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  4. Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach
 
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2023
Journal Article
Title

Radio Resource Allocation in 5G-NR V2X: A Multi-Agent Actor-Critic Based Approach

Abstract
The efficiency of radio resource allocation and scheduling procedures in Cellular Vehicle-to-X (Cellular V2X) communication networks directly affects link quality in terms of latency and reliability. However, owing to the continuous movement of vehicles, it is impossible to have a centralized coordinating unit at all times to manage the allocation of radio resources. In the unmanaged mode of the fifth generation new radio (5G-NR) V2X, the sensing-based semi-persistent scheduling (SB-SPS) loses its effectiveness when V2X data messages become aperiodic with varying data sizes. This leads to misinformed resource allocation decisions among vehicles and frequent resource collisions. To improve resource selection, this study formulates the Cellular V2X communication network as a decentralized multi-agent networked markov decision process (MDP) where each vehicle agent executes an actor-critic-based radio resource scheduler. Developing further the actor-critic methodology for the radio resource allocation problem in Cellular V2X, two variants are derived: independent actor-critic (IAC) and shared experience actor-critic (SEAC). Results from simulation studies indicate that the actor-critic schedulers improve reliability, achieving a 15 - 20% higher probability of reception under high vehicular density scenarios with aperiodic traffic patterns.
Author(s)
Hegde, Anupama
Technische Hochschule Ingolstadt
Song, Rui
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Festag, Andreas  
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Journal
IEEE access  
Project(s)
5G Innovation Concept Ingolstadt
Ingolstadt Innovation Laboratory
Funder
Bundesministerium für Digitales und Verkehr  
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Open Access
DOI
10.1109/ACCESS.2023.3305267
Language
English
Fraunhofer-Institut für Verkehrs- und Infrastruktursysteme IVI  
Keyword(s)
  • cellular V2X

  • radio resource allocation

  • deep reinforcement learning

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