Low-Complexity Blind Estimator of SNR and MSE for mmWave Multi-Antenna Communications

TL;DR

This paper introduces a low-complexity blind estimation method for SNR and MSE in mmWave multi-antenna systems that leverages channel sparsity to improve real-time robustness without requiring pilot signals.

Contribution

It presents a novel blind estimator that avoids high computational complexity and ground-truth knowledge, utilizing beamspace sparsity for accurate SNR and MSE estimation.

Findings

Achieves accurate SNR and MSE estimation without pilot signals.

Reduces computational complexity for real-time applications.

Enhances robustness of mmWave systems in dynamic environments.

Abstract

To enhance the robustness and resilience of wireless communication and meet performance requirements, various environment-reflecting metrics, such as the signal-to-noise ratio (SNR), are utilized as the system parameter. To obtain these metrics, training signals such as pilot sequences are generally employed. However, the rapid fluctuations of the millimeter-wave (mmWave) propagation channel often degrade the accuracy of such estimations. To address this challenge, various blind estimators that operate without pilot have been considered as potential solutions. However, these algorithms often involve a training phase for machine learning or a large number of iterations, which implies prohibitive computational complexity, making them difficult to employ for real-time services and the system less resilient to dynamic environment variation. In this paper, we propose blind estimators for average noise power, signal power, SNR, and mean-square error (MSE) that do not require knowledge of the ground-truth signal or involve high computational complexity. The proposed algorithm leverages the inherent sparsity of mmWave channel in beamspace domain, which makes the signal and noise power components more distinguishable.