Here the intelligent jammer issue is studied. With the rapid development of cognitive radio technology, current cognitive terminals can adaptively or intelligently switch channel by spectrum sensing and decision-making. Most of the traditional jamming methods, such as swept jamming and comb jamming, generally work in a relatively fixed pattern, which are not able to effectively jam the terminals empowered with cognition and spectrum decision-making capability. In view of this problem, the authors propose an intelligent jamming decision-making system based on reinforcement learning. First, in order to jam a pair of transmitter and receiver with adaptive frequency hopping capability, a jammer with spectrum sensing, offline training and learning scheme is proposed. Second, a reinforcement learning-based algorithm for jamming decision-making is proposed and simulated. A special feature of the proposed scheme is that considering the reward is difficult to obtain in the actual communication system, a virtual jamming decision-making method is used to enable the jammer to learn and jam efficiently without the user's prior information. Finally, the proposed jamming model and algorithm are implemented and verified on Universal software radio peripheral testbed.

中文翻译：

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基于强化学习的智能干扰系统的设计与实现

这里研究智能干扰器问题。随着认知无线电技术的飞速发展，当前的认知终端可以通过频谱感知和决策来自适应或智能地切换信道。大多数传统的干扰方法（例如扫频干扰和梳状干扰）通常以相对固定的模式工作，无法有效地干扰具有认知和频谱决策能力的终端。针对这一问题，作者提出了一种基于强化学习的智能干扰决策系统。首先，为了干扰具有自适应跳频能力的一对发送器和接收器，提出了一种具有频谱感测，离线训练和学习方案的干扰器。第二，提出并仿真了一种基于增强学习的干扰决策算法。该方案的一个特点是考虑到在实际的通信系统中很难获得奖励，因此采用了一种虚拟的干扰决策方法，可以使干扰者在没有用户先验信息的情况下有效地学习和干扰。最后，在通用软件无线电外围测试平台上实现并验证了所提出的干扰模型和算法。