Paper Info
Title
Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning: A Model-Free Deep Reinforcement Learning Approach
Abstract
Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the fast-changing network topology. By leveraging the widely deployed LTE infrastructures, we propose a semi-decentralized network slicing framework in this paper based on the C-V2X Mode-4 standard to provide customized network slices for diverse V2V services. With only the long-term and partial information of vehicular networks, eNodeB (eNB) can infer the underlying network situation and then intelligently adjust the configuration for each slice to ensure the long-term QoS performance. Under the coordination of eNB, each vehicle can autonomously select radio resources for its V2V transmission in a decentralized manner. Specifically, the slicing control at the eNB is realized by a model-free deep reinforcement learning (DRL) algorithm, which is a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared to the existing DRL algorithms, the proposed DRL neither requires any prior knowledge nor assumes any statistical model of vehicular networks. Furthermore, simulation results show the effectiveness of our proposed intelligent network slicing scheme.
Year
DOI
Venue
2022
10.1109/TITS.2021.3109878
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Keywords
DocType
Volume
Network slicing, Quality of service, Resource management, Sensors, Mathematical model, Vehicular ad hoc networks, Vehicle dynamics, V2V communication, C-V2X mode-4, network slicing, deep reinforcement learning
Journal
23
Issue
ISSN
Citations 
8
1524-9050
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jie Mei100.34
Xianbin Wang22365223.86
Kan Zheng300.34