Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications
Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7328 |
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| author | Zhifang Xing Yunhui Qin Changhao Du Wenzhang Wang Zhongshan Zhang |
| author_facet | Zhifang Xing Yunhui Qin Changhao Du Wenzhang Wang Zhongshan Zhang |
| author_sort | Zhifang Xing |
| collection | DOAJ |
| description | Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. |
| format | Article |
| id | doaj-art-986bc4da35184f5fa1038af7bcb4da3c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-986bc4da35184f5fa1038af7bcb4da3c2024-11-26T18:21:33ZengMDPI AGSensors1424-82202024-11-012422732810.3390/s24227328Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle CommunicationsZhifang Xing0Yunhui Qin1Changhao Du2Wenzhang Wang3Zhongshan Zhang4School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, ChinaNational School of Elite Engineering, University of Science and Technology Beijing, Beijing 100081, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaDespite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively.https://www.mdpi.com/1424-8220/24/22/7328unmanned aerial vehicle (UAV)jamming UAVdeep reinforcement learningsequential decision problem |
| spellingShingle | Zhifang Xing Yunhui Qin Changhao Du Wenzhang Wang Zhongshan Zhang Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications Sensors unmanned aerial vehicle (UAV) jamming UAV deep reinforcement learning sequential decision problem |
| title | Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications |
| title_full | Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications |
| title_fullStr | Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications |
| title_full_unstemmed | Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications |
| title_short | Deep Reinforcement Learning-Driven Jamming-Enhanced Secure Unmanned Aerial Vehicle Communications |
| title_sort | deep reinforcement learning driven jamming enhanced secure unmanned aerial vehicle communications |
| topic | unmanned aerial vehicle (UAV) jamming UAV deep reinforcement learning sequential decision problem |
| url | https://www.mdpi.com/1424-8220/24/22/7328 |
| work_keys_str_mv | AT zhifangxing deepreinforcementlearningdrivenjammingenhancedsecureunmannedaerialvehiclecommunications AT yunhuiqin deepreinforcementlearningdrivenjammingenhancedsecureunmannedaerialvehiclecommunications AT changhaodu deepreinforcementlearningdrivenjammingenhancedsecureunmannedaerialvehiclecommunications AT wenzhangwang deepreinforcementlearningdrivenjammingenhancedsecureunmannedaerialvehiclecommunications AT zhongshanzhang deepreinforcementlearningdrivenjammingenhancedsecureunmannedaerialvehiclecommunications |