Low-Complexity Near-Field Channel Estimation and Subcarrier-Cooperative Hybrid Precoding for Wideband XL-MIMO OFDM Systems

Abstract

This paper addresses both accurate near-field channel acquisition and scalable precoding for wideband extremely large aperture MIMO (XL-MIMO) OFDM systems, with particular focus on reducing complexity. In the considered system, each MIMO multipath component is characterized by five continuous angle-distance-delay parameters, making conventional sparse recovery methods computationally infeasible while suffering from grid mismatch. We propose a decoupled off-grid channel estimation algorithm based on sequential sparse Bayesian learning (SBL). By exploiting the separable structure of the OFDM near-field atom, the five-dimensional joint search is replaced by a sequence of low-dimensional operations and active-set inference, avoiding multiplicative scaling with the per-dimension grid sizes. A continuous-domain Newton refinement is incorporated based on the exact marginal likelihood to mitigate the grid mismatch. Based on the estimated multipath parameters, we then develop a subcarrier-cooperative hybrid precoding framework for multiuser wideband transmission. A large-scale-matrix-inversion-free alternating optimization algorithm is established to maximize the sum user rate, together with a non-iterative low-complexity design that exploits the parameterized channel structure and provides an effective initialization. Simulation results demonstrate the accuracy of the estimation methods compared to several benchmarks and the effectiveness of the precoding algorithm based on estimated channel parameters.