Optimizing drone deployment for cellular communication coverage during crowded events
Résumé
In case of unexpected or temporary events, cellular networks can become quickly saturated. A promising solution is using unmanned aerial vehicles (UAVs), known as drones, as flying base stations. In this article, we address the issue of anomalous behaviour within cellular networks that occurs during crowded events. The proposed approach consists of two parts: the detection of overloaded cells using machine learning algorithm (LSTM – Long Short-Term Memory) and the deployment of drone-Bss to assist the cellular network by providing wireless coverage. Initially, we use the LSTM algorithm to analyze the impact of extra-data on the network and then detect the peaks of users demands. Then, we formulate an optimization problem for maximizing the number of users to serve when deploying drones taking into account the energy constraints. The proposed approach is validated using real dataset extracted from the CDR of Milan. Simulation results show that the use of drones can satisfy the QoS requirements of the network.