Analysis and Evaluation of Random Access Transmission for UAV-Assisted Vehicular-to-Infrastructure Communications

In this paper, we propose a random access protocol for vehicular-to-infrastructure communications. We consider the case where an unmanned aerial vehicle (UAV) provides assistance to a roadside unit to enhance the system throughput. In a traditional carrier sense multiple access schemes (CSMA), the v...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access 2019, Vol.7, p.12427-12440
Hauptverfasser: Shafiq, Zeeshan, Abbas, Rana, Zafar, Mohammad Haseeb, Basheri, Mohammed
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper, we propose a random access protocol for vehicular-to-infrastructure communications. We consider the case where an unmanned aerial vehicle (UAV) provides assistance to a roadside unit to enhance the system throughput. In a traditional carrier sense multiple access schemes (CSMA), the vehicle senses the channel first and it does not transmit the data until the channel is free. However, the CSMA has been shown to be often wasteful of resources and includes potentially unbounded channel access delays in dense networks. In this paper, we use the capture effect, where collisions can be resolved, provided the signal-to-interference-plus-noise ratio is larger than a predetermined threshold. Moreover, we show that the access probability of the vehicles can be optimized based on the known density of the network to maximize throughput. Based on the proposed random access protocol, we model the behavior of the vehicles using a two-dimensional Markov chain and derive the expression for the average system throughput. Finally, we propose two transmission power control schemes to further enhance system throughput. We present extensive simulation results to show that the UAV can provide 9%-38% improvement in throughput for variable network densities.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2892776