This paper proposes joint sensing and beamforming solutions tailored for extremely large-scale MIMO (XL-MIMO) near-field systems under both static and dynamic scenarios. For static scenarios, we develop a novel Multi-Layer Reconstruction (MLR) mechanism to address the challenges of large-scale near-field dictionary matrix and coarse range grid spacing, and further propose a sparse sensing algorithm, MLR mechanism based Linear Approximation Variational Bayesian Inference (MLR-LA-VBI), to achieve precise user/target position and radar cross section (RCS) sensing with low pilot overhead. Building upon these sensing results, a beamforming scheme is proposed to optimize radiation patterns. For dynamic scenarios, we exploit near-field Doppler-frequency characteristics to propose the modified MLR-LA-VBI (MMLR-LA-VBI) algorithm for sensing and a predictive beamforming framework, where the former serves as the core module of the latter. Our sensing approach enables full motion status sensing of users/targets from a single echo without requiring prior information of the target motion model. By eliminating echo accumulation and leveraging correlations across consecutive coherent processing intervals (CPIs), it achieves high performance with low computational complexity. Moreover, the proposed predictive beamforming framework naturally inherits the aforementioned advantages of MMLR-LA-VBI, and leverages the sensed full motion status to achieve an efficient and seamless beam tracking scheme with Doppler frequency compensation. In addition, theoretical analysis is conducted to characterize the algorithmic complexity, highlighting the advantages of proposed algorithms in terms of efficiency. Finally, simulations and analyses validate the effectiveness of the proposed algorithms in both static and dynamic scenarios.

The emergence of extremely large-scale MIMO (XLMIMO) has made target sensing in near-field environments crucial. However, the vast number of antennas and the big size of near-field dictionary matrix (DM) result in substantial pilot overhead for beam training algorithms and significant computational complexity for subspace algorithms. Moreover, there is few of work capable of accurately obtaining information beyond target location, such as radar cross-section (RCS). To this end, we propose a high-precision, low-pilot-overhead off-grid compressive sensing (CS) algorithm capable of jointly estimating target's location and RCS-the Multi-Layer Reconstruction Linear Approximation Variational Bayesian Inference (MLR-LA-VBI) algorithm. Specifically, the entire algorithm is divided into two phases. In the first phase, we propose the Multi-Layer Reconstruction (MLR) mechanism to reconstruct a surrogate DM. In the second phase, based on the surrogate DM, thus proposing the MLR-LA-VBI algorithm for joint estimation of target location and RCS. The final simulation results verify the superior performance of the proposed algorithm.

In this paper, we consider real-time beamforming design for dynamic wireless environments with different channel state information (CSI) distributions in cell-free systems. Specifically, a sum-rate maximization optimization problem for different CSI distributions is built to model the beamforming design of dynamic wireless environments in cell-free systems. To efficiently solve the optimization problem, we propose a high-generalization network (HGNet). By preserving invariant features and discarding sensitive features for different CSI distributions, HGNet effectively improves the generalization performance of beamforming design for dynamic wireless environments in cell-free systems. Numerical results demonstrate that HGNet achieves a higher sum rate with a lower reflection time for different CSI distributions, thus realizing real-time beamforming design for dynamic wireless environments in cell-free systems.