Pattern-Aware Intelligent Anti-Jamming Communication: A Sequential Deep Reinforcement Learning Approach

Abstract

This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility.