Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation

TL;DR

This paper introduces a statistical method to predict the mean squared error of deep learning-based channel estimators in ultra-massive MIMO systems without requiring ground-truth data, enhancing reliability and practical deployment.

Contribution

It proposes a low-complexity, blind prediction technique for DL-based channel estimation accuracy, independent of prior channel knowledge or noise statistics.

Findings

Accurately predicts MSE of DL estimators using received pilots

Scales linearly with the number of antennas, suitable for ultra-massive MIMO

Verifies effectiveness with simulations involving mixed near-field and far-field paths

Abstract

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical deployment. In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators. An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics. The complexity of the blind performance prediction is low and scales only linearly with the number of antennas. Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.