M. Abadi, TensorFlow: a system for largescale machine learning, In OSDI, vol.16, pp.265-283, 2016.

A. , Next generation global telecom infrastructure, 2018.

F. , FAA Approves Drone to Restore Puerto Rico Cell Service, 2017.

A. Fotouhi, DroneCells: Improving 5G Spectral Efficiency using Drone-mounted Flying Base Stations, 2017.

M. Grossglauser, Mobility increases the capacity of ad hoc wireless networks, IEEE/ACM Trans. Networking, vol.10, pp.477-486, 2002.

X. Hong, A group mobility model for ad hoc wireless networks, ACM MSWiM, pp.53-60, 1999.

Y. Lecun, Deep learning, nature, vol.521, p.436, 2015.

R. Li, DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised MillimetreWave Backhauls, 2018.

H. Mao, Neural adaptive video streaming with pensieve, SIGCOMM. ACM, pp.197-210, 2017.

V. Mnih, Human-level control through deep reinforcement learning, Nature, vol.518, p.529, 2015.

V. Mnih, Asynchronous methods for deep reinforcement learning, ICML, 1928.

A. Orsino, Effects of Heterogeneous Mobility on D2D-and Drone-Assisted Mission-Critical MTC in 5G, IEEE Comms. Mag, vol.55, pp.79-87, 2017.

J. Oueis, Overview of LTE Isolated EUTRAN Operation for Public Safety, IEEE Comms. Standards Magazine, vol.1, issue.2, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01573383

. Rcr-wireless, Drone test trials to shape FAA rules, 2018.

C. Zhang, Deep Learning in Mobile and Wireless Networking: A Survey, 2018.