Decentralized Expectation Propagation for Semi-Blind Channel Estimation in Cell-Free Networks

TL;DR

This paper introduces a decentralized Expectation Propagation method for semi-blind channel estimation in cell-free massive MIMO systems, reducing computational complexity and mitigating pilot contamination.

Contribution

It presents a novel decentralized EP-based semi-blind channel estimation technique that simplifies processing and eliminates auxiliary variables, improving efficiency in cell-free networks.

Findings

Reduces computational load on central CPU

Effectively mitigates pilot contamination

Enhances semi-blind channel estimation accuracy

Abstract

This paper serves as a correction to the conference version. In this work, we explore uplink communication in cell-free (CF) massive multiple-input multiple-output (MaMIMO) systems, employing semi-blind transmission structures to mitigate pilot contamination. We propose a simplified, decentralized method based on Expectation Propagation (EP) for semi-blind channel estimation. By utilizing orthogonal pilots, we preprocess the received signals to establish a simplified equivalent factorization scheme for the transmission process. Moreover, this study integrates Central Limit Theory (CLT) with EP, eliminating the need to introduce new auxiliary variables in the factorization scheme. We also refine the algorithm by assessing the variable scales involved. Finally, a decentralized approach is proposed to significantly reduce the computational demands on the Central Processing Unit (CPU).