In this article, we investigate intelligent anti-jamming communication method for wireless sensor networks. The stochastic game framework is introduced to model and analyze the multi-user anti-jamming problem, and a joint multi-agent anti-jamming algorithm (JMAA) is proposed to obtain the optimal anti-jamming strategy. In intelligent multi-channel blocking jamming environment, the proposed JMAA adopts multi-agent reinforcement learning to make online channel selection, which can effectively tackle the external malicious jamming and avoid the internal mutual interference among sensor nodes. The simulation results show that, the proposed JMAA is superior to the frequency-hopping method, the sensing-based method and the independent reinforcement learning. Specifically, the proposed JMAA has the higher average packet receive ratio than both the frequency-hopping method and the sensing-based method. Compared with the independent reinforcement learning, JMAA has faster convergence rate when reaching the same performance of average packet receive ratio. In addition, since the JMAA does not need to model the jamming patterns, it can be widely used for combating other malicious jamming such as sweep jamming and probabilistic jamming.

中文翻译：

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无线传感器网络的智能抗干扰通信：一种多智能体强化学习方法

在本文中，我们研究了用于无线传感器网络的智能抗干扰通信方法。引入了随机博弈框架对多用户抗干扰问题进行建模和分析，提出了一种联合多智能体抗干扰算法（JMAA）以获得最优的抗干扰策略。在智能多通道阻塞干扰环境中，提出的JMAA采用多智能体强化学习进行在线通道选择，可以有效解决外部恶意干扰，避免传感器节点内部的相互干扰。仿真结果表明，所提出的JMAA优于跳频方法，基于感知的方法和独立的强化学习方法。具体来说，与跳频方法和基于感知的方法相比，提出的JMAA具有更高的平均数据包接收率。与独立强化学习相比，JMAA在达到相同的平均分组接收率性能时具有更快的收敛速度。此外，由于JMAA不需要对干扰模式进行建模，因此它可以广泛用于抵抗其他恶意干扰，例如扫掠干扰和概率干扰。