QoS based Deep Reinforcement Learning for V2X Resource Allocation

Bhadauria S, Shabbir Z, Roth-Mandutz E, Fischer G (2020)

Publication Type: Conference contribution

Publication year: 2020

Publisher: Institute of Electrical and Electronics Engineers Inc.

Conference Proceedings Title: 2020 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2020

Event location: Odessa UA

ISBN: 9781728171272

DOI: 10.1109/BlackSeaCom48709.2020.9234960

Abstract

The 3rd generation partnership project (3GPP) standard has introduced vehicle to everything (V2X) communi-cation in Long Term Evolution (LTE) to pave the way for future intelligent transport solutions. V2X communication envisions to support a diverse range of use cases for e.g. cooperative collision avoidance, infotainment with stringent quality of service (QoS) requirements. The QoS requirements range from ultra-reliable low latency to high data rates depending on the supported application. This paper presents a QoS aware decentralized resource allocation for V2X communication based on a deep reinforcement learning (DRL) framework. The proposed scheme incorporates the independent QoS parameter, i.e. priority associated to each V2X message, that reflects the latency required in both user equipment (UE) and the base station. The goal of the approach is to maximize the throughput of all vehicle to infrastructure (V2I) links while meeting the latency constraints of vehicle to vehicle (V2V) links associated to the respective priority. A performance evaluation of the algorithm is conducted based on system level simulations for both urban and highway scenarios. The results show that incorporating the QoS parameter (i.e. priority) pertaining to the type of service supported is crucial in order to meet the latency requirements of the mission critical V2X applications.

Authors with CRIS profile

Shubhangi Bhadauria Professur für Technische Elektronik Georg Fischer Professur für Technische Elektronik

Involved external institutions

Altran Technologies, SA FR France (FR) Fraunhofer-Institut für Integrierte Systeme und Bauelementetechnologie (IISB) DE Germany (DE)

How to cite

APA:

Bhadauria, S., Shabbir, Z., Roth-Mandutz, E., & Fischer, G. (2020). QoS based Deep Reinforcement Learning for V2X Resource Allocation. In 2020 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2020. Odessa, UA: Institute of Electrical and Electronics Engineers Inc..

MLA:

Bhadauria, Shubhangi, et al. "QoS based Deep Reinforcement Learning for V2X Resource Allocation." Proceedings of the 2020 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2020, Odessa Institute of Electrical and Electronics Engineers Inc., 2020.

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