Approaching the MMSE Bound of Channel Estimation by Machine Learning

In wireless communication systems where the received signal model is linear with Gaussian-distributed channel and noise, linear minimum mean square error (LMMSE) channel estimation (CE) achieves the best performance in terms of mean square error (MSE). However, LMMSE CE relies on parameters that may be either unavailable at the receiver (e.g., accurate knowledge of the power delay profile (PDP)) or too complex for practical implementation (e.g., the LMMSE filter size). A suboptimal choice of parameters may severely degrade LMMSE CE performance. Motivated by this observation, we investigate machine learning as a tool for refining and improving CE performance. We show that our proposed low-complexity learning-aided LMMSE CE can overcome the impact of suboptimal parameters and approach the ideal LMMSE performance.