Research Article
Joint Power and Channel Selection for Anti-jamming Communications: A Reinforcement Learning Approach
@INPROCEEDINGS{10.1007/978-3-030-32388-2_47, author={Xufang Pei and Ximing Wang and Lang Ruan and Luying Huang and Xingyue Yu and Heyu Luan}, title={Joint Power and Channel Selection for Anti-jamming Communications: A Reinforcement Learning Approach}, proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings}, proceedings_a={MLICOM}, year={2019}, month={10}, keywords={Multi-domain anti-jamming Markov decision process (MDP) Reinforcement learning}, doi={10.1007/978-3-030-32388-2_47} }- Xufang Pei
Ximing Wang
Lang Ruan
Luying Huang
Xingyue Yu
Heyu Luan
Year: 2019
Joint Power and Channel Selection for Anti-jamming Communications: A Reinforcement Learning Approach
MLICOM
Springer
DOI: 10.1007/978-3-030-32388-2_47
Abstract
In this paper, the decision-making problem for anti-jamming communications is studied. Most of the existing anti-jamming researches mainly focus on the single-domain anti-jamming such as power domain or frequency domain, which has limited performance facing strong jamming. Therefore, to effectively deal with some jamming attack, this paper proposes a multi-domain joint anti-jamming scheme, and considers the power domain and the frequency domain jointly. By modeling the anti-jamming process as a Markov decision process (MDP), reinforcement learning (RL) is adopted to solve the MDP. Then, the multi-domain joint anti-jamming algorithm is proposed to find the optimal decision-making strategy. Moreover, the proposed algorithm is verified to converge to an effective strategy. Simulation results show that the proposed algorithm has better throughput performance than the sensing-based random selection algorithm.
