Blind Estimation of Sparse Broadband Massive MIMO Channels with Ideal and One-bit ADCs

TL;DR

This paper presents a robust blind channel estimation method for massive MIMO systems that exploits sparsity in the angular domain, effectively handles one-bit quantization, and reduces pilot overhead in rapidly changing environments.

Contribution

It introduces a maximum likelihood approach leveraging sparsity for blind estimation in broadband massive MIMO with one-bit ADCs, improving accuracy and robustness over existing methods.

Findings

Outperforms traditional methods in accuracy and robustness.

Effective in low SNR and rapidly time-varying scenarios.

Achieves near-optimal performance with limited assumptions.

Abstract

We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. The main advantage of this approach is the fact that the overhead due to pilot sequences can be dramatically reduced especially when operating at low SNR per antenna. In addition, as sparsity is the only assumption made about the channel, the proposed method is robust with respect to the statistical properties of the channel and data and allows the channel estimation and the separation of interfering users from adjacent base stations to be performed in rapidly time-varying scenarios. For the case of one-bit receivers, a blind channel estimation is proposed that relies on the Expectation Maximization (EM) algorithm. Additionally, performance limits are derived based on the clairvoyant Cramer Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy in the narrowband as well as the wideband regime with reasonable computational complexity and limited model assumptions.