用强化学习对抗动态干扰，自动优化通信参数
How to Combat Reactive and Dynamic Jamming Attacks with Reinforcement Learning
- 通过强化学习动态调整功率、调制和信道选择
- 在干扰策略变化时仍保持高吞吐率，收敛速度快
- 适合无线通信系统抗干扰设计与智能频谱管理
本文研究反应式干扰问题，即干扰者采用动态策略选择信道和感知阈值以检测并干扰传输。收发双方利用强化学习（RL）在无先验知识的情况下，通过自适应调整发射功率、调制方式和信道选择，实现对干扰的规避并优化吞吐量。针对离散干扰状态使用Q-learning，针对基于接收功率的连续状态采用深度Q网络（DQN）。通过不同奖励函数与动作集设计，实验表明，该方法能快速适应频谱动态变化，在信道与干扰策略演进过程中维持高传输速率。
原文摘要 · Abstract (English)
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.
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