Deep Learning Based Throughput Estimation for UAV-Assisted Network

被引：0
|
作者：
Munaye, Yirga Yayeh [1 ]
Adege, Abebe Belay [1 ]
Tarekegn, Getaneh Berie [1 ]
Lin, Hsin-Piao [2 ]
Li, Yun-Ruei [3 ]
Jeng, Shiann-Shiun [4 ,5 ]
机构：
[1] Natl Taipei Univ Technol, Dept Elect Engn & Comp Sci, Taipei, Taiwan
[2] Natl Taipei Univ Technol, Dept Elect Engn, Taipei, Taiwan
[3] Natl Chiao Tung Univ, Dept Elect Engn & Comp Engn, Hsinchu, Taiwan
[4] Natl Dong Hwa Univ, Dept Elect Engn, Shoufeng Township, Taiwan
[5] Pervas Artificial Intelligence Res Pair Labs, Hsinchu, Taiwan
来源：
2019 IEEE 90TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2019-FALL) | 2019年
关键词：
unmanned aerial vehicles(UAV); base stations; throughput estimation; deep learning (DL);
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Due to the rapid growth of mobile technology, unmanned aerial vehicles(UAV) is emerging as a promising solution to distribute wireless data for ground users as a base station (BS). Our study focuses on the analysis of UAV-based BS that assist aerial wireless network. We practically used the real data measurement from the UAVs connected with ground mobile users as air-to-ground(A2G) communication service. The main aim of our work is to analyze and estimate the UAV-BS user throughput with different parameters such as height and distance. In order to achieve our objective, we have estimated the locations of UAVs' and mobile-users', heights of UAV, elevation angle and nature of LoS/NLoS. The system performances are evaluated through long short term memory(LSTM) and comparison was made with multi-layer perceptron(MLP) algorithm. Finally, the evaluation result shows the system has accurate and motivated prediction performances of the user throughput.
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页数：5
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