A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks
A cognitive radio network (CRN) is a novel solution that promises to solve the spectrum scarcity problem and enhance spectrum utilization. However, unsecured CRN can easily be manipulated in order to attack legacy users on the communication channel. As a result, the network’s performance...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9383211/ |
| _version_ | 1819078723002957824 |
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| author | Huynh Thanh Thien Van-Hiep Vu Insoo Koo |
| author_facet | Huynh Thanh Thien Van-Hiep Vu Insoo Koo |
| author_sort | Huynh Thanh Thien |
| collection | DOAJ |
| description | A cognitive radio network (CRN) is a novel solution that promises to solve the spectrum scarcity problem and enhance spectrum utilization. However, unsecured CRN can easily be manipulated in order to attack legacy users on the communication channel. As a result, the network’s performance significantly degrades. Therefore, communication channel security is an important issue that needs to be addressed in a CRN. In this work, we focus on improving the security of multi-channel communication in a CRN, while various jammers try to access channels of interest to prevent SUs from using them. By using game-theoretic concepts and by defining states, actions, and players’ rewards, we propose game–based schemes that find the best channel for the secondary users (SUs) in order to avoid jammer’s attacks on communication channels. Accordingly, the problem is finding the optimal channel to maximize the long-term reward of the SU where communication channels are not used by the primary users (PUs) and are not jammed by attackers. In addition, the idea of transfer learning might be applied to the problem under consideration, and thus, a transfer Game-Actor-Critic (TGACT) scheme is proposed, which uses the transferred knowledge in a double-game period to accelerate the learning process and provide performance improvement in channel selection. Finally, the performance of the proposed schemes is simulated with different configurations. The simulation results show that the proposed schemes are quite resistant to jammer attacks, and achieve better performance compared to other channel selection schemes. |
| first_indexed | 2024-12-21T19:17:37Z |
| format | Article |
| id | doaj.art-8710c9fb41e24f1b8282c190dba4b603 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T19:17:37Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8710c9fb41e24f1b8282c190dba4b6032022-12-21T18:53:02ZengIEEEIEEE Access2169-35362021-01-019478874790010.1109/ACCESS.2021.30681299383211A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio NetworksHuynh Thanh Thien0https://orcid.org/0000-0001-5217-2144Van-Hiep Vu1https://orcid.org/0000-0001-5581-4376Insoo Koo2https://orcid.org/0000-0001-7476-8782Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaNTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, VietnamDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South KoreaA cognitive radio network (CRN) is a novel solution that promises to solve the spectrum scarcity problem and enhance spectrum utilization. However, unsecured CRN can easily be manipulated in order to attack legacy users on the communication channel. As a result, the network’s performance significantly degrades. Therefore, communication channel security is an important issue that needs to be addressed in a CRN. In this work, we focus on improving the security of multi-channel communication in a CRN, while various jammers try to access channels of interest to prevent SUs from using them. By using game-theoretic concepts and by defining states, actions, and players’ rewards, we propose game–based schemes that find the best channel for the secondary users (SUs) in order to avoid jammer’s attacks on communication channels. Accordingly, the problem is finding the optimal channel to maximize the long-term reward of the SU where communication channels are not used by the primary users (PUs) and are not jammed by attackers. In addition, the idea of transfer learning might be applied to the problem under consideration, and thus, a transfer Game-Actor-Critic (TGACT) scheme is proposed, which uses the transferred knowledge in a double-game period to accelerate the learning process and provide performance improvement in channel selection. Finally, the performance of the proposed schemes is simulated with different configurations. The simulation results show that the proposed schemes are quite resistant to jammer attacks, and achieve better performance compared to other channel selection schemes.https://ieeexplore.ieee.org/document/9383211/Actor–criticcognitive radio networksgame theoryjammerreinforcement learningtransfer learning |
| spellingShingle | Huynh Thanh Thien Van-Hiep Vu Insoo Koo A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks IEEE Access Actor–critic cognitive radio networks game theory jammer reinforcement learning transfer learning |
| title | A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks |
| title_full | A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks |
| title_fullStr | A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks |
| title_full_unstemmed | A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks |
| title_short | A Transfer Games Actor–Critic Learning Framework for Anti-Jamming in Multi-Channel Cognitive Radio Networks |
| title_sort | transfer games actor x2013 critic learning framework for anti jamming in multi channel cognitive radio networks |
| topic | Actor–critic cognitive radio networks game theory jammer reinforcement learning transfer learning |
| url | https://ieeexplore.ieee.org/document/9383211/ |
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