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Reinforcement learning Based A Non-Zero-Sum Game for Secure Transmission Against Smart Jamming
Digital Signal Processing ( IF 2.871 ) Pub Date : 2021-02-22 , DOI: 10.1016/j.dsp.2021.103002
Chenyu Zhao; Qing Wang; Xiaofeng Liu; Chun Li; Lidong Shi

Smart jammer and smart anti-jammer have always been attacked and defensed in a contradictory way. In fact, there exists fundamental trade-off between all evolved parties. It is well known that only through interactive training with powerful opponents can the strategy optimization ability in actual combat be improved. In the process of electronic countermeasures between the communication system and the jammer, the traditional electronic warfare attacker uses an open-loop jamming method, that is, the opponent's information cannot be obtained after the jamming, which greatly reduces the combat effect. In order to improve the strategic optimization capabilities of the combatant, cognition and intelligence are introduced into electronic countermeasures. In this paper, we start from modeling a non-zero-sum game, and analyze the Nash equilibrium (NE) of the static secure game and the conditions for its existence. Then, we design a multi-agent reinforcement learning framework with a optimal power control strategy in the dynamic game between the smart jammer and the trained base station (BS). Finally, due to the non-cooperative hostile relationship between the two sides in the actual combat, we add the eavesdropping function and the jamming effect evaluation modular to build a cognitive closed-loop. The experiment shows that the intelligent jammer with the eavesdropping function can seriously reduce the performance of the interfered communication party. However, the intelligent BS after training can effectively combat smart jamming. It can be demonstrated that confrontation training can improve the intelligence level of agents.



中文翻译:

基于强化学习的非零和游戏,可防止智能干扰的安全传输

智能干扰器和智能抗干扰器始终以相互矛盾的方式进行攻击和防御。实际上,所有进化方之间都存在基本的权衡。众所周知,只有通过与强大对手的互动训练,才能提高实际战斗中的战略优化能力。传统的电子战攻击者在通信系统与干扰器之间的电子对策过程中采用开环干扰的方法,即干扰后无法获得对方的信息,大大降低了战斗力。为了提高战斗人员的战略优化能力,将认知和情报引入电子对策中。在本文中,我们从建模非零和博弈开始,并分析了静态安全博弈的纳什均衡(NE)及其存在的条件。然后,在智能干扰器与受训基站(BS)之间的动态游戏中,设计了具有最佳功率控制策略的多主体强化学习框架。最后,由于在实际战斗中双方之间的非合作敌对关系,我们增加了窃听功能和干扰效果评估模块,以建立认知闭环。实验表明,具有监听功能的智能干扰器会严重降低被干扰方的性能。但是,经过训练的智能基站可以有效抵抗智能干扰。可以证明,对抗训练可以提高特工的情报水平。

更新日期:2021-02-22
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