Adaptive Modulation and Coding for Underwater Acoustic OTFS Communications Based on Meta-Learning

This letter proposes an adaptive modulation and coding (AMC) scheme based on deep learning for underwater acoustic (UWA) communications. To achieve good communication performance in fast time-varying UWA channels, the proposed AMC scheme is implemented on the orthogonal time-frequency space (OTFS) modulation system. We design an end-to-end deep convolutional neural network (CNN) to capture the channel features and determine the optimal modulation and coding scheme. Additionally, we utilize a meta-learning algorithm to address environment mismatch in real-world UWA applications. This algorithm effectively adapts the CNN model from a given UWA environment to a new UWA environment with only a small amount of data. The performance of the proposed scheme is verified through real-world measured channels. Simulation results demonstrate that the proposed method outperforms existing machine learning-based AMC and fixed modulation and coding schemes in various UWA scenarios, offering better communication throughput and stronger learning capabilities.