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

• Published in: IEEE communications letters 2024-08, Vol.28 (8), p.1845-1849
• Main Authors: Jing, Lianyou, Dong, Chaofan, He, Chengbing, Shi, Wentao, Wang, Han, Zhou, Yi
• Format: Article
• Language: English
• Subjects: adaptive modulation and coding; Algorithms; Artificial neural networks; Channels; Coding; convolutional neural network; Convolutional neural networks; Deep learning; Feature extraction; Kernel; Machine learning; meta-learning; Modulation; orthogonal time frequency space; Symbols; Task analysis; Time-frequency analysis; Underwater acoustic communications; Underwater acoustics
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2024.3418192

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.