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

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.

arXiv comment5 pages, 2 figures. This paper has been accepted for publication in IEEE Wireless Communications Letters. Code available at https://github.com/jiananbai/distributed-AMP

References in corpus (6)

• Message Passing Algorithms for Compressed Sensing
• Massive Connectivity with Massive MIMO-Part I: Device Activity Detection and Channel Estimation
• Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO
• Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver
• Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO
• Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks with Massive Connectivity

Cited by in corpus (2)

• Activity Detection for Massive Connectivity in Cell-free Networks with Unknown Large-scale Fading, Channel Statistics, Noise Variance, and Activity Probability: A Bayesian Approach
• Covariance-Based Device Activity Detection with Massive MIMO for Near-Field Correlated Channels