Combat Intelligent Jammer with Intelligence: DRL Enhanced Random Access for SAGIN
Hongyuan Wang, Qiaolin Ouyang, Jianxiong Pan, Xi Wang, Peng Zhang, Neng Ye · 2024
Space-air-ground integrated network presents a promising solution to the challenge of accommodating large-scale device access while confronting sophisticated interference threats. Existing random access techniques neglect the dynamic interference environment and thus often struggle to realize anti-intelligent interference effectively. This paper proposes a novel approach to address this issue. By employing deep reinforcement learning algorithms, we utilize real-time feedback to adapt to the dynamic environment resulting from the time-varying interference strategy, as well as the involvement of various types of entities. Moreover, we propose a hierarchical reward function to improve the access efficiency. Simulation results show that our method reduces the congestion between users by up to 47% and enhances access efficiency is about 3.2 times compared with random access under malicious jammer intro conclusion finding.