Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO

TL;DR

This paper introduces a scalable, robust Bayesian learning algorithm based on expectation propagation to mitigate pilot contamination in cell-free massive MIMO systems, improving uplink channel estimation and data detection.

Contribution

It proposes a novel distributed bilinear-EP algorithm for joint channel estimation and data detection that outperforms existing Bayesian methods in mitigating pilot contamination.

Findings

Non-orthogonal pilots can outperform orthogonal ones in certain scenarios.

The proposed metric effectively quantifies pilot contamination at the user equipment level.

Algorithm performance degrades monotonically with increased pilot contamination.

Abstract

Pilot contamination (PC) arises when the pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. In this work, we propose a novel expectation propagation (EP)-based joint channel estimation and data detection (JCD) algorithm specifically designed to mitigate the effects of PC in the uplink of cell-free massive multiple-input multiple-output (CF-MaMIMO) systems. This modified bilinear-EP algorithm is distributed, scalable, demonstrates strong robustness to PC, and outperforms state-of-the-art Bayesian learning algorithms. Through a comprehensive performance evaluation, we assess the performance of Bayesian learning algorithms for different pilot sequences and observe that the use of non-orthogonal pilots can lead to better performance compared to shared orthogonal sequences. Motivated by this analysis, we introduce a new metric to quantify PC at the UE level. We show that the performance of the considered algorithms degrades monotonically with respect to this metric, providing a valuable theoretical and practical tool for understanding and managing PC via iterative JCD algorithms.