基于强化学习的认知无线网络抗干扰传输
首发时间:2019-03-28
摘要:无线网络的广播特性使其易受干扰攻击,各类干扰攻击都会对无线网络的正常通信造成严重威胁,因此针对不同干扰类型需要制定相应抗干扰策略。在认知无线网络中,干扰用来恶意阻断次要用户的通信导致传输中断。本文研究了认知无线网络中次要用户和扫频干扰之间的相互作用,提出一种假设干扰状态未知下采取Q学习算法来自主学习的最佳抗干扰方法。最终仿真验证了在信道检测有误差时此抗干扰模型的有效性。
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Anti-jamming transmission in cognitive radio networks with reinforcement learning
Abstract:The broadcast characteristic of wireless network makesit vulnerable to the jamming attacks. All types of jamming attacks pose severe threats to the normal communications of wireless networks. Therefore, corresponding anti-jamming strategies need to be developed for different types of jammers.In cognitive radio networks, jammers can maliciously block the communication of secondary users, causing the transmissions interruption.This paper investigates the interaction between secondary users and sweeping jammer in cognitive radio networks.This paper proposes an optimal anti-jamming method based on learning independentlyby the Q-learning algorithmassuming that the jammer state is unknown.Finally the validity of this anti-jamming model with observation errors was verified by simulations.
Keywords: telecommunication anti-jamming cognitive radio networks Q-learing
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