Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems
Increasing the hopping frequency speed and integrating artificial intelligence (AI) technologies are currently two of the most effective strategies for enhancing the anti-jamming performance of frequency hopping (FH) systems. However, due to the complexity of the decision-making process in intellige...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10843343/ |
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| author | Sixi Cheng Xiang Ling Lidong Zhu |
| author_facet | Sixi Cheng Xiang Ling Lidong Zhu |
| author_sort | Sixi Cheng |
| collection | DOAJ |
| description | Increasing the hopping frequency speed and integrating artificial intelligence (AI) technologies are currently two of the most effective strategies for enhancing the anti-jamming performance of frequency hopping (FH) systems. However, due to the complexity of the decision-making process in intelligent agents, the system cannot complete decisions within the intervals between hops in fast frequency hopping (FFH) systems. As a result, there is no existing strategy for directly applying AI technologies to FFH systems. In this work, we introduce the concept of the available frequency set (AFS) and apply deep reinforcement learning (DRL) methods to FFH systems, enabling them to retain their inherent advantages while also gaining adaptability to dynamic environments. Building on this, we propose an improved multi-action deep recurrent Q-network (MA-DRQN) algorithm to determine the AFS for hopping sequence generation. Finally, the proposed method is shown to outperform both traditional FFH systems and advanced intelligent FH systems in handling passive and active jammers. Moreover, the hopping sequences generated based on AFS exhibit strong unpredictability. |
| format | Article |
| id | doaj-art-e2882d33c3df45af9b3f3fa52c1a36ff |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-e2882d33c3df45af9b3f3fa52c1a36ff2025-02-07T00:01:59ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-01696197110.1109/OJCOMS.2025.352998210843343Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping SystemsSixi Cheng0https://orcid.org/0000-0002-5424-8293Xiang Ling1https://orcid.org/0000-0002-2012-8795Lidong Zhu2https://orcid.org/0000-0002-3737-9435National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, ChinaNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, ChinaIncreasing the hopping frequency speed and integrating artificial intelligence (AI) technologies are currently two of the most effective strategies for enhancing the anti-jamming performance of frequency hopping (FH) systems. However, due to the complexity of the decision-making process in intelligent agents, the system cannot complete decisions within the intervals between hops in fast frequency hopping (FFH) systems. As a result, there is no existing strategy for directly applying AI technologies to FFH systems. In this work, we introduce the concept of the available frequency set (AFS) and apply deep reinforcement learning (DRL) methods to FFH systems, enabling them to retain their inherent advantages while also gaining adaptability to dynamic environments. Building on this, we propose an improved multi-action deep recurrent Q-network (MA-DRQN) algorithm to determine the AFS for hopping sequence generation. Finally, the proposed method is shown to outperform both traditional FFH systems and advanced intelligent FH systems in handling passive and active jammers. Moreover, the hopping sequences generated based on AFS exhibit strong unpredictability.https://ieeexplore.ieee.org/document/10843343/Anti-jammingfast frequency hoppingsecure communication systemdeep reinforcement learning |
| spellingShingle | Sixi Cheng Xiang Ling Lidong Zhu Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems IEEE Open Journal of the Communications Society Anti-jamming fast frequency hopping secure communication system deep reinforcement learning |
| title | Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems |
| title_full | Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems |
| title_fullStr | Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems |
| title_full_unstemmed | Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems |
| title_short | Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems |
| title_sort | deep reinforcement learning based anti jamming approach for fast frequency hopping systems |
| topic | Anti-jamming fast frequency hopping secure communication system deep reinforcement learning |
| url | https://ieeexplore.ieee.org/document/10843343/ |
| work_keys_str_mv | AT sixicheng deepreinforcementlearningbasedantijammingapproachforfastfrequencyhoppingsystems AT xiangling deepreinforcementlearningbasedantijammingapproachforfastfrequencyhoppingsystems AT lidongzhu deepreinforcementlearningbasedantijammingapproachforfastfrequencyhoppingsystems |