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.
Deep Learning-Based Throughput Estimation for UAV-Assisted Network
01.09.2019
864895 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
THROUGHPUT ESTIMATION DEVICE AND THROUGHPUT ESTIMATION METHOD
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