Communication Anti-jamming System Based on Deep Reinforcement Learning

Abstract

Abstract Due to the open characteristics of the electromagnetic channel, there are malicious nodes that jam the normal data flow, prevent the legitimate receiver from obtaining information, and then intercept and tamper with the data, which makes the research of communication anti-jamming becomes more and more important. Traditional anti-jamming methods employ a single anti-jamming approach and cannot adaptively adjust their anti-jamming strategy based on the environment, often can’t achieve a good anti-jamming effect in the complex communication environment. In order to solve these challenges, this paper studies the anti-jamming communication model based on deep reinforcement learning (DRL), and builds a simulation system to realize intelligent anti-jamming decision-making by using DRL algorithm. The simulation results show that the proposed intelligent anti-jamming decision can choose the best anti-jamming scheme according to the complex environment and effectively improve the communication quality.