Jac-PCG Based Low-Complexity Precoding for Extremely Large-Scale MIMO Systems

作者：Bokai Xu, Jiayi Zhang, Jiaxun Li, Huahua Xiao, Bo Ai · 发表于：IEEE Transactions on Vehicular Technology · 年份：2023 · DOI：10.1109/tvt.2023.3293546 · 被引用次数：9 · 研究领域：Advanced MIMO Systems Optimization、Energy Harvesting in Wireless Networks、Full-Duplex Wireless Communications

Extremely large-scale multiple-input-multiple-output (XL-MIMO) has been reviewed as a promising technology for future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized ZF (RZF) precoding, are sufficient to achieve near-optimal performance in traditional massive MIMO (mMIMO) systems. It is critical to note that in large-scale antenna arrays the operation of channel matrix inversion poses a significant computational challenge for these precoders. Therefore, we explore several iterative methods for determining the precoding matrix for XL-MIMO systems instead of direct matrix inversion. Taking into account small- and large-scale fading as well as spatial correlation between antennas, we study their computational complexity and convergence rate. Furthermore, we propose the Jacobi-Preconditioning Conjugate Gradient (Jac-PCG) iterative inversion method, which enjoys a faster convergence speed than the CG method. Besides, the closed-form expression of spectral efficiency (SE) considering the interference between subarrays in downlink XL-MIMO systems is derived. In the numerical results, it is shown that the complexity given by the Jac-PCG algorithm has about$54 \%$reduction than the traditional RZF algorithm at basically the same SE performance.