Movable antenna (MA) systems have emerged as a promising technology for future wireless communication systems. The movement of antennas gives rise to mutual coupling (MC) effects, which have been previously ignored and can be exploited to enhance the capacity of multiple-input multiple-output (MIMO) systems. To this end, we first model an MA-enabled point-to-point MIMO communication system with MC effects using a circuit-theoretic framework. The capacity maximization problem is then formulated as a non-concave optimization problem and solved via a block coordinate ascent (BCA)-based algorithm. The subproblem of optimizing MA positions is challenging due to the presence of the analytically intractable MC matrices. To overcome this difficulty, we develop a trust region method (TRM)-based algorithm to optimize MA positions, wherein Sylvester equations are employed to compute the derivatives of the inverse square roots of the MC matrices. Simulation results show significant capacity gains from leveraging MC effects, primarily due to customizable MC matrices and superdirectivity.

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.

Fluid antenna systems (FAS) offer enhanced spatial diversity for next-generation wireless systems. However, acquiring accurate channel state information (CSI) remains challenging due to the large number of reconfigurable ports and the limited availability of radio-frequency (RF) chains— particularly in high-dimensional FAS scenarios. To address this challenge, we propose an efficient posterior sampling-based channel estimator that leverages a diffusion model (DM) with a simplified U-Net architecture to capture the spatial correlation structure of two-dimensional FAS channels. The DM is initially trained offline in an unsupervised way and then applied online as a learned implicit prior to reconstruct CSI from partial observations via posterior sampling through a denoising diffusion restoration model (DDRM). To accelerate the online inference, we introduce a skipped sampling strategy that updates only a subset of latent variables during the sampling process, thereby reducing the computational cost with minimal accuracy degradation. Simulation results demonstrate that the proposed approach achieves significantly higher estimation accuracy and over 20× speedup compared to state-of-the-art compressed sensing-based methods, highlighting its potential for practical deployment in high-dimensional FAS.

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

This paper studies the joint device selection and power control for wireless federated learning (FL), considering both the analog downlink and over-the-air computation (AirComp)-based uplink communications between the parameter server (PS) and the terminal devices. First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and derive the upper bound on the expected optimality gap between the expected and optimal global loss values with respect to (w.r.t.) the selected devices and downlink and uplink transmit power values. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problem, which is solved by using the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the idealFedAvgscheme with error-free model exchange and full device participation.

This article studies the energy efficiency (EE) of the two-way wireless communication system operating in the full-duplex (FD) and half-duplex (HD) modes, respectively. Particularly, both the residual self-interference (RSI) and power consumption for the self-interference cancellation (SIC) in the FD mode are modeled as linear functions over the transmit power. The EE maximization problems for FD, time-division duplex (TDD), and frequency-division duplex (FDD) modes with the sum and individual spectral efficiency (SE) constraints are studied, respectively. With the sum SE constraint, closed-form expressions for the maximum EE of these three modes are derived, and the maximum EE among them are compared. Based on the comparison results, a duplex selection scheme to achieve the highest EE among the three duplex modes is proposed. With the individual SE constraint, the optimal and suboptimal resource allocation are obtained by utilizing fractional programming for the three duplex modes. Somehow surprisingly, numerical results reveal that the FD mode achieves the best EE performance when the target sum SE or the distance between the two transceivers is relatively large.