Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems

TL;DR

This paper introduces a decentralized L-MMSE equalizer for massive MIMO systems that operates under colored noise, overcoming limitations of previous white noise assumptions and reducing processing bottlenecks.

Contribution

It develops a novel decentralized equalization method under colored noise, using block coordinate descent for near-optimal performance in massive MIMO uplink processing.

Findings

Achieves near-optimal equalization performance in simulations.

Significantly reduces data interconnection and computational bottlenecks.

Extends decentralized processing to more realistic colored noise scenarios.

Abstract

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antennas increases, centralized baseband processing architectures encounter two bottlenecks, i.e., the tremendous data interconnection and the high-dimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear minimum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks.