Super-Resolution Blind Channel-and-Signal Estimation for Massive MIMO with One-Dimensional Antenna Array

TL;DR

This paper introduces a super-resolution blind channel and signal estimation method for massive MIMO systems with one-dimensional antenna arrays, overcoming limitations of traditional approaches by using a sparse, high-resolution angular representation and an EM-based inference algorithm.

Contribution

It proposes a novel super-resolution sampling grid and a hidden Markovian support model, along with an efficient EM-based blind estimation algorithm for massive MIMO systems.

Findings

Significantly reduces estimation error compared to existing methods

Demonstrates robustness across various system settings

Provides a low-complexity implementation using factor graphs

Abstract

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method.