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.
翻译:极大规模多投入-多输出(XL-MIMO)是未来第六代(6G)网络实现更高性能的一个大有希望的技术。实际上,考虑到XL-IMIMO系统的空间非固定性特性,各种线性预编码(如零强制(ZF)和常规化的零强制(RZF)预编码(RZF)计划能够实现大型光谱效率(SE)和低位误差率(BER),但是,由于固有的空间非静止性和高计算复杂性,这些方法在超大型制度中效率不高。为了解决这一问题,我们调查低兼容性预编码算法(例如零强制(ZF)和常规化的零推进(RKA)计划,考虑到XL-MIM系统的空间非固定性特性,我们提出了一种新型随机化模式,即在没有替换 RKA(SWR-rKA) 算法(Swo-rKA) 具有更快的趋同速度。此外,由于SE-R-F的闭式表达方式使得SIMO(S)的S-RIMO)的递减结果,S-R-R-R-ral-ral 的递减结果显示的S-R-L)的S-R-R-r-ral,S-ral 的S-ral 的递减后,S-r-r-r-r-r-r-sal 递算结果结果显示了S-r-r-r-r-r-r-sx-sal 的S-sal 。