A Distributed Anti-Jamming Algorithm Based on Actor–Critic Countering Intelligent Malicious Jamming for WSN

Abstract

In this paper, in order to solve the problem of wireless sensor networks’ reliable transmission in intelligent malicious jamming, we propose a Distributed Anti-Jamming Algorithm (DAJA) based on an actor–critic algorithm for a multi-agent system. The Multi-Agent Markov Decision Process (MAMPD) is introduced to model the progress of wireless sensor networks’ anti-jamming communication, and the multi-agent system learns the intelligent jamming from the external environment by using an actor–critic algorithm. On the basis of coping with the influence of external and internal factors effectively, each sensor in networks selects the appropriate channels for transmission and finally realizes the optimal transmission of the system overall in a unit time period. In the environment of probabilistic intelligent jamming with tracking properties set in this paper, the simulation shows that the algorithm proposed can outperform the algorithm based on joint Q-learning and the conventional scheme based on orthogonal frequency hopping in terms of transmission. In addition, the proposed algorithm completes two updates of strategy evaluation and action selection in one iteration, which means that the system has higher efficiency of action selection and better adaptability to the environment through the interaction with the external environment, resulting in the better performance of transmission and convergence.

A Fast Anti-Jamming Algorithm Based on Imitation Learning for WSN

“…However, this method has limited generalization capability and limited ability to handle jamming patterns beyond those contained in the training samples. In future plans, there will be a continued exploration and integration of reinforcement learning algorithms, the such as DQN, AC [23], and PPO [24] algorithms,…”

“…However, this method has limited generalization capability and limited ability to handle jamming patterns beyond those contained in the training samples. In future plans, there will be a continued exploration and integration of reinforcement learning algorithms, the such as DQN, AC [23], and PPO [24] algorithms, into the field of communication anti-jamming. An exploration of the merger between imitation learning, deriving insights from past data, and RL, adapting to instantaneous alterations in the present environment, is warranted.…”