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| Talabbeydokhti, Nasser |
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| Laffite, Ernesto Dante Rodriguez |
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| Kassens-Noor, Eva | Darmstadt |
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| Cho, Seongchul |
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| Tonne, Cathryn |
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| Schmitt, Konrad Erich Kork |
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| Grimm, Daniel |
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Silva, P.
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- 2012Controlling multi-switch networks for prompt reconfigurationcitations
- 2009Experiments on timing aspects of DC-Powerline communicationscitations
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document
A Machine Learning Based Quality of Service Estimator for Aerial Wireless Networks
Abstract
Unmanned Aerial Vehicles (UAVs) acting as aerial Wi-Fi Access Points or cellular Base Stations are being considered to deploy on-demand network capacity in order to serve traffic demand surges or replace Base Stations. The ability to estimate the Quality of Service (QoS) for a given network setup may help in solving UAV placement problems. This paper proposes a Machine Learning (ML) based QoS estimator, based on convolutional neural networks, which estimates the QoS for a given network by considering the UAV positions, the user positions and their offered traffic. The ML-based QoS estimator represents a novel paradigm for estimating the QoS in aerial wireless networks. It provides fast and accurate estimations with reduced computational complexity. We demonstrate the usefulness and applicability of the proposed QoS estimator using the ideal UAV placement algorithm. Simulation results show the QoS estimator has an average prediction error lower than 5%.
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