Blind SNR Estimation and Nonparametric Channel Denoising in Multi-Antenna mmWave Systems

TL;DR

This paper introduces low-complexity blind estimators for noise power, signal power, SNR, and MSE in multi-antenna mmWave systems, leveraging beamspace sparsity for efficient real-time tracking and denoising.

Contribution

It presents novel blind estimators based on beamspace sparsity, enabling efficient SNR and MSE estimation without pilot overhead in mmWave systems.

Findings

Estimators accurately track key quantities in simulated environments.

Proposed methods outperform traditional approaches in denoising tasks.

Theoretical analysis confirms estimator effectiveness.

Abstract

We propose blind estimators for the average noise power, receive signal power, signal-to-noise ratio (SNR), and mean-square error (MSE), suitable for multi-antenna millimeter wave (mmWave) wireless systems. The proposed estimators can be computed at low complexity and solely rely on beamspace sparsity, i.e., the fact that only a small number of dominant propagation paths exist in typical mmWave channels. Our estimators can be used (i) to quickly track some of the key quantities in multi-antenna mmWave systems while avoiding additional pilot overhead and (ii) to design efficient nonparametric algorithms that require such quantities. We provide a theoretical analysis of the proposed estimators, and we demonstrate their efficacy via synthetic experiments and using a nonparametric channel-vector denoising task with realistic multi-antenna mmWave channels.