Adaptive Transmission Performance of Underwater Autoencoder Group Based on DNN Channel Estimation

Autoencoders can leverage deep neural networks to jointly optimize transmitters and receivers for end-to-end communication performance. The time-varying characteristics of underwater channels due to turbulence, absorption, and scattering seriously affect the reliability of autoencoder-based underwater wireless optical communication (UWOC) systems. In order to reduce the need for complex online training of autoencoders in real underwater channels, we propose a deep autoencoder group adaptive transmission scheme, which can adaptively select the optimal autoencoder group at the transmitter side for signaling based on the instantaneous channel state information (CSI) estimation obtained using a deep neural network (DNN) during the online transmission process, thus suppressing the underwater effect of the time-varying channel. The selection of the optimal number of encoders in the autoencoder group can balance the error performance and complexity of the system, as well as reduce the complexity of the system while ensuring the reliability of the adaptive transmission system.