Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach
Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement...
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8928502/ |
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doaj-fcdd42587e11436d8ecab761526256752021-03-30T00:35:59ZengIEEEIEEE Access2169-35362019-01-01718053218054310.1109/ACCESS.2019.29583288928502Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning ApproachJinlin Peng0https://orcid.org/0000-0002-1944-888XZixuan Zhang1https://orcid.org/0000-0002-1782-4108Qinhao Wu2https://orcid.org/0000-0003-3039-1455Bo Zhang3https://orcid.org/0000-0001-5183-9867Artificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, ChinaCollege of Computer, National University of Defense Technology, Changsha, ChinaCollege of Electronic Science, National University of Defense Technology, Changsha, ChinaArtificial Intelligence Research Center, National Innovation Institute of Defense Technology, Beijing, ChinaIntelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm.https://ieeexplore.ieee.org/document/8928502/Intelligent UAV swarmanti-jamming communicationmulti-parameter joint programmingantenna patternmotion cost |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Jinlin Peng Zixuan Zhang Qinhao Wu Bo Zhang |
| spellingShingle |
Jinlin Peng Zixuan Zhang Qinhao Wu Bo Zhang Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach IEEE Access Intelligent UAV swarm anti-jamming communication multi-parameter joint programming antenna pattern motion cost |
| author_facet |
Jinlin Peng Zixuan Zhang Qinhao Wu Bo Zhang |
| author_sort |
Jinlin Peng |
| title |
Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach |
| title_short |
Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach |
| title_full |
Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach |
| title_fullStr |
Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach |
| title_full_unstemmed |
Anti-Jamming Communications in UAV Swarms: A Reinforcement Learning Approach |
| title_sort |
anti-jamming communications in uav swarms: a reinforcement learning approach |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2019-01-01 |
| description |
Intelligent unmanned aerial vehicle (UAV) swarm may accomplish complex tasks through cooperation, relying on inter-UAV communications. This paper aims to improve the communication performance of intelligent UAV swarm system in the presence of jamming, by multi-parameter programming and reinforcement learning. This paper considers a communication system, where the communication between a UAV swarm and the base station is jammed by multiple interferers. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, motion and antenna spatial domain to optimize the communication quality in the receiving area. This paper proposes a modified Q-Learning algorithm based on multi-parameter programming, where a cost is introduced to strike a balance between the motion and communication performance of the UAVs. The simulation results show the effectiveness of the algorithm. |
| topic |
Intelligent UAV swarm anti-jamming communication multi-parameter joint programming antenna pattern motion cost |
| url |
https://ieeexplore.ieee.org/document/8928502/ |
| work_keys_str_mv |
AT jinlinpeng antijammingcommunicationsinuavswarmsareinforcementlearningapproach AT zixuanzhang antijammingcommunicationsinuavswarmsareinforcementlearningapproach AT qinhaowu antijammingcommunicationsinuavswarmsareinforcementlearningapproach AT bozhang antijammingcommunicationsinuavswarmsareinforcementlearningapproach |
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