Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO

TL;DR

This paper introduces a distributed algorithm based on expectation propagation for joint user activity detection, channel estimation, and data detection in cell-free massive MIMO systems, effectively mitigating pilot contamination.

Contribution

The paper presents a novel distributed JACD algorithm using expectation propagation tailored for GF-CF-MaMIMO systems, outperforming existing methods.

Findings

Effective mitigation of pilot contamination in large user scenarios

Superior performance compared to state-of-the-art algorithms

Distributed message passing approach enhances scalability

Abstract

We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activities, channels, and transmitted data, then, mapping functions and variables onto a factor graph, and finally, performing a message passing on the resulting factor graph. If users with the same pilot sequence are sufficiently distant from each other, the JACD-EP algorithm is able to mitigate the effects of pilot contamination which naturally occurs in grant-free systems due to the large number of potential users and limited signaling resources. Furthermore, it outperforms state-of-the-art algorithms for JACD in GF-CF-MaMIMO systems.