Machine Learning for Underwater Acoustic Communications

• DOI: 10.1109/mwc.2020.2000284
• Publication Date: 2022-05-05T19:56:54Z
• Authors: Lihuan Huang; Yue Wang; Qunfei Zhang; Jing Han; Weijie Tan; Zhi Tian
• Keywords: Underwater acoustic communication

Energy-efficient and link-reliable underwater acoustic communication (UAC) systems are of vital importance to both marine scientific research oceanic resource exploration. However, owing the unique characteristics environments, (UWA) propagation experiences arguably harshest wireless channels in nature. As a result, traditional model-based approaches system design implementation may no longer be effective or reliable for UAC systems. In this article, we resort machine learning (ML) techniques empower with intelligence capabilities, which capitalize on potential ML progressively improving performance through task-oriented from data. We first briefly overview literature ML. Then, illustrate promising ML-based solutions by highlighting one specific niche application adaptive modulation coding (AMC). Lastly, discuss other key open issues opportunities layer-by-layer, focus providing concise taxonomy algorithms relevant networks.