CAREER: Online Learning-based Underwater Acoustic Communications and Networking

职业：基于在线学习的水下声学通信和网络

• 批准号: 1651135
• 负责人: Zhaohui Wang
• 金额: $ 50万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651135&HistoricalAwards=false
• 关键词: CAREER; Online; Learning; based; Underwater

Underwater acoustic communication networks are the enabling technologies for unmanned, in situ, and real-time aquatic monitoring in a wide range of applications, such as scientific studies, pollution detection, offshore exploration, and tactical surveillance. The lifespan of underwater systems varies from a few years to decades, while the spatiotemporal dynamics of underwater acoustic environments at multiple scales pose grand challenges to efficient and reliable acoustic data transmission. The objective of this project is to develop a fundamental and systematic online-learning-based framework for underwater acoustic communications and networking, where the underwater acoustic system 1) models and predicts the long-term dynamics of the acoustic environment, and 2) proactively adapts its communication and networking strategy to the dynamics of the environment, thereby maximizing the long-term system performance in the aspects of energy efficiency, spectrum efficiency, and transmission reliability. Through explicit learning about its environment, the proposed framework will allow harmonious co-existence with other acoustic systems, including marine animals, to achieve eco-friendly operation. This project's research will be integrated with education through summer youth K-12 outreach, curriculum development, undergraduate and graduate student training that will be particularly tailored to females and underrepresented minorities, and collaboration with an underwater robotics team from the local Dollar Bay High School. These activities are designed to motivate and better train rural, female, minority, and economically disadvantaged students to pursue STEM careers.This project tackles fundamental challenges in online-learning-based underwater acoustic communications and networking by innovating across three interrelated domains. First, novel signal processing and sparse learning techniques will be developed to model and predict the large-scale dynamics and the statistical distribution of small-scale fading of underwater acoustic environments, including the acoustic transmission loss, ambient soundscape, and statistical characterization of external (anthropogenic and marine animal) acoustic sources. Second, an optimization framework will be developed, based on the acoustic environment prediction, for joint transmission power control, link scheduling, node-cooperative routing, and autonomous vehicle mobility control to achieve high network utility and harmonious coexistence with other acoustic systems. Third, the acoustic environment exploration-exploitation tradeoff will be tackled in the Bayesian reinforcement learning framework, which will provide a principled approach to weighing the immediate reward of a communication and networking strategy and its associated long-term benefit of revealing the environment's dynamics. Leveraging the geographic advantage of Michigan Tech and the state-of-the-art facilities of Michigan Tech's Great Lakes Research Center, extensive field experiments will be conducted for acoustic measurement collection and for offline and online algorithm evaluation in a software-defined networking architecture. The methodologies and crosscutting techniques developed in this project can be applied to the design of intelligent radio-frequency communication networks.

水下声学通信网络是无人、原位和实时水生监测的使能技术，其应用范围广泛，例如科学研究、污染检测、近海勘探和战术监视。水下系统的寿命从几年到几十年不等，而多尺度下的水声环境的时空动态对有效和可靠的声学数据传输提出了巨大的挑战。该项目的目标是为水下声学通信和联网开发一个基本的、系统的、基于在线学习的框架，其中水下声学系统1）建模和预测声学环境的长期动态，2）主动调整其通信和联网战略以适应环境的动态，从而在能量效率、频谱效率和传输可靠性方面最大化长期系统性能。通过明确了解其环境，拟议的框架将允许与其他声学系统（包括海洋动物）和谐共存，以实现生态友好型操作。该项目的研究将通过夏季青年K-12外展，课程开发，本科生和研究生培训与教育相结合，这些培训将特别针对女性和代表性不足的少数民族，并与当地Dollar Bay高中的水下机器人团队合作。这些活动旨在激励和更好地培训农村、女性、少数民族和经济困难的学生从事STEM职业。该项目通过在三个相互关联的领域进行创新，解决基于在线学习的水声通信和网络的基本挑战。首先，将开发新的信号处理和稀疏学习技术，以模拟和预测水下声学环境的大规模动态和小规模衰落的统计分布，包括声学传输损耗、环境声景和外部（人类和海洋动物）声源的统计特性。其次，将开发一个优化框架，基于声学环境预测，用于联合传输功率控制，链路调度，节点协作路由和自主车辆移动性控制，以实现高网络效用和与其他声学系统的和谐共存。第三，将在贝叶斯强化学习框架中解决声学环境探索-利用权衡问题，该框架将提供一种原则性方法来权衡通信和网络战略的即时回报及其揭示环境动态的相关长期利益。利用密歇根理工大学的地理优势和密歇根理工大学五大湖研究中心最先进的设施，将在软件定义的网络架构中进行广泛的现场实验，用于声学测量收集和离线和在线算法评估。本计画所发展之方法论与横切技术可应用于智慧型射频通讯网路之设计。

项目成果

Adaptive Switching for Multimodal Underwater Acoustic Communications Based on Reinforcement Learning

基于强化学习的多模态水声通信自适应切换

• DOI: 10.1145/3491315.3491354
• 发表时间: 2021
• 期刊: the 15th International Conference on Underwater Networks & Systems (WUWNet
• 作者: Fan, Cheng;Wang, Zhaohui
• 通讯作者: Wang, Zhaohui

Parameterized Interference Cancellation for Single-Carrier Underwater Acoustic Communications

• DOI: 10.1109/ieeeconf38699.2020.9389172
• 发表时间: 2020-10
• 期刊: Global Oceans 2020: Singapore – U.S. Gulf Coast
• 作者: Wei Ge;Zhaohui Wang;Jingwei Yin;Xiao Han

Exploring Simulation of Software-Defined Underwater Wireless Networks

• DOI: 10.1145/3148675.3148720
• 发表时间: 2017-11
• 期刊: Proceedings of the 12th International Conference on Underwater Networks & Systems
• 作者: Li Wei;Yuxin Tang;Y. Cao;Zhaohui Wang;M. Gerla

Receiver design for spread-spectrum communications with a small spread in underwater clustered multipath channels

水下集​​群多径信道中小扩展扩频通信的接收机设计

• DOI: 10.1121/1.4977747
• 发表时间: 2017
• 期刊: Journal of the Acoustical Society of America
• 影响因子: 2.4
• 作者: Kuai Xiaoyan;Zhou Shengli;Wang Zhaohui;Cheng En
• 通讯作者: Cheng En

A Variational Auto-Encoder Model for Underwater Acoustic Channels

• DOI: 10.1145/3491315.3491330
• 发表时间: 2021-11
• 期刊: Proceedings of the 15th International Conference on Underwater Networks & Systems
• 作者: Li Wei;Zhaohui Wang