• 理论与算法 •

### 融合改进强化学习的认知无线电抗干扰决策算法

1. 天津大学 微电子学院，天津 300072
• 出版日期:2019-04-01 发布日期:2019-04-10

### Cognitive Radio Anti-Jamming Decision Algorithm Based on Improved Reinforcement Learning

ZHU Rui, MA Yongtao+, NAN Yafei, ZHANG Yunlei

1. School of Microelectronics, Tianjin University, Tianjin 300072, China
• Online:2019-04-01 Published:2019-04-10

Abstract: For the problem that the cognitive users are easily jammed in cognitive radio systems, this paper investigates a novel algorithm based on the interaction between a cognitive user with frequency hopping and a smart jammer. To fully utilize the limited spectrum resources, comprehensively considering channel selection and power allocation, this paper designs the utility function of the spectral energy efficiency of the cognitive users as the reference standard and integrates the improved reinforcement learning algorithm into the cognitive decision engine. By transforming the environment and agent model of reinforcement learning into the interaction between the cognitive environment and the decision engine, the decision algorithm explores the maximum return of action feedback to the cognitive user and finally obtains the adaptive optimization strategy. Simulation results show that the proposed algorithm can converge faster than the traditional one and the adaptive strategy can effectively improve the secondary user??s performance against smart jammers which is more than 50% higher than the random strategy.