Distributed Cross-Layer Optimization for Cognitive Radio Networks

This paper presents a distributed cross-layer optimization algorithm for a multihop cognitive radio network, with the objective of maximizing data rates for a set of user communication sessions. We study this problem with joint consideration of power control, scheduling, and routing. Even under a centralized approach, such a problem has a mixed-integer nonlinear program formulation and is likely NP-hard. Thus, a distributed problem is very challenging. The main contribution of this paper is the development of a distributed optimization algorithm that iteratively increases data rates for user communication sessions. During each iteration, our algorithm has routing, minimalist scheduling, and power control/scheduling modules for improving the current solution at all three layers. To evaluate the performance of the distributed optimization algorithm, we compare it with an upper bound of the objective function. Results show that the distributed optimization algorithm can achieve a performance close to this upper bound. Because the optimal solution (unknown) is between the upper bound and the solution obtained by our distributed algorithm, we conclude that the results obtained by our distributed algorithm are highly competitive.