Research on Cooperative Spectrum Detection Based on Evolutionary Game Theory under Noise Uncertainty

In cognitive radio, cooperative spectrum detection has been shown to improve the detection performance of the system. But in the case of noise uncertainty, the cognitive user will be more inclined to reduce the detection of consumption to improve their throughput. In this paper, evolutionary game theory is applied to cooperative spectrum detection under noise uncertainty. Cognitive users can dynamically select whether to participate in cooperative spectrum detection. The system will achieve an evolutionary stabilization strategy (ESS) through continuous iterative learning. In this paper, all cognitive users participate in the game as a whole, and the evolutionary game theory algorithm is used to get the average throughput of the cognitive users participating in the collaboration and compare it with the average throughput of all users. Such as the same iteration until it is equal, so as to get the final evolutionary stability strategy. The simulation results show that the higher the noise uncertainty is, the higher the probability of cognition users participating in cooperative detection. Evolutionary game theory algorithm allows a user to selectively participate in the collaborative cognitive detect than to allow all users to collaborate on detection of a higher system throughput.