The increasing demand for exploring and managing the vast marine resources of the planet has underscored the importance of research on advanced underwater acoustic communication (UAC) technologies. However, owing to the severe characteristics of the oceanic environment, underwater acoustic (UWA) propagation experiences nearly the harshest wireless channels in nature. This article resorts to the perspective of machine learning (ML) to cope with the major challenges of adaptive modulation and coding (AMC) design in UACs. First, we present an ML AMC framework for UACs. Then, we propose an attention-aided k-nearest neighbor (A-kNN) algorithm with simplicity and robustness, based on which an ML AMC approach is designed with immunity to channel modeling uncertainty. Leveraging its online learning ability, such A-kNN-based AMC classifier offers salient capabilities of both sustainable self-enhancement and broad applicability to various operation scenarios. Next, aiming at higher implementation efficiency, we take strategies of complexity reduction and present a dimensionality-reduced and data-clustered A-kNN (DRDC-A-kNN) AMC classifier. Finally, we demonstrate that these proposed ML approaches have superior performance over traditional model-based methods by simulations using actual data collected from three lake experiments.

对探索和管理地球上大量海洋资源的需求不断增长，这突出了研究先进的水下声通信（UAC）技术的重要性。但是，由于海洋环境的严重特性，水下声学（UWA）传播几乎经历了自然界中最恶劣的无线信道。本文从机器学习（ML）的角度出发，以应对UAC中自​​适应调制和编码（AMC）设计的主要挑战。首先，我们介绍用于UAC的ML AMC框架。然后，我们提出一个注意辅助的k最近邻（A- k具有简单性和鲁棒性的NN）算法，在此算法的基础上设计了一种ML AMC方法，不受信道建模不确定性的影响。凭借其在线学习能力，此类基于Ak k NN的AMC分类器提供了可持续的自我增强功能和广泛适用于各种操作场景的显着功能。接下来，针对更高的实施效率，我们采取降低复杂性的策略，并提出了降维和数据聚类的A- k NN（DRDC-A- k NN）AMC分类器。最后，通过使用从三个湖泊实验收集的实际数据进行仿真，我们证明了这些拟议的ML方法优于传统的基于模型的方法。