| 基于强化学习的无人机辅助物联网抗敌意干扰算法 |
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| 引用本文： | 张孟杰,赵睿,王培臣,周洁. 基于强化学习的无人机辅助物联网抗敌意干扰算法[J]. 信号处理, 2021, 37(1): 11-18. DOI: 10.16798/j.issn.1003-0530.2021.01.002 |
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| 作者姓名： | 张孟杰 赵睿 王培臣 周洁 |
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| 作者单位： | 华侨大学厦门市移动多媒体通信实验室 |
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| 基金项目： | 国家自然科学基金（61401165）；福建省自然科学基金（2019J01055）；华侨大学研究生科研创新基金（18013082036） |
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| 摘 要： | 无人机充当中继辅助物联网节点传输信号时，易遭受干扰强度动态变化的智能干扰等敌意攻击，论文建立了抗敌意干扰攻防Stackelberg博弈模型，其中物联网节点、无人机和智能干扰机为博弈的3个参与者，推导出博弈均衡点及其存在条件，揭示了参与者的信道增益、距离等参数对物联网效用等性能的影响。在未知干扰模型的条件下，论文引入WoLF-PHC算法动态优化物联网节点的发射功率、无人机的发射功率和移动轨迹。仿真结果表明，与基于Q-learning的算法相比，所提算法将无人机的效用提升了84.8%。
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| 关 键 词： | 无人机 抗敌意干扰 博弈论 强化学习 物联网 |
| 收稿时间： | 2020-10-21 |
Anti-jamming Algorithm with Reinforcement Learning in UAV-Aided Internet of Things |
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| Affiliation: | Xiamen Mobile Multimedia Communication Lab, Huaqiao University |
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| Abstract: | The Internet of Things (IoTs) node can transmit messages aided by unmanned aerial vehicle (UAV) who acts as the relay node, which are vulnerable to be attacked, such as the Jammer who can change the interference intensity randomly. A Stackelberg game of attack and defense anti-jammer was proposed in this thesis, the IoTs node, UAV and Jammer were the three players in the game, and the Stackelberg equilibriums (SEs) and its existence condition were derived, the influence of channel gain, distance and other parameters on the utility of IoTs were revealed. The Wolf-PHC was used to dynamically optimize the transmit of the IoTs node and the UAV and the moving trajectory of the UAV unknown the interference model. The simulation results show that the proposed anti-jamming algorithm increases the utility of UAV by 84.8% compared with the algorithm based Q-learning. |
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