Journal of Theoretical Physics & Mathematics Research
ISSN: 3065-8802 | DOI: 10.64030/3065-8802
Andrii Grekhov and Vasyl Kondratiuk
Deep Reinforcement Learning (DRL)-based solution for jamming communications using Frequency-Hopping Spread Spectrum (FHSS) technology in a 16-channel radio environment is presented. Deep Q-Network (DQN)-based UAV 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 (BCH)-type codes (with t = 0–10 correctable errors) is systematically evaluated, demonstrating that even moderate redundancy (t = 1–2) significantly reduces packet loss. Extensive visualization of the learning dynamics, channel utilization distribution, ε-greedy decay, cumulative reward, BER/ 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.