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.
