Dynamic Resource Allocation Algorithms for Cognitive Radio Networks
Cognitive radio networks are fundamentally different from traditional cellular networks because multiple competing service providers simultaneously co‐exist in the same wireless spectrum. Service providers can significantly increase the network capacity by deploying dynamic resource allocation algorithms that are robust to time‐varying and uncertain environments as well as the actions of other, possibly selfish entities. The proposed project will develop provably good algorithms for user selection as well as power and rate allocation that can be implemented by service providers in the next generation cognitive radio networks.
The proposed project provides a first hand experience on understanding the underlying mathematical theories behind the success of wireless communication systems. In particular, concepts from multi‐user information theory will be used to develop the performance metrics to be optimized, whereas concepts from game theory will be used to model selfishness of competing service providers. Additionally techniques for convergence analysis of iterative algorithms will also be investigated. The state of the art literature is far from providing a complete understanding of good resource allocation algorithms. The proposed project will examine an important generalization of these works, where each transmitter intends to either broadcast or unicast multiple information streams to several recipients. The proposed project will study iterative and distributed joint user selection and power allocation policies that achieve the equilibrium points of this hybrid game.
The student will be involved in all aspects of this project, including literature review, problem formulation, and algorithm development and analysis. He/she will learn the theoretical modeling and analysis of wireless networks and design practical engineering solutions based on these insights
