Adaptive Modulation and Coding for Underwater Acoustic

In this paper, an adaptive modulation and coding (AMC) scheme is proposed that aims to maximize the energy efficiency of a single link by jointly scheduling the coding rate, modulation order and transmission power. Considering the complexity of UWA channels, a deep neural network (DNN)-based bit-error-rate (BER) estimation method is proposed that allows channel estimation, feature extraction, and BER estimation by using a fixed guide frequency of the feedback link. A channel classification method based on BER for all modulation and coding methods (MCS) is designed, and the UWA channel is further modeled as a finite-state Markov chain (FSMC) with unknown transfer probability. to formulate the AMC problem as a Markov decision process (MDP), and a DDQN method was used to achieve near-optimal energy efficiency.