Performance Analysis on Machine Learning-Based Channel Estimation

TL;DR

This paper provides a theoretical analysis of the mean square error performance of machine learning-based channel estimation, deriving bounds and relations that aid in understanding and designing such systems with limited training data.

Contribution

It introduces a statistical model and analytical bounds for the MSE of machine learning-based channel estimation, focusing on linear modules with low input dimensions.

Findings

Derived an MSE upper bound using hypothesis testing.

Established a relation between training data size and estimation performance.

Validated analysis through OFDM system simulations.

Abstract

Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.