用深度Q网络对抗会预测的跳频干扰，提升无人机通信抗扰能力。
Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers
- 基于DQN的发射端自适应选择跳频信道，应对可预测干扰。
- 在16信道环境中实现近最优抗扰策略，包丢失率显著降低。
- 适合研究智能电子战、抗干扰通信的工程师与研究人员。
针对16信道无线环境中的频率跳变扩频通信，提出一种基于深度强化学习的抗干扰方案。深度Q网络（DQN）驱动的发射端持续选择下一跳频信道，以应对一阶马尔可夫型主动干扰，该干扰利用观测到的转移统计量预测并中断传输。通过自训练，所提智能体学习到均匀随机跳频策略，有效消除干扰者的预测优势。在瑞利衰落和加性噪声环境下，系统评估了前向纠错编码（BCH码）的影响，表明适度冗余可显著降低包丢失率。通过大量可视化分析学习动态、信道利用率分布、ε-贪婪衰减、累计奖励、误码率（BER）与信噪比（SNR）演化及详细包丢失表格，验证了算法收敛至接近最优的抗扰策略。结果为现代电子战场景下的自主弹性通信提供了实用框架。
原文摘要 · Abstract (English)
Deep Reinforcement Learning based solution for jamming communications using Frequency Hopping Spread Spectrum technology in a 16 channel radio environment is presented. Deep Q Network based transmitter continuously selects the next frequency hopping channel while facing first order reactive jamming, which uses observed transition statistics to predict and interrupt transmissions. Through self training, the proposed agent learns a uniform random frequency hopping policy that effectively neutralizes the predictive advantage of the jamming. In the presence of Rayleigh fading and additive noise, the impact of forward error correction Bose Chaudhuri Hocquenghem type codes is systematically evaluated, demonstrating that even moderate redundancy significantly reduces packet loss. Extensive visualization of the learning dynamics, channel utilization distribution, epsilon greedy decay, cumulative reward, BER and SNR evolution, and detailed packet loss tables confirms convergence to a near optimal jamming strategy. The results provide a practical framework for autonomous resilient communications in modern electronic warfare scenarios.
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