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TUTCRIS

Reinforcement learning for improved UAV-based integrated access and backhaul operation

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
KustantajaIEEE
Sivumäärä7
ISBN (elektroninen)9781728174402
ISBN (painettu)978-1-7281-7441-9
DOI - pysyväislinkit
TilaJulkaistu - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Communications Workshops - Dublin, Irlanti
Kesto: 7 kesäkuuta 202011 kesäkuuta 2020

Julkaisusarja

NimiIEEE/CIC international conference on communications in China - workshops
ISSN (painettu)2474-9133
ISSN (elektroninen)2474-9141

Conference

ConferenceIEEE International Conference on Communications Workshops
MaaIrlanti
KaupunkiDublin
Ajanjakso7/06/2011/06/20

Tiivistelmä

There is a strong interest in utilizing commercial cellular networks to support unmanned aerial vehicles (UAVs) to send control commands and communicate heavy traffic. Cellular networks are well suited for offering reliable and secure connections to the UAVs as well as facilitating traffic management systems to enhance safe operation. However, for the full-scale integration of UAVs that perform critical and high-risk tasks, more advanced solutions are required to improve wireless connectivity in mobile networks. In this context, integrated access and backhaul (IAB) is an attractive approach for the UAVs to enhance connectivity and traffic forwarding. In this paper, we study a novel approach to dynamic associations based on reinforcement learning at the edge of the network and compare it to alternative association algorithms. Considering the average data rate, our results indicate that the reinforcement learning methods improve the achievable data rate. The optimal parameters of the introduced algorithm are highly sensitive to the donor next generation node base (DgNB) and UAV IAB node densities, and need to be identified beforehand or estimated via a stateful search. However, its performance nearly converges to that of the ideal scheme with a full knowledge of the data rates in dense deployments of DgNBs.

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