Performance Analysis on Machine Learning-Based Channel Estimation
This work addresses a theoretical gap for researchers and engineers in wireless communications, but it is incremental as it builds on existing machine learning methods for channel estimation.
The paper tackles the lack of theoretical performance analysis for machine learning-based channel estimation by investigating its mean square error (MSE) performance, deriving an analytical relation between training data size and performance, and verifying results through simulations in OFDM systems.
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.