Addressing Pilot Contamination in Channel Estimation with Variational Autoencoders

TL;DR

This paper introduces a variational autoencoder-based method to mitigate pilot contamination in massive MIMO systems, improving channel estimation accuracy by leveraging statistical knowledge of interfering channels.

Contribution

It presents the first application of VAEs for reducing pilot contamination effects in multi-cell channel estimation, utilizing statistical moments for enhanced performance.

Findings

Outperforms classical channel estimation methods under pilot contamination

Exploits interferers' statistical information for better estimates

Performance depends on setup configuration

Abstract

Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, PC constitutes one of the main bottlenecks of the system's performance. In this paper, we propose a method based on the variational autoencoder (VAE), capable of reducing the impact of PC-related interference during channel estimation (CE). We obtain the first and second-order statistics of the conditionally Gaussian (CG) channels for both the user equipments (UEs) in a cell of interest and those in interfering cells, and we then use these moments to compute conditional linear minimum mean square error estimates. We show that the proposed estimator is capable of exploiting the interferers' additional statistical knowledge, outperforming other classical approaches. Moreover, we highlight how the achievable performance is tied to the chosen setup, making the setup selection crucial in the study of multi-cell CE.