Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO

Forsch C, Zhao Z, Slock D, Cottatellucci L (2025)


Publication Type: Conference contribution

Publication year: 2025

Publisher: IEEE

City/Town: New York City

Pages Range: 19-25

Conference Proceedings Title: 2025 28th International Workshop on Smart Antennas (WSA)

Event location: Erlangen DE

DOI: 10.1109/WSA65299.2025.11202775

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.

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How to cite

APA:

Forsch, C., Zhao, Z., Slock, D., & Cottatellucci, L. (2025). Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO. In 2025 28th International Workshop on Smart Antennas (WSA) (pp. 19-25). Erlangen, DE: New York City: IEEE.

MLA:

Forsch, Christian, et al. "Bayesian Learning for Pilot Decontamination in Cell-Free Massive MIMO." Proceedings of the 2025 28th International Workshop on Smart Antennas (WSA), Erlangen New York City: IEEE, 2025. 19-25.

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