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Computer Science > Information Theory

arXiv:2305.13925 (cs)
[Submitted on 23 May 2023]

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

Authors:Bokai Xu, Jiayi Zhang, Jiaxun Li, Huahua Xiao, Bo Ai
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Abstract:Extremely large-scale multiple-input-multipleoutput (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.
Subjects: Information Theory (cs.IT); Signal Processing (eess.SP)
Cite as: arXiv:2305.13925 [cs.IT]
  (or arXiv:2305.13925v1 [cs.IT] for this version)
  https://doi.org/10.48550/arXiv.2305.13925

Submission history

From: Bokai Xu [view email]
[v1] Tue, 23 May 2023 10:51:51 UTC (734 KB)
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