Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

TL;DR

This paper introduces a joint channel estimation and data detection algorithm for cell-free massive MU-MIMO systems that reduces pilot overhead and improves communication reliability for many users.

Contribution

It presents a novel iterative JED algorithm that exploits channel sparsity and QAM boundedness, with methods to enhance convergence via virtual cells.

Findings

Significantly reduces pilot overhead compared to orthogonal training.

Achieves lower symbol error rates and bit error rates.

Enables reliable communication with short packets for many UEs.

Abstract

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.