Channel Equalization Using Machine Learning for Underwater Acoustic Communications

Sammanfattning: Wireless underwater acoustic (UWA) communications is a developing field with various applications. The underwater acoustic communication channel is very special and its behavior is environment-dependent. The UWA channel is characterized by low available bandwidth, and severe motion-introduced Doppler effect compared to wireless radio communication. Recent literature suggests that machine learning (ML)-based channel estimation and equalization offer benefits over traditional techniques (a decision feedback equalizer), in UWA communications. ML can be advantageous due to the difficultly in designing algorithms for UWA communication, as finding general channel models have proven to be difficult. This study aims to explore if ML-based channel estimation and equalization as a part of a sophisticated physical layer structure can offer improved performance. In the study, supervised ML using a deep neural network and a recurrent neural network will be utilized to improve the bit error rate. A channel simulator with environment-specific input is used to study a wide range of channels. The simulations are utilized to study in which environments ML should be tested. It is shown that in highly time-varying channels, ML outperforms traditional techniques if trained with prior information of the channel. However, utilizing ML without prior information of the channel yielded no improvement of the performance.