Extremely large-scale multiple-input multiple-output (XL-MIMO) is crucial for next-generation communication systems. In practice, the deployment of non-square uniform planar arrays (UPAs) fundamentally alters wavefront characteristics and induces anisotropic beamfocusing capability along different axes due to the aperture disparity originating from the non-square array geometry. To fully uncover the performance impact of such non-square geometry and thus unleash the potential of the non-square UPAs, we investigate the anisotropic near-field characteristics, fundamental limits, and channel estimation for non-square UPA-enabled XL-MIMO systems. First, we derive the effective beamfocusing distances for the long and short axes of the array. Interestingly, the radiation space of a non-square UPA can be partitioned into three regions, i.e., the fully near-field, the anisotropic near-field, and the far-field regions, and the anisotropic region asymptotically dominates the overall near-field space as the array aspect ratio increases. Then, the asymptotic effective degree of freedom for non-square UPA-enabled XL-MIMO systems is provided, which reveals that distance-domain multiplexing is governed by the long-axis aperture in the large array aspect ratio regime. Furthermore, the closed-form Cramer-Rao bound for distance estimation and the three-dimensional (3D) position error bound (PEB) are derived to reveal the geometry-induced performance trade-offs among distance, azimuth, and elevation estimation, based on which the optimal array aspect ratio that minimizes the 3D PEB is determined. Finally, by exploiting the anisotropic wavefront properties, we design a 3D anisotropic near-field codebook to facilitate low-complexity channel estimation for non-square UPAs. Numerical results validate that the proposed codebook achieves comparable accuracy to the 3D polar-domain codebook at reduced complexity.

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.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems, which eliminate inter-cell interference and enhance spectral efficiency, are considered as a key enabling technology for 6th generation mobile communications (6G). Meanwhile, reconfigurable intelligent surface (RIS), featuring low power consumption, programmability, and flexible deployment, offers significant potential for improving channel quality and extending coverage. In this paper, a RIS-assisted CF-mMIMO system was investigated, and a joint optimization problem involving power control, precoding, and RIS phase adjustment was formulated to maximize energy efficiency (EE). To tackle this non-convex multi-variable problem, an alternating optimization framework was proposed. Specifically, the precoding and RIS phase subproblems were decoupled and solved using the Lagrangian dual transform and fractional programming, while the power control subproblem was addressed via Newton method. Simulation results demonstrate that the proposed scheme achieves faster convergence and significantly outperforms the conventional uniform power allocation methods. Moreover, it simultaneously improves both energy efficiency and communication rate, effectively enabling the joint optimization of spectrum efficiency (SE) and EE.

Wideband channel estimation (CE) in high-mobility scenarios remains challenging because channel responses vary rapidly, while practical systems can allocate only sparse pilots to accommodate dense users. Fortunately, many high-mobility environments, such as high-speed railways, exhibit scheduled trajectories, predictable velocities, and a limited number of dominant propagation paths. These properties induce a delay–beam power spectrum that is more stable than the instantaneous complex channel frequency response (CFR), less sensitive to the random phase coherence, and rich in geometric information. To exploit such environmental properties, we propose GeoGS-CE, a two-stage channel estimation framework for sparse-pilot high-mobility scenarios. In the offline stage, GeoGS-CE jointly models: 1) a scene-level 3D Gaussian representation that captures the non-line-of-sight (NLoS) geometric scattering support, and 2) a leakage-aware differentiable wireless rendering process that maps the NLoS Gaussians, together with an explicit virtual line-of-sight (LoS) component, to the measured delay–beam power spectrum, while accounting for practical OFDM delay and array leakage effects. In the online stage, the delay–beam power spectrum is predicted for each user location and used as a strong covariance prior, enabling accurate full-band and full-array CFR reconstruction and tracking through a linear MMSE estimator. Simulations based on channels generated from a segment of the Guangshen high-speed railway show that the proposed geometric prior substantially improves CFR reconstruction over pilot-only and non-geometric baselines.

Low-earth orbit (LEO) satellites have garnered significant attention for their potential to deliver global connectivity with low latency and high throughput. In the paper, we investigate cell-free massive multi-input multi-output (MIMO)-enabled LEO satellite communications. Unlike previous work which selects satellites from the entire satellite constellation, this work restricts selection to a subset of satellite access points (SAPs) that cover the user, referred to as the SAP-user association scheme. We consider a comprehensive channel model accounting for the impact of Doppler effects and time delay, and derive a closed-form expression of the system energy efficiency (EE). Based on it, we jointly optimize SAP-user association and downlink power allocation to maximize EE, subject to SAP coverage range constraints and total power limitations. Simulation results validate the accuracy of our theoretical derivations and highlight the superiority of the proposed joint optimization algorithm. Compared with previous works that optimize only standalone parameters, our method achieves significantly higher EE, with the performance advantage becoming increasingly in dense-user scenarios.

This paper investigates a new integrated sensing and communication (ISAC) scheme for underwater acoustic (UWA) networks based on deep reinforcement learning, referred to as Deep UWA-ISAC (DeepUSC). Specifically, we consider a UWA-ISAC system, where an autonomous underwater vehicle (AUV) transmits the collected environmental data to the buoy, while sensing the sea area to monitor the unauthorized mobile target. The expected communication rate over a given navigation period is maximized by jointly optimizing the AUV's beamforming and trajectory, subject to the constraints on the average signal-to-noise ratio requirement for target sensing as well as the navigation mission, collision avoidance, and maximum transmit power limit of the AUV. Three key challenges for DeepUSC are: (i) long propagation delays in the UWA-ISAC system may cause interference from the previous echo to the current ISAC signal; (ii) the mobility pattern of the target is unknown in advance; and (iii) the AUV navigation-oriented ISAC problem is a long-term optimization problem as the navigation mission typically lasts for a long period. To circumvent the above challenges, DeepUSC is developed based on a specific partially observable Markov decision process model termed episode task, where each navigation period is considered as an episode and the navigation mission corresponds to the episode task. Through judicious design of a reward function and action selection policy, DeepUSC can satisfy various preset constraints without requiring prior knowledge of the target's mobility. Besides, to enable efficient learning in episode tasks, we propose an episodic experience replay mechanism that dynamically prioritizes high-value recent experiences and utilizes all experiences generated within each episode to jointly train the neural network. Simulation results demonstrate that compared with benchmarks, DeepUSC yields a higher communication rate while satisfying all constraints, converges faster, and is more robust against different simulation setups.

Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying LAMs to physical layer communications, addressing obstacles of conventional AI-based approaches. LAM-based solutions are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. The motivations and key frameworks of these approaches are comprehensively examined through multiple use cases. Both strategies significantly improve performance and adaptability across diverse wireless scenarios. Future research directions, including efficient architectures, interpretability, standardized datasets, and collaboration between large and small models, are proposed to advance LAM-based physical layer solutions for next-generation communication systems.

This paper presents a reconfigurable intelligent surface (RIS)-enhanced backscatter communication system. In this system, the primary receiver (PRx) employs joint decoding to receive information from both the primary transmitter (PTx) and the RIS-backscatter device (RIS-BDx), akin to symbiotic radio (SR) systems. Concurrently, the backscatter receiver (BRx) utilizes an energy detector for demodulating information from the RIS-BDx, aligning with the methodology of ambient backscatter communication (AmBC) systems. We initially address the joint design of the PTx's transmit beamforming and the RIS's reflection coefficients (TBF-RC) within the system, with the objective of minimizing transmit power while adhering to transmission performance constraints. The challenge of this problem lies in the multitude of constant modulus (CM) constraints and fourth-order constraints. By exploiting the problem's structure, we break it down into two sub-problems featuring rate-balanced constraints and propose an alternating optimization (AO) algorithm with linear complexity. Furthermore, we extend the algorithm to tackle the TBF-RC design problem incorporating secrecy rate constraints, which safeguard against the BRx demodulating the primary information. This secure transmission issue underscores a key distinction between the proposed hybrid system and traditional SR systems, where the BRx is typically required to demodulate the primary information. Finally, simulation results are presented to demonstrate the efficacy, security, and computational efficiency of the proposed system and algorithm.

Joint power and admission control (JPAC) is crucial for interference management in wireless networks, but its mixed-integer nature renders the problem NP-hard. This letter pioneers JPAC optimization for fluid antenna system (FAS)-aided interference channels, where users leverage dynamic port selection to enhance spatial diversity. We formulate the FAS-JPAC problem for a K-link single-input single-output (SISO) interference channel as a unified sparse & ell;(0)-minimization problem. The introduction of port selection variables induces novel integer constraints and bilinear SINR constraints, challenges absent in prior JPAC formulations. To address these challenges, we propose a generalized & ell;(q)-minimization deflation (GLQMD) framework. Our solution features: 1) An alternating direction method of multipliers (ADMM)-based algorithm with semi-closed-form updates for efficient resolution of the nonconvex sparse & ell;(q)-minimization subproblem; and 2) Port-aware pre/ postprocessing that accelerates deflation and narrow the & ell;(q)-& ell;(0) approximation gap, respectively. Simulations show that FAS-JPAC yields substantial gains in both admitted links and power efficiency over conventional JPAC baselines while maintaining runtimes on the same order as LQMD

Integrated sensing and communications (ISAC) is one of the key technologies for the sixth-generation wireless communications, in which the extremely large-scale multiple-input multiple-output (XL-MIMO) with higher and wider frequency bands will be incorporated. In this paper, a near field wideband terahertz XL-MIMO ISAC system is investigated, where the dual-functional base station communicates with a user and detects a non-cooperative target simultaneously. Our objective is to maximize the achievable rate within the constraints of sensing coverage and resolution in both angular and distance domains by designing the delays and phase shifts for the three-dimensional (3D) delay-phase beamforming framework. Firstly, the phase shifts are derived based on the controllable 3D beam squint method. Then, the mathematical relationship between the delays introduced by the true-time-delays (TTDs) and the constraints of sensing coverage and resolution in both angular and distance domains is derived based on the analysis for controllable 3D beam squint effect, based on which the optimization problem is reconstructed. Finally, the optimal delays introduced by the TTDs are derived, facilitating the trade-off between the achievable rate and sensing coverage with adequate sensing resolution. The proposed method can degenerate to the far field case. Numerical simulations are conducted to validate the effectiveness of the proposed method and illustrate the performance trade-off between the achievable rate and sensing coverage with sufficient sensing resolution.

Cell-free massive multiple-input multiple-output (MIMO) systems, capable of eliminating inter-cell interference and enhancing spectral efficiency, are regarded as a promising key technology in 6th Generation Mobile Communications (6G). Meanwhile, reconfigurable intelligent surfaces (RIS), characterized by low power consumption, programmability, and easy deployment, demonstrate broad application prospects in improving channel quality and extending coverage range. This paper investigates a RIS-assisted cell-free massive MIMO system and formulates a joint optimization problem encompassing power control, precoding, and RIS phase adjustment to maximize system energy efficiency (EE). To address this non-convex tri-variable optimization challenge, an alternating optimization framework is proposed: the Lagrangian dual transform and fractional programming methods are employed to decouple and solve the precoding and RIS phase subproblems, while the power control subproblem is optimized using Newton's method. Simulation results demonstrate that the proposed RIS-assisted power optimization scheme exhibits superior convergence characteristics compared to conventional uniform power allocation approaches, achieving significant performance improvements. Notably, the optimization algorithm simultaneously enhances both energy efficiency and communication rate, thereby effectively realizing the co-optimization of spectral efficiency and energy efficiency.

The active reconfigurable intelligent surface (RIS) is able to actively reflect signals with amplification and overcome the multiplicative fading effect. Therefore, introducing the active RIS into the unmanned aerial vehicle (UAV) communication systems can further enhance the communication performance. In this paper, we investigate an active RIS-assisted UAV communication system in which the UAV employed a single directional antenna is functioned as an airborne BS. Our objective is to maximize the achievable rate with the power constraint at the active RIS by jointly designing the three-dimensional (3D) flight position for the UAV and the phase shift matrix at the active RIS. Firstly, the phase shift matrix at the active RIS is derived by using the phase alignment approach, and the 3D coordinates for the UAV are derived based on the fixed point iteration method. Then, an alternating optimization algorithm is introduced to obtain the aforementioned variables. Finally, the numerical results are conducted to verify the effectiveness of the proposed method.

This paper investigates the impact of imperfect visibility region (VR) estimation on the energy efficiency (EE) of active extra large-reconfigurable intelligent surface (XL-RIS)-aided spatially non-stationary Internet of Things (IoT) systems. Unlike existing VR-aware XL-RIS studies that either assume perfect VR knowledge or evaluate system performance under a given detected VR pattern, we consider a practical VR acquisition process with detection errors and quantitatively analyze the impact of VR errors on the ergodic performance. Specifically, we first propose an uplink VR estimation scheme in which only elements within the true VR can receive user signals, and derive the false detection and missed detection probabilities by analyzing the accumulated pilot’s energy distribution. In the downlink, only the estimated VR elements are activated for reflection, and the base station applies conjugate beamforming based on the estimated cascaded channel information. A deterministic approximation for the ergodic average EE is then obtained, enabling an alternate optimization of the VR detection threshold, pilot length, transmit power, the active RIS phase-shift and amplification factor. Moreover, an efficient EE approximation by taking a few dominant VR detection outcomes is further developed, reducing the computational complexity significantly. Simulation results show that accurate VR estimation is crucial for realizing the performance gains of XL-RIS, and the proposed scheme achieves EE close to that with perfect VR knowledge.

6G networks will introduce unprecedented complexity, which calls for a paradigm shift in network optimization and management. Artificial intelligence (AI)-based solutions, especially those enabled by the recently developed foundation models, have been recognized as promising candidates. Foundation models are large-scale AI models with general-purpose feature extraction capabilities, and once trained on massive amounts of data, they can be adapted to solve a wide range of downstream tasks, either in a zero-shot manner or with few-shot fine-tuning. This article provides a comprehensive overview of how foundation models are reshaping physical-layer processing and wireless resource management across three progressive paradigms. First, we examine the adaptation of off-the-shelf pre-trained foundation models to various wireless tasks. Second, we explore wireless-native foundation models, built from scratch on wireless data to bridge cross-domain modality gaps and capture universal wireless-domain physical characteristics. Third, we highlight agentic foundation models, which elevate static data processing into autonomous, reasoning-driven network orchestration. Furthermore, we discuss the impact of applying foundation models to emerging 6G frontiers, including integrated sensing and communications (ISAC), new multiple-input multiple-output (MIMO) architectures, semantic communications, and system-level network autonomy. Finally, we identify critical open challenges and opportunities, charting a promising path toward fully intelligent and adaptive wireless networks.

This paper investigates the unmanned aerial vehicle (UAV)-borne simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted non-orthogonal multiple access system under the influence of UAV jitter. Since jitter leads to beam misalignment and hence significant user ergodic rate degradation, this paper formulates the weighted ergodic sum-rate maximization optimization problem. To cope with the challenges posed by coupled variables, non-convex constraints and jitter characteristics, this paper proposes a scheme that combines jitter-free optimization with jitter adaptive optimization. The first stage under jitter-free assumptions optimizes UAV hovering positions, base station beamforming, and power allocation strategies via linear search and closed-form solutions, reducing computational complexity while ensuring baseline performance. The second stage adaptively adjusts STAR-RIS phases based on real-time jitter states, employing wide beams to compensate for beam misalignment. Simulation results demonstrate that the proposed scheme dynamically adjusts beamwidth based on jitter intensity, effectively mitigating rate degradation from jitter and enhancing system robustness.

Integrated sensing and communication (ISAC) achieves promising applications in target localization and environment sensing by sharing spectrum, hardware and software resources. Cell-free massive multiple-input multiple-output (CF-mMIMO) can suppress inter-cell interference and improve spectral efficiency. The CF-mMIMO ISAC system is expected to support both high-rate communication and high-precision sensing. Most existing studies model sensing targets as a single reflection point ones in CF-mMIMO ISAC systems. Massive distributed access point (AP) deployed in such systems shorten the distance between AP and targets, resulting in limited sensing information and failure to characterize target spatial distribution. This paper proposes a multiple reflection points target model to enrich sensing information and improve sum-rate, and focuses on precoding design.. For maximizing the system sum rate under sensing performance constraints, a two-stage framework is proposed: transform the non-convex problem via weighted minimum mean square error, derive a closed-form precoding solution using semi-definite relaxation, and solve optimal precoding via alternating optimization. Simulation results demonstrate that under sensing constraints, more reflection points boost the system sum-rate by around 30%. Moreover, the multiple reflection points model achieves greater communication gains with more users or AP, which aligns with future communication scenarios trends of massive users and ultra-dense CF-mMIMO, further underscoring its research significance.

With the rapid development of the low-altitude economy, the communication support role of cell-free massive multiple-input multiple-output (MIMO) systems has become increasingly important. To address the limitations of conventional clustering methods based on instantaneous locations, which were subject to delayed updates, and fixed power control schemes, which were unable to adapt to dynamic position variations, the joint optimization problem of unmanned aerial vehicle (UAV) –access point association and uplink power control was formulated as a Markov decision process. A two-stage alternating optimization algorithm based on deep deterministic policy gradient was then proposed, in which the large-scale fading sequence over the entire UAV flight cycle was taken as global prior input to enable joint planning-oriented learning of clustering and power control sequences throughout the full flight period. Simulation results showed that the proposed algorithm achieved higher and more stable system performance than conventional schemes and exhibited good adaptability under various UAV spatial topology distributions. These results demonstrate that the proposed method provides an effective solution for joint UAV–access point association and uplink power control in dynamic low-altitude communication scenarios.

In low-altitude economy (LAE) networks, integrated sensing and communication (ISAC) exhibits transformative potential yet is constrained by fixed-antenna architectures amid dynamic environments. Fluid antenna (FA) technology has accordingly been proposed as a mitigating solution. Nevertheless, the suboptimal synergy between FA and LAE platforms, alongside self-interference during signal transmission and echo reception, remain critical hurdles for ISAC's practical implementation in LAE contexts. To address these challenges, we propose a novel FA-aided ISAC system for LAE networks. Detailed models for the communication and sensing processes are introduced to support this system. Then, we formulate beamforming and antenna positioning strategies as a non-cooperative game, aiming to maximize the total communication and sensing rate for each ISAC BS. To tackle this complex problem, we decompose the game-theoretic problem into multiple sub-game optimization problems and analyze each separately. A mixed alternating iterative algorithm based on the sine cosine and particle swarm optimization (SCPSO) is developed to find the optimal solutions. Extensive simulations show the rapid convergence of the proposed mixed SCPSO-based algorithm. In addition, the FA-aided ISAC scheme for LAE outperforms conventional antenna positioning schemes. The results under different parameter settings further demonstrate that the FA-aided ISAC system for LAE significantly improves both communication and sensing performance through dynamic antenna reconfiguration.

This letter studies symbol-level precoding (SLP) for multi-cell multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) under a coordinated beamforming (CBF) architecture. The goal is to maximize the target illumination power for sensing while meeting SLP constraints for communications. Unlike most multi-cell ISAC works in single-carrier settings, OFDM sensing couples the precoders both across base stations and across subcarriers, which complicates the design. To enable network-friendly computation, we develop a distributed alternating direction method of multipliers (ADMM) algorithm that uses only local channel state information and user data at each base station and exchanges a small set of consensus variables. We further propose a payload-reduced variant that aggregates inter-cell terms over subcarriers into a frequency-agnostic consensus, decoupling the exchanged payload from the number of subcarriers. A centralized successive upper-bound minimization method is provided as a benchmark. Simulations show that the distributed method converges quickly, approaches the centralized benchmark, and exhibits a predictable tradeoff in the payload-reduced setting.

The angular-domain channel knowledge map (A-CKM) stores channel path angles distributed in a target area, which is particularly useful for multiple-input multiple-output (MIMO) transmission. However, the spatial non-stationary nature of angular features poses a challenge to A-CKM construction, especially in complex propagation environments. For this problem, we propose a framework for A-CKM construction, which consists of key steps including subregion partition, spatial interpolation, and sampling optimization. Specifically, the target area is firstly partitioned into multiple disjoint subregions based on cluster-level angular features, ensuring local environmental similarity in every subregion. Subsequently, a subregion-based inverse distance weighting (IDW) interpolation method is proposed to construct the A-CKM from sparsely sampled measurements. In addition, a sequential sampling optimization scheme is proposed to improve the sampling efficiency, while enabling continuous map refinement over time. Simulations validate the effectiveness of our proposed A-CKM construction scheme over conventional approaches. Moreover, through a case study of MIMO beamforming, we further demonstrate that the proposed CKM construction method effectively enhances communication performance.