Multiple QoS Enabled Intelligent Resource Management in Vehicle-to-Vehicle Communication

Vehicular networks have stringent quality of service (QoS) requirements in terms of reliability, throughput, and latency. With the emergence of diverse services for autonomous driving, the resource contention in vehicle-to-vehicle communication can cause unavoidable packet loss and, therefore, must...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-09, Vol.25 (9), p.12081-12094
Main Authors: Deng, Yafeng, Paul, Rajib, Choi, Young-June
Format: Article
Language:English
Subjects:
5G mobile communication
Adaptive resource allocation
Autonomous driving
Broadcasting
C-V2X
DQN
multiple QoS
Quality of service
Resource management
Training
Ultra reliable low latency communication
Vehicle dynamics
Vehicular ad hoc networks
vehicular networks
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Summary:Vehicular networks have stringent quality of service (QoS) requirements in terms of reliability, throughput, and latency. With the emergence of diverse services for autonomous driving, the resource contention in vehicle-to-vehicle communication can cause unavoidable packet loss and, therefore, must be handled for safety. Moreover, different levels of QoS should be defined for each service and task; however, existing solutions can neither provide a resource allocation scheme for any level of QoS requirement nor serve new stringent services without configuration or modification. We propose a distributed hierarchical deep Q-network (DH-DQN) to handle resource contention specifically. Thus, an intelligence resource management (I-RM) scheme is designed to serve on-demand QoSs. We first formulate the problem to address multiple QoS requirements, which extends the coverage of resource management tasks for on-demand stringent services. From the perspective of transmission pattern, we designed a hierarchical DQN structure that deals with resource block contention in a fully distributed manner and a state-action framework that enables a numerically defined service demand. In addition, a target \epsilon -greedy is proposed to accelerate convergence, and a modified transfer learning algorithm is used to enhance learning performance for various levels of service. Through extensive simulations, we demonstrated that the proposed DH-DQN can learn successful transmission patterns to meet different levels of multiple QoS requirements.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2024.3365557