Journal
IEEE SYSTEMS JOURNAL
Volume 14, Issue 4, Pages 4694-4705Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2020.2982857
Keywords
Device-to-device communication; Resource management; Quality of service; Throughput; Vehicle dynamics; Virtualization; Machine learning; Deep reinforcement learning (DRL); network slicing; resource aggregation; resource allocation; V2V communication
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Funding
- National Natural Science Research Foundation of China [61771098]
- Fundamental Research Funds for the Central Universities [ZYGX2018J068]
- Department of Science and Technology of Sichuan province [2017GFW0128,8ZDYF2265, 2018JYO578, 2017JY0007]
- ZTE Innovation Research Fund for Universities Program 2016
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Considering bandwidth-hungry and low latency requirement of vehicular communications applications, we propose a novel dynamic reinforcement learning-based slicing framework and optimization solutions for efficient resource provisioning in virtualized network for D2D-based vehicle-to-vehicle (V2V) communication. The aim is to balance resource utilization and quality of service (QoS) satisfaction levels for multiple slices. The slicing framework is designed as a three-stage layered framework. In the first stage, we propose dynamic deep reinforcement learning-based virtual resource allocation scheme to allocate distinct resources to slices. In the second stage, we aggregate the D2D resource portion of the slice resource for D2D-based V2V communication. In the third stage, due to the computational complexity and signaling overhead of the physical resource allocation, we transform the problem into a convex optimization problem and solve with an alternating direction method of multipliers-based distributed algorithm. Performance results are provided in terms of resource utilization, QoS satisfaction and throughput to show the benefit of integrating resource slices dedicated to supporting interslice D2D-based V2V communication in vehicular network.
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