Deep Reinforcement Learning Based Congestion Control for {V2X} Communication

Bhadauria S, Roshdi M, Shawky Hassan K, Fischer G (2021)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Conference Proceedings Title: 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): International workshop on beyond 5G support for the future vehicular networks (IEEE PIMRC 2021 - WS3)

ISBN: 978-1-7281-7586-7

URI: https://ieeexplore.ieee.org/document/9569259

DOI: 10.1109/PIMRC50174.2021.9569259

Abstract

In release 14 (Rel-14) Long Term Evolution
(LTE), the 3rd generation partnership project (3GPP) standard
has introduced Cellular Vehicle to Everything (C-V2X)
communication to pave the way for future intelligent transport
systems (ITS). C-V2X communication envisions supporting a
diverse range of use cases with varying quality of service (QoS)
requirements. For example, cooperative collision avoidance requires
stringent reliability, while infotainment use cases require
a high data throughput. C-V2X communication remains susceptible
to performance degradation due to network congestion.
This paper presents a centralized congestion control scheme
for C-V2X communication based on the Deep Reinforcement
Learning (DRL) framework. A performance evaluation of the
algorithm is conducted based on system-level simulation based
on TAPASCologne scenario in the Simulation of Urban Mobility
(SUMO) platform. The results show the effectiveness of a DRL-based
approach to achieve the packet reception ratio (PRR) as
per the packet's associated QoS while maintaining the average
measured Channel Busy Ratio (CBR) below 0.65.

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How to cite

APA:

Bhadauria, S., Roshdi, M., Shawky Hassan, K., & Fischer, G. (2021). Deep Reinforcement Learning Based Congestion Control for {V2X} Communication. In 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): International workshop on beyond 5G support for the future vehicular networks (IEEE PIMRC 2021 - WS3).

MLA:

Bhadauria, Shubhangi, et al. "Deep Reinforcement Learning Based Congestion Control for {V2X} Communication." Proceedings of the 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): International workshop on beyond 5G support for the future vehicular networks (IEEE PIMRC 2021 - WS3) 2021.

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