Smart Anti-jamming Mobile Communication for Cloud and Edge-Aided UAV Network

被引:3
|
作者
Li, Zhiwei [1 ]
Lu, Yu [1 ]
Wang, Zengguang [2 ]
Qiao, Wenxin [1 ]
Zhao, Donghao [1 ]
机构
[1] Army Engn Univ PLA, Shijiazhuang Campus, Shijiazhuang 050003, Hebei, Peoples R China
[2] Natl Def Univ, Shijiazhuang 050000, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Anti-Jamming; A3C; Edge Computing; IoT; UAV Network; TRANSMISSION; GAMES;
D O I
10.3837/tiis.2020.12.004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.
引用
收藏
页码:4682 / 4705
页数:24
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