Cooperative Sensing and Allocation Algorithm of Cognitive Radio Spectrum Based on Artificial Intelligence

Abstract

In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.