Activity Detection in Distributed MIMO: Distributed AMP via Likelihood Ratio Fusion

TL;DR

This paper introduces a distributed AMP algorithm for activity detection in MIMO systems, leveraging likelihood ratio fusion and a novel state evolution insight, leading to improved detection performance.

Contribution

The paper presents a novel distributed AMP algorithm with a new theoretical insight into state evolution, enhancing activity detection in distributed MIMO systems.

Findings

Algorithm outperforms existing schemes in numerical tests.

State evolution maintains block-diagonal structure with MMSE denoiser.

Theoretical basis links covariance structure to AMP state evolution.

Abstract

We develop a new algorithm for activity detection for grant-free multiple access in distributed multiple-input multiple-output (MIMO). The algorithm is a distributed version of the approximate message passing (AMP) based on a soft combination of likelihood ratios computed independently at multiple access points. The underpinning theoretical basis of our algorithm is a new observation that we made about the state evolution in the AMP. Specifically, with a minimum mean-square error denoiser, the state maintains a block-diagonal structure whenever the covariance matrices of the signals have such a structure. We show by numerical examples that the algorithm outperforms competing schemes from the literature.