How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912623744778240 |
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| author | Sagduyu, Yalin E. Erpek, Tugba Davaslioglu, Kemal Kompella, Sastry |
| author_facet | Sagduyu, Yalin E. Erpek, Tugba Davaslioglu, Kemal Kompella, Sastry |
| contents | This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power. Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning Sagduyu, Yalin E. Erpek, Tugba Davaslioglu, Kemal Kompella, Sastry Machine Learning Artificial Intelligence Networking and Internet Architecture Signal Processing This paper studies the problem of mitigating reactive jamming, where a jammer adopts a dynamic policy of selecting channels and sensing thresholds to detect and jam ongoing transmissions. The transmitter-receiver pair learns to avoid jamming and optimize throughput over time (without prior knowledge of channel conditions or jamming strategies) by using reinforcement learning (RL) to adapt transmit power, modulation, and channel selection. Q-learning is employed for discrete jamming-event states, while Deep Q-Networks (DQN) are employed for continuous states based on received power. Through different reward functions and action sets, the results show that RL can adapt rapidly to spectrum dynamics and sustain high rates as channels and jamming policies change over time. |
| title | How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning |
| topic | Machine Learning Artificial Intelligence Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2510.02265 |