Adaptive Multi-Mode DRL Framework for Intelligent RIS-Assisted Anti-Jamming in 6G Wireless Networks

Abstract

This paper proposes an adaptive multi-mode Deep Reinforcement Learning (DRL) framework for intelligent RIS assisted anti-jamming communication in dynamic 6G wireless networks. The proposed Framework jointly integrates RIS beamforming, channel hopping, and transmit power adaptation through a DRL-Driven decision engine capable of dynamically responding to varying interference conditions and channel fluc tuations. To improve deployment realism, practical constraints including imperfect Channel State Information (CSI), finite resolution RIS phase quantization, reflection loss, control delay, and user mobility are incorporated into the system model. The anti-jamming problem is formulated as a Markov decision process and solved using DQN, PPO, and SAC algorithms. Ex tensive simulations are conducted using MATLAB-based wireless channel modeling and Python-based DRL training platforms. Simulation results demonstrate that the proposed framework achieves approximately 25%–40% higher throughput and 18% 35% SINR improvement compared with conventional anti jamming approaches. Moreover, the proposed scheme maintains stable communication performance under strong jamming power, CSI uncertainty, and high-mobility scenarios. Statistical evalu ations over 20 independent random seeds further confirm the robustness and reproducibility of the proposed framework.

Keywords