel estimation and refined estimated channels of DeUEs are then used in DL precoding for better DL performance. Also, ZF IN precoding is adopted at UL BSs of DeUEs, who pose the strongest interference on their serving DeUEs in DL, by leveraging estimated channels of DeUes in UL without any message transferring. The closed-form achievable DL rate of DeUEs is derived and analyzed, which shows that DL rate can be improved remarkably by DA method and ZF IN precoding although there exists rate upper bounds for co-channel interference from other BSs.

This paper proposes a new hybrid architecture by using multiple unmanned aerial vehicles enabled aerial base stations (ABSs) to offload data traffic for a single overloaded ground base station (GBS). We consider two practical spectrum sharing strategies, i.e., orthogonal spectrum sharing and nonorthogonal spectrum reusing between GBS and ABSs. For the non-orthogonal spectrum reusing case, we aim to maximize the minimum throughput for cell-edge ground users (GUs) by jointly optimizing the coverage radius of ABSs, and the number of ABSs. For the orthogonal spectrum sharing case, we have also optimized an additional variable, i.e., bandwidth allocation ratio. Numerical results indicate that the orthogonal spectrum sharing strategy outperforms the non-orthogonal spectrum reusing strategy and the conventional GBS-only case, thus provides an attractive solution to offload data traffic for a temporary cellular hotspot.