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

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 coordi...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Hegde, Anupama, Song, Rui, Festag, Andreas
Format: Article
Language:English
Subjects:
5G mobile communication
Artificial intelligence
Cellular communication
Cellular networks
Cellular V2X
Communication networks
Deep learning
deep reinforcement learning
Markov processes
Multiagent systems
Network latency
radio resource allocation
Reinforcement learning
Reliability
Resource allocation
Resource management
Resource scheduling
Sensors
Traffic control
Vehicle-to-everything
Vehicles
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3305267