Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

Amin Azari, Fayezeh Ghavimi, Mustafa Ozger, Riku Jäntti, Cicek Cavdar

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

9 Citations (Scopus)
143 Downloads (Pure)


Cellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.

Original languageEnglish
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
ISBN (Electronic)978-1-7281-5207-3
Publication statusPublished - May 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Vehicular Technology Conference - Antwerp, Belgium
Duration: 25 May 202028 May 2020
Conference number: 91

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465


ConferenceIEEE Vehicular Technology Conference
Abbreviated titleVTC-Spring


  • deep machine learning
  • drone communications
  • handover
  • radio resource management


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