Research On Cognitive Wireless Network Power Allocation Algorithm Based On Game Theory

Cognitive wireless network dynamic spectrum allocation technology is one of the hotspots in the field of wireless communication research.Based on the relevant theory and key technologies of cognitive wireless networks,this paper uses game theory to implement cognitive wireless network dynamic spectrum allocation technology.Research,the main work results are as follows:Firstly,For the MIMO cognitive wireless network power control problem,the secondary user(SU)can access the spectrum belonging to the primary user(PU)only when the interference to the user terminal is less than a certain threshold.It is assumed that all network nodes are equipped with multiple antennas.Using game theory method and original decomposition method,a low-complexity distributed algorithm based on Nash bargaining solution is proposed for spectrum sharing and power allocation in MIMO cognitive radio networks.The algorithm uses two iterative algorithms Jacobi and Gauss-Seidel to conFig,and its performance is studied through simulation.The convergence of this algorithm is further proved by analysis.The simulation results show that the algorithm not only achieves the sharing of authorized spectrum resources between primary and secondary users,but also effectively improves the efficiency of spectrum usage.Secondly,Focusing on Nash's bargaining cooperative game theory,it mainly studies power control problems in cognitive wireless networks.A utility function based on channel-to-interference ratio(SINR)is designed,and a distributed power control algorithm based on Nash bargaining solution(NBS)is proposed to ensure the Pareto optimality between the system and cognitive users.The Lagrangian relaxation technique is used to obtain the optimal transmission strategy for each cognitive user.Combined with the fixed-point iterative technique,the complexity of the power control problem is greatly reduced.Simulation results show that the proposed algorithm is simple to implement and has a fast convergence speed.It is of great goodness and effectively improves the fairness among cognitive users and the overall performance of the system.