Underwater Acoustic Rapidly Time-Varying Channel Equalization Technique Integrating Deep Learning and Domain Knowledge

Multicarrier communication schemes, such as orthogonal frequency division multiplexing(OFDM), are the mainstream solutions for achieving high spectral efficiency in underwater acoustic transmissions. These schemes effectively address frequency-selective fading caused by multipath acoustic propagation in underwater environments. However, in rapidly time-varying scenarios, inter-carrier interference(ICI) can severely compromise transmission reliability. To mitigate the time-frequency doubly-selective fading in such underwater acoustic rapidly time-varying channels and reduce the bit error rate(BER) of OFDM systems, this paper proposed an underwater acoustic rapidly time-varying channel equalization method that combined deep learning with domain knowledge. Different from regarding the outcomes of traditional channel estimation and equalization detection as preprocessing results or supplementary information sources for deep neural networks(DNNs), this paper employed the structured information from classical frequency-domain equalization models to assist in the training and inference of DNN models, so as to counteract the adverse effects of ICI and adapt to scenarios where there is a mismatch between the actual deployment channel environment and the training channel environment. Numerical simulation and sea trial results confirm that the proposed approach can effectively reduce the BER of receivers and achieve faster model convergence, and the potential to achieve stronger generalization performance under unknown channel conditions.