Power and Frequency Selection optimization in Anti-Jamming Communication: A Deep Reinforcement Learning Approach
Yangyang Li, Yitao Xu, Ximing Wang, Wen Li, Wei Bai · 2019
This paper studies the problem of anti-jamming in selecting communication frequency and power by using deep reinforcement learning (DRL) algorithm. In existing works, the anti-jamming methods are considered in frequency domain or power domain only. With the development of communication devices, more and more communication devices have the ability to switch communication frequency and adjust power simultaneously. To be specifically, the anti-jamming problem can be considered in both frequency and power domains. In this paper, we formulate the anti-jamming problem as a Markov decision process (MDP). And we use DRL algorithm to cope with the problem. The results of simulation show that the proposed algorithm achieves high throughput and with lower power.