@inproceedings{b18554d5c95947b4ac7327c48e03e7ad,
title = "Reinforcement Learning Vs ILP Optimization in IoT support of Drone assisted Cellular Networks",
abstract = "Several reinforcement techniques are compared to take control of Unmanned Aerial Vehicles (UAVs) and optimize communication offloading in cellular networks. Navigation actions are calculated to send the drones to the required position and turn them back when not needed. First, a use case is expressed and solved in form of a linear programming problem (ILP). Then, a Q learning algorithm is designed and evaluated to solve the same problem. Finally, a deep neural network based on Long Short Term Memory recurrent networks is used. The results of the three approaches are obtained with a real dataset extracted from the CDRs (Call Detail Records) in Milan city, Italy. It is shown that Q learning needs long convergence times to succeed to approach the ILP optimal results. Also, we demonstrate that deep neural network techniques learn much faster and mimic the ILP with very high scores.",
keywords = "Anomaly detection, LSTM, Machine learning, Network outliers, Qlearning, reinforcement learning techniques",
author = "Aicha Dridi and Mohammed Laroui and Cherifa Boucetta and Hossam Afifi and Hassine Moungla",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Communications, ICC 2022 ; Conference date: 16-05-2022 Through 20-05-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/ICC45855.2022.9838311",
language = "English",
series = "IEEE International Conference on Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4589--4594",
booktitle = "ICC 2022 - IEEE International Conference on Communications",
}