Learning to Equalize for Single-Carrier Underwater Acoustic Communications

Learning-based equalizers for multicarrier communication systems have been widely studied over underwater acoustic (UWA) channels. In this article, a learning-based equalizer is utilized for single-carrier (SC) underwater acoustic communications. A comprehensive comparison is made between existing deep learning (DL)-based approaches and a classical equalizer designed with adaptive filtering principles. It motivates the design of equalization for SC communications over underwater acoustic channels. To overcome distortion over the UWA channel, we propose a sliding deep learning-based equalizer that uses a sliding nonlinear network for equalization rather than a single-layer linear method. Moreover, to accelerate convergence during training, we proposed a preprocessing-based training phase. To mitigate the impact of time-varying channels, we additionally propose a meta-learning-enhanced adaptive filter algorithm for online adaptive equalization, named Meta-DNN. Based on the proposed DL equalizer, we leverage the pilot and data relationship to perform online transfer to achieve better BER performance. Moreover, to make this work more convincing, we test bit-error-rate (BER) performance across reproducible, realistic multi-scenario channels.