Huahua Xiao

In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

With the development of the Internet of Things (IoT) and the popularity of wireless fidelity (WiFi), WiFi-based indoor fingerprint localization has raised increasing attention. However, in dynamic multipath environments, traditional regression-based deep learning (DL) localization models not only require extensive training data but also struggle to cope with signal distortions induced by obstacles and human motion. To address these limitations, we propose BoundLocNet, an object detection–driven framework for device localization using channel state information (CSI) fingerprints. By redefining spatial coordinates as bounding boxes and employing an improved training scheme, the potential correlation between CSI fingerprints and the physical region of a device is captured. Then, the region proposal network (RPN) iteratively refines the bounding box predictions to mitigate environmental interference. Experimental results show that BoundLocNet improves the mean distance error (MDE) by 31% compared to baselines, with enhanced robustness to environmental changes.

Electromagnetic information theory (EIT) has emerged as a pivotal research area in 6G communications, due to its potential in revealing the performance limits of wireless communication systems. However, the high-dimensional characteristic of continuous electromagnetic (EM) channels is always a significant challenge in effectively characterizing multiple-input multiple-output (MIMO) systems in complex space. To address this issue, this paper establishes a stochastic multipath channel model grounded in EIT, where the continuous EM channel is approximated by a set of spatially discrete sampling points. Furthermore, inspired by the recently developed concept of movable antennas, a tabu search algorithm is proposed to optimize the sampling point selection. This method effectively leverages the spatial degrees of freedom offered by a limited number of antennas, thereby maximizing the utilization of the available EM space.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former's estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.