南京邮电大学学报（自然科学版）2024，Vol.44Issue(6)：1-11,11.DOI:10.14132/j.cnki.1673-5439.2024.06.001
基于深度强化学习的动态频谱智能干扰算法研究
A dynamic spectrum intelligent jamming algorithm based on deep reinforcement learning
摘要
Abstract
With the advancement of artificial intelligence technology,reinforcement learning has shown great potential in enhancing electromagnetic spectrum control and jamming efficiency.Given the robust anti-jamming capability of frequency-hopping communication systems and the inadequacy of traditional jamming methods,this paper intends to utilize deep reinforcement learning(DRL)for intelligent electromagnetic jamming in dynamic spectrum environments.First,a partially observable Markov decision process(POMDP)is introduced to model the communication counteraction process between jammers and frequency-hopping communication users.Second,a jamming decision network capable of mining spectrum features and performing memory backtracking is designed,based on convolutional neural networks(CNNs)and long short-term memory networks(LSTMs).This network implements a dynamic spectrum intelligent jamming(DSIJ)algorithm grounded in deep reinforcement learning.Simulation results indicate that compared to the traditional deep Q network(DQN)algorithm,the proposed DSIJ algorithm increases the jamming success rate by approximately 18％;compared to traditional sweeping jamming methods,the success rate is further increased by about 68％.These demonstrate that the proposed algorithm holds effectiveness and significant advantages in implementing intelligent jamming strategies in dynamic spectrum environments.关键词
深度强化学习/跳频通信/智能干扰决策/部分可观测马尔可夫决策过程Key words
deep reinforcement learning(DRL)/frequency-hopping communication/intelligent jamming decision/partially observable Markov decision processes(POMDP)分类
信息技术与安全科学引用本文复制引用
张兰,张彪,梁天一,朱辉杰..基于深度强化学习的动态频谱智能干扰算法研究[J].南京邮电大学学报（自然科学版）,2024,44(6):1-11,11.基金项目
国家自然科学基金(61772287)资助项目 （61772287）
