DRL-Based Anti-Jamming Optimization Aided by RIS in Dynamic Electromagnetic Environments

In complex electromagnetic environments, tactical wireless communication links face severe jamming threats, which can easily lead to communication disruption and adversely affect the stability and reliability of mission execution. To improve the anti-jamming capability of wireless communication systems in dynamic interference scenarios, this paper proposes an adaptive anti-jamming architecture that integrates reconfigurable intelligent surface (RIS) with deep reinforcement learning (DRL). The proposed architecture improves the robustness of the communication link and the intelligence of decision-making from two dimensions: enhancing the strength of useful signals and generating dynamic anti-jamming strategies. In terms of methodology, the system first leverages the beamforming capability of RIS to actively manipulate the wireless propagation environment, thereby improving the channel signal-to-noise ratio, effectively suppressing the interference, and accelerating the convergence of learning strategies. Next, frequency selection and power control are modeled as a Markov decision process. A greedy action selection strategy incorporating historical value estimation is introduced, thus forming a reinforcement learning framework based on double deep Q-networks with prioritized experience replay. The RIS-enhanced signal improves the stability of the learning strategy and significantly shortens the training period. Simulation results demonstrate that, in such typical jamming scenarios as wideband frequency sweeping, random pulse jamming and intelligent adversarial games, the proposed architecture achieves a certain degree of improvement in average communication success rate, as compared with the solutions that rely solely on deep reinforcement learning or RIS. These results validate the strong robustness and broad adaptability of the proposed architecture in highly dynamic environments.