Underwater wireless communication networks have found a wide range of applications, such as environmental monitoring, offshore exploration, disaster prevention, and military surveillance. These wireless networks are required to provide sophisticated real-time data collection and serve for prolonged deployment time, such as 30 to 50 years. The objective of this research is to develop perpetual underwater wireless networks in which high-speed multiple-input multiple-output (MIMO) transceivers are powered up by long-lasting super-capacitors via energy harvesting from environmental energy sources. This project will develop real-time resource allocation and stochastic optimization solutions to efficiently allocate the bandwidth and power resources in underwater energy-harvesting wireless networks, taking into account the unique dynamics of energy-harvesting power sources and the time variation of the underwater channels. The proposed approach will establish a practical design approach for energy-harvesting MIMO transceivers to achieve high reliability, high data rate, and high energy efficiency, in the presence of highly dynamic underwater acoustic communication environments. Another important goal of the project is to train next-generation researchers through a set of education and outreach activities including course development, graduate and undergraduate student training, service to local high school robotics teams, and participation in marine robotics competition. These activities will also advocate responsible engineering approaches for reducing sound pollution in oceans.
Underwater wireless communications often require large MIMO transceivers to achieve the demanding requirements on reliability and throughput. As such, resource allocation for energy-harvesting MIMO communication becomes a complicated joint design of precoding and bandwidth allocation that involves a large number of design variables. Meanwhile, the dynamics of energy-harvesting power sources and the time variation of the underwater channels are on a similar time scale, thus requiring online stochastic optimization instead of the offline resource allocation commonly used in MIMO RF networks. To address these unique challenges in perpetual underwater wireless networks, this project formulates the joint online precoder design and bandwidth allocation into a multi-variable stochastic dynamic programming (SDP) problem, and proposes a novel nested optimization algorithm to solve it iteratively with low computational complexity. This project contributes to advancing wireless communications and network technology by a novel nested optimization algorithm that solves the multi-variable SDP problem with linear computational complexity and an online resource allocation method that uses stochastic models on both communication channels and energy sources rather than commonly used deterministic models. A hardware test bed will also be built to verify the online resource allocation algorithms and super-capacitor energy sources.
