Performance Comparison of Neural Network and Minimum Mean Square Error Methods for Channel Estimation in Severe Frequency Selective Fading Channel

Wireless communication systems require accurate channel estimation to compensate for the effects of fading channels. Traditional methods, such as minimum mean square error (MMSE) channel estimation, rely heavily on prior channel knowledge. Different types of MMSE channel estimation can be defined by the kinds of the prior channel knowledge applied. Recently, machine learning (ML) techniques, especially neural networks (NN), have shown great effectiveness in handling large volumes of data. This letter compares the performance of NN with various types of MMSE channel estimation. The findings confirm that NN can outperform MMSE methods that rely solely on statistical knowledge, maintaining high estimation accuracy even in severe frequency selective channel.