Joint Spectrum Allocation and Power Control for Cognitive Radio Networks Based on Potential Game

Abstract

To solve the spectrum and power allocation problem of distributed cognitive radio networks for D2D users, we propose a joint spectrum and power allocation algorithm based on potential game. Through all the D2D nodes uploading their own strategy selections, the model enables distributed nodes to make decisions using limited information, and establishes a resource allocation model that maximizes the overall benefit. The utility function takes the throughput as the gain, and the total mutual interference as the cost. Cognitive users detect the channel condition according to the interference criterion of SINR constraint, in order to guarantee that the node can select the strategy which meets the user's requirements and achieves Nash equilibrium quickly. In this paper, the convergence of the algorithm is proved theoretically. Simulation results show that the algorithm achieves the goal of joint allocation of spectrum and power with a fast convergence speed, and demonstrate superior throughput and fair allocation of resources in comparison with other non game-theoretic methods.