A Novel Price-Based Algorithm for Spectrum Sharing in Cognitive Radio Networks

Cognitive radio network is expected to use flexible radio frequency spectrum sharing techniques to achieve more efficient frequency spectrum usage. This article considers the spectrum sharing problem that one primary user (PU) can share its frequency spectrum by renting this spectrum to multiple secondary users (SUs). The pricing scheme is a key issue for spectrum sharing in cognitive radio network. We first propose a nonlinear one leader multiple followers (NLMF) sharing spectrum scheme as a multi-object optimization problem; the prices are offered by PU to SUs at the same time. This problem can be solved by using particle swarm optimization (PSO); SUs gradually and iteratively adjust their strategies respectively based on the observations on their opponents´ previous strategies until Nash equilibrium is completed. We then present a general nonlinear bilevel one leader multiple followers (NBMF) optimization problem to further consider the revenue of the PU and a new optimal strategic pricing optimization technique which applies bilevel programming and swarm intelligence. A leader-follower game is formulated to obtain the stackelberg-nash equilibrium for spectrum sharing that considers not only revenue of a PU but also the SUs utility. The behaviors of the pricing model have been evaluated; and the performance results show that the proposed algorithm is well-performed to solve the spectrum sharing in a cognitive radio network.