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arXiv 2602.03590eess.SPcs.ITmath.IT

基于统计近似的分布式波束成形用于无中心大规模MIMO

Statistics Approximation-Enabled Distributed Beamforming for Cell-Free Massive MIMO

Zhe Wang, Emil Björnson, Jiayi Zhang, Peng Zhang, Vitaly Petrov, Bo Ai

AI总结:

本文提出一种基于统计近似的分布式波束成形方法,用于无中心大规模MIMO网络,实现与集中式MMSE方案相当的性能。

摘要:

我们研究了一种用于无中心大规模多输入多输出网络的分布式波束成形方法,称为全局统计与局部瞬时信息基于最小均方误差(GSLI-MMSE)。考虑多天线接入点(APs)在三种不同的信道模型下的情况:相关瑞利衰落以及具有固定或随机线-of-sight(LoS)相位移的相关瑞利衰落。借助矩阵求逆推导,我们可以从每个AP的角度构造传统MMSE组合,其中涉及全局瞬时信息。然后,对于任意一个AP,我们应用统计近似方法,利用信道统计信息来近似与其他AP相关的瞬时项,从而在每个AP上构造出具有局部瞬时信息和全局统计信息的分布式组合方案。借助上行-下行对偶性,我们推导出各自的GSLI-MMSE预编码方案。数值结果表明,所提出的GSLI-MMSE方案在稳定的LoS条件下,其性能与最优集中式MMSE方案相当,例如静态用户具有瑞利衰落且LoS路径固定的情况。

英文摘要:

We study a distributed beamforming approach for cell-free massive multiple-input multiple-output networks, referred to as Global Statistics & Local Instantaneous information-based minimum mean-square error (GSLI-MMSE). The scenario with multi-antenna access points (APs) is considered over three different channel models: correlated Rician fading with fixed or random line-of-sight (LoS) phase-shifts, and correlated Rayleigh fading. With the aid of matrix inversion derivations, we can construct the conventional MMSE combining from the perspective of each AP, where global instantaneous information is involved. Then, for an arbitrary AP, we apply the statistics approximation methodology to approximate instantaneous terms related to other APs by channel statistics to construct the distributed combining scheme at each AP with local instantaneous information and global statistics. With the aid of uplink-downlink duality, we derive the respective GSLI-MMSE precoding schemes. Numerical results showcase that the proposed GSLI-MMSE scheme demonstrates performance comparable to the optimal centralized MMSE scheme, under the stable LoS conditions, e.g., with static users having Rician fading with a fixed LoS path.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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