The multi-radio multi-channel (MR-MC) networking provides a generic computing platform for a wide range of next-generation wireless networks. However, the capacity of MR-MC networking has been underexplored, due to the lack of effective computing methodologies to address the complex multi-dimensional resource allocation. The current state of the art of MR-MC network capacity analysis is limited to heuristic algorithms and loose capacity bound analysis.
This project targets at breakthrough studies on optimal resource allocation and low-complexity algorithm development for MR-MC networks. The proposed research activities weave up optimization theory, graph theory, stochastic control, and asymptotic scaling law analysis, to reveal the underpinning factors that distinguish the MR-MC network optimization from the single-radio single-channel (SR-SC) counterpart, develop low-complexity algorithms with theoretically provable performance, and exploit the particular advantages of MR-MC networking in incorporating new techniques for further capacity enhancement.
This project will develop a set of novel theoretical tools to fundamentally address the capacity optimization and computational complexity in a multi-dimensional resource space. On the education front, this interdisciplinary research will not only provide various training projects to undergraduate and graduate students, but also inspire students (particularly PhD students) to pursue high-quality research with a creative, open-minded, and cross-disciplinary perspective. On the industry front, the capacity planning and dynamic network control techniques developed in this project are of critical importance to practical wireless network design, and have the potential to be transformed into practical network protocols with low complexity and provable efficiency.
