Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks
The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slot...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/807 |
| _version_ | 1797437328086532096 |
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| author | Ruiquan Lin Hangding Qiu Weibin Jiang Zhenglong Jiang Zhili Li Jun Wang |
| author_facet | Ruiquan Lin Hangding Qiu Weibin Jiang Zhenglong Jiang Zhili Li Jun Wang |
| author_sort | Ruiquan Lin |
| collection | DOAJ |
| description | The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps. |
| first_indexed | 2024-03-09T11:17:40Z |
| format | Article |
| id | doaj.art-cbd57048e0684ea6ac9df65df22adc2a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:40Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cbd57048e0684ea6ac9df65df22adc2a2023-12-01T00:27:57ZengMDPI AGSensors1424-82202023-01-0123280710.3390/s23020807Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio NetworksRuiquan Lin0Hangding Qiu1Weibin Jiang2Zhenglong Jiang3Zhili Li4Jun Wang5College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaCollege of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, SingaporeCollege of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, ChinaThe paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.https://www.mdpi.com/1424-8220/23/2/807cognitive radio networkenergy harvestingphysical layer securitydeep reinforcement learning |
| spellingShingle | Ruiquan Lin Hangding Qiu Weibin Jiang Zhenglong Jiang Zhili Li Jun Wang Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks Sensors cognitive radio network energy harvesting physical layer security deep reinforcement learning |
| title | Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks |
| title_full | Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks |
| title_fullStr | Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks |
| title_full_unstemmed | Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks |
| title_short | Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks |
| title_sort | deep reinforcement learning for physical layer security enhancement in energy harvesting based cognitive radio networks |
| topic | cognitive radio network energy harvesting physical layer security deep reinforcement learning |
| url | https://www.mdpi.com/1424-8220/23/2/807 |
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