Cross-layer design problems in wireless networks are usually very complex, since they require simultaneously optimizing a large amount of algorithms and parameters. Most existing solutions for cross-layer optimization rely on heuristic procedures to solve this problem. However, to obtain an optimal utility for the wireless user, cross-layer optimization should be formulated rigorously as a sequential decision problem that takes into account the capability of the various layers to autonomously make forecasts about their experienced dynamics, and perform foresighted adaptation, while adhering to the existing layered network architecture. To address this challenge, the investigators study a new, systematic framework for cross-layer optimization that allows each layer to make autonomous and foresighted decisions on the selected transmission strategies (e.g. protocol parameters and algorithms), while cooperatively maximizing the utility of the wireless user by optimally determining what necessary information should be exchanged among layers.
This research involves two main thrusts: (a) Develop a novel cross-layer optimization framework with message exchange among layers in which each layer optimizes its own protocol parameters and algorithms based on its own experienced dynamics and the information exchanged with other layers, in order to cooperatively maximize the wireless user?s utility in a foresighted manner while adhering to the layered network architecture; (b) Design layered on-line learning algorithms for the cross-layer optimization to allow each layer to interactively learn the experienced dynamics and other necessary information from other layers, such that the cross-layer strategies can be optimized cooperatively. This research leads to a fundamentally new way for designing wireless networks, systems and applications, where devices evolve and become smarter, by learning from their interactions with the environment over time.
