水声通信中的信道估计与机器学习交叉研究进展

Advances in the intersection of channel estimation and machine learning in underwater acoustic communications

近年来,以深度学习为代表的机器学习技术飞速发展,凭借其出色的学习能力,在复杂环境条件下的建模问题中展现出了独特的优势。当前,基于机器学习的水声通信技术研究方兴未艾,在信道估计及均衡、典型通信系统应用等方面取得了一定的进展,但是针对实际水声环境约束条件下的研究较少。为此,文章围绕信道估计这一水声通信关键技术,针对水声信道估计中存在的样本不足,标签标定困难以及水声环境时空变导致的源域、目标域失配等问题,讨论了水声信道估计与数据增强、无标签学习、少样本学习等模型和方法交叉研究的发展思路,并给出了初步的仿真和试验结果。文章是对水声通信中的信道估计与机器学习交叉领域研究重难点问题的初步探索,为水下各类平台自主智能化的通信技术发展提供了参考。

In recent years,machine learning technology represented by deep learning has been developing rapidly.With its excellent learning ability,it has shown its unique advantages in modeling problems under complex environmental conditions.Currently,the research on the machine learning based underwater acoustic(UWA)communication is flourishing,and certain progresses have been made in channel estimation and equalization,typical communication system applications,and other aspects.However,few researches regard the real-world constraints on UWA communications.Therefore,the development ideas of UWA channel estimation with data augmentation,label-free learning,and few-shot learning are discussed in view of the issues of insufficient samples in UWA communication,labeling difficulties,and source/target domain mismatches due to the spatial and temporal variability.Besides,the preliminary simulation and experimental results are given.This paper is a preliminary exploration of the important and difficult issues in the intersection research on UWA communication and machine learning,which provides a reference for the development of autonomous and intelligent communication technology of various underwater platforms.