Low-Complexity Precoding for Extremely Large-Scale MIMO Over Non-Stationary Channels

TL;DR

This paper introduces low-complexity precoding algorithms, rKA and SwoR-rKA, for XL-MIMO systems that reduce computational complexity by over 50% while maintaining spectral efficiency and improving BER performance.

Contribution

The paper proposes a novel low-complexity randomized precoding method, SwoR-rKA, tailored for non-stationary XL-MIMO channels, with faster convergence and reduced complexity.

Findings

Both algorithms reduce complexity by 51.3% compared to RZF.

Algorithms maintain spectral efficiency similar to traditional methods.

Effective BER reduction with imperfect channel estimation.

Abstract

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.