Decentralized Baseband Processing with Gaussian Message Passing Detection for Uplink Massive MU-MIMO Systems

TL;DR

This paper introduces a decentralized Gaussian message passing detection method for massive MU-MIMO systems that improves convergence and performance by partitioning antennas into clusters and fusing local estimates.

Contribution

It develops a novel decentralized GMP detection algorithm with a message fusion rule, enhancing detection efficiency in DBP architectures for massive MU-MIMO.

Findings

Nonuniform antenna clustering improves convergence rate.

Decentralized GMP outperforms existing decentralized algorithms.

Analytical convergence verified by simulations.

Abstract

Decentralized baseband processing (DBP) architecture, which partitions the base station antennas into multiple antenna clusters, has been recently proposed to alleviate the excessively high interconnect bandwidth, chip input/output data rates, and detection complexity for massive multi-user multiple-input multiple-output (MU-MIMO) systems. In this paper, we develop a novel decentralized Gaussian message passing (GMP) detection for the DBP architecture. By projecting the discrete probability distribution into a complex Gaussian function, the local means and variances iteratively calculated in each antenna cluster are fused to generate the global symbol beliefs based on the proposed message fusion rule in the centralized processing unit. We present the framework and analysis of the convergence of the decentralized GMP detection based on state evolution under the assumptions of large-system limit and Gaussian sources. Analytical results corroborated by simulations demonstrate that nonuniform antenna cluster partition scheme exhibits higher convergence rate than the uniform counterpart. Simulation results illustrate that the proposed decentralized GMP detection outperforms the recently proposed decentralized algorithms.