Acceleration Improvement of a Sigmoid Power Control Game Algorithm in Cognitive Radio Networks

In cognitive radio networks, each cognitive radio CR's signal represent a source of interference to other users that sharing the same spectrum. Amount of interference that should be below the interference temperature and battery power of cognitive devices are the critical issues that require an efficient power control algorithms. These algorithms aimed to attain two objectives: achieve the quality of service (QoS) and increase the system capacity. The power control problem in CRN is obviously suitable to formulation as a non-cooperative game in which CRs choose to the balance between signal-to interference ratio (SIR) error and power usage. We considered perversely the problem of power control by using the static Nash game formulation based on a sigmoid function. The solution obtained from proposed game led to a system of nonlinear algebraic sigmoid equations. In this paper, we present the distributed power control game using Newton iterations to solve the slow of convergence problem. The effectiveness result of the new improved algorithm is demonstrated in simulation on a small and pragmatic cognitive radio system. The results indicates that the new development algorithm based on Newton iteration can reduce the number of iterations up to 58% comparing with traditional fixed point algorithm.