Unsupervised Massive MIMO Channel Estimation with Dual-Path Knowledge-Aware Auto-Encoders

TL;DR

This paper introduces an unsupervised deep learning framework using dual-path variational auto-encoders for accurate angle-of-arrivals and channel estimation in massive MIMO systems, leveraging physical knowledge for improved performance.

Contribution

It proposes a novel dual-path VAE with a knowledge-aware decoder for physical parameter estimation in massive MIMO, enhancing convergence and accuracy over traditional methods.

Findings

The framework accurately estimates AoAs and channel parameters.

The dual-path VAE converges reliably with the proposed initialization.

Numerical results show performance improvements over existing methods.

Abstract

In this paper, an unsupervised deep learning framework based on dual-path model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals (AoAs) and channel estimation in massive MIMO systems. Specifically designed for channel estimation, the proposed VAE differs from the original VAE in two aspects. First, the encoder is a dual-path neural network, where one path uses the received signal to estimate the path gains and path angles, and another uses the correlation matrix of the received signal to estimate AoAs. Second, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder forces the encoder to output meaningful physical parameters of interests (i.e., path gains, path angles, and AoAs), which cannot be achieved by original VAE. Rigorous analysis is carried out to characterize the multiple global optima and local optima of the estimation problem, which motivates the design of the dual-path encoder. By alternating between the estimation of path gains, path angles and the estimation of AoAs, the encoder is proved to converge. To further improve the convergence performance, a low-complexity procedure is proposed to find good initial points. Numerical results validate theoretical analysis and demonstrate the performance improvements of our proposed framework.