Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO

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.

7 pages, 8 figures, published in Proceedings of the 28th International Workshop on Smart Antennas (WSA)

References in corpus (6)

• A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
• Blind pilot decontamination
• Massive MIMO has Unlimited Capacity
• Robust Pilot Decontamination Based on Joint Angle and Power Domain Discrimination
• Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach
• Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO