Deep Q-Network Based Resilient Drone Communication:Neutralizing First-Order Markov Jammers
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 4 references that are in the map. First posted on arXiv 1 Jan 2026.
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