UAV ad hoc network link prediction based on deep graph embedding

被引：0
|
作者：
Shu J. [1 ]
Wang Q. [1 ]
Liu L. [2 ]
机构：
[1] School of Software, Nanchang Hangkong University, Nanchang
[2] School of Information Engineering, Nanchang Hangkong University, Nanchang
来源：
Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Graph embedding; Link prediction; Long short-term memory network; UAV ad hoc network;
D O I：
10.11959/j.issn.1000-436x.2021083
中图分类号：
学科分类号：
摘要：
Aiming at the characteristics of the UAV ad hoc network (UAANET), such as topological temporal-varying, node mobility and intermittent connection, a temporal graph embedding model was proposed to present the preprocessed UAANET. To improve the sampling efficiency, the sampling interval was calculated based on linear probability. The network structure features were mapped to the relationship between nodes, and the contextual semantic features of nodes were extracted by adversarial training. With the help of long and short-term memory network, the temporal characteristics of the UAANET were extracted to predict the connection at the next moment. AUC, MAP, and Error Rate were employed as evaluation indexes. The simulation experiments based on NS-3 show that compared with Node2vec, DDNE and E-LSTM-D, the proposed method has a better accuracy. © 2021, Editorial Board of Journal on Communications. All right reserved.
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页码：137 / 149
页数：12
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