Decentralized MIMO Systems with Imperfect CSI using LMMSE Receivers

TL;DR

This paper analyzes decentralized MIMO systems with imperfect CSI using LMMSE receivers, deriving optimal and sub-optimal fusion schemes, SINR expressions, and strategies for antenna partitioning to improve performance and reduce complexity.

Contribution

It introduces the first comprehensive analysis of decentralized MIMO with imperfect CSI, proposing optimal and sub-optimal fusion schemes and deriving closed-form SINR expressions.

Findings

Optimal fusion scheme with high computational cost

Sub-optimal schemes with reduced complexity

SINR decreases as the number of clusters increases

Abstract

Centralized baseband processing (CBP) is required to achieve the full potential of massive multiple-input multiple-output (MIMO) systems. However, due to the large number of antennas, CBP suffers from two major issues: 1) Tremendous data interconnection between radio frequency (RF) circuitry and processing fabrics; and 2) high-dimensional computation. To this end, decentralized baseband processing (DBP) has been proposed, where the antennas at the BS are partitioned into clusters connected to separate RF circuits and equipped with separate computing units. Unfortunately, due to the decentralized structure, the optimal fusion scheme and performance analysis for DBP with general spatial correlation between clusters and imperfect channel state information (CSI) are not available in the literature. In this paper, we consider a decentralized MIMO system where all clusters adopt linear minimum mean-square error (LMMSE) receivers with imperfect CSI. Specifically, we first establish the optimal linear fusion scheme which has high computational and data input/output (I/O) costs. To reduce the costs, we further propose two sub-optimal fusion schemes with reduced complexity. For all three schemes, we derive the closed-form expressions for the signal-to-interference-and-noise ratio (SINR) by leveraging random matrix theory (RMT) and demonstrate the conditions under which the sub optimal schemes are optimal. Furthermore, we determine the optimal regularization parameter for decentralized LMMSE receivers, identify the best antenna partitioning strategy, and prove that the SINR will decrease as the number of clusters increases. Numerical simulations validate the accuracy of the theoretical results.