Optimizing flow control in multi-interface wireless cognitive radio networks

This paper solves the optimal flow management problem in cognitive radio (CR) networks with multiple self-interested CRs and multiple self interested servers with multiple radio interfaces. We formulate a novel flow management problem in which the cognitive nodes compete to minimize the total delay over all interfaces while at the same time the servers compete to maximize their individual profit. We model this problem as a leader-follower game and propose a dynamic linear pricing scheme designed to achieve optimal flow allocation. We propose an iterative algorithm to solve the game and analyze the criteria for convergence to the unique Nash Equilibrium. The messaging required to implement this algorithm is minimal thereby making it suitable for distributed implementations. Numerical simulations demonstrate significant improvement in terms of average total delay for cognitive nodes in comparison with alternative algorithms. Simulation results show that when quality of service in terms of average total delay is fixed, our algorithm improves the capacity of the network by 40% for the maximum allowable throughput demand.