Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)
32 Lataukset (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.

Otsikko2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
ISBN (elektroninen)9781728152073
DOI - pysyväislinkit
TilaJulkaistu - toukokuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Vehicular Technology Conference - Antwerp, Belgia
Kesto: 25 toukokuuta 202028 toukokuuta 2020
Konferenssinumero: 91


NimiIEEE Vehicular Technology Conference
ISSN (painettu)1550-2252
ISSN (elektroninen)2577-2465


ConferenceIEEE Vehicular Technology Conference

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