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Cybertwin-Driven DRL-Based Adaptive Transmission Scheduling for Software Defined Vehicular Networks.
- Source :
- IEEE Transactions on Vehicular Technology; May2022, Vol. 71 Issue 5, p4607-4619, 13p
- Publication Year :
- 2022
-
Abstract
- Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic and unpredictable link status. In this paper, we propose a cybertwin-driven learning-based transmission scheduling mechanism for software-defined vehicular networks, which can adaptively select/adjust transmission control methods, i.e., loss-based, delay-based and hybrid ones, to suit to the time-varying network environment. In particular, we first analyze the dynamic network characteristics of three realistic vehicular network scenarios in terms of network throughput, round-trip time (RTT) and RTT jitter. Furthermore, we propose a novel transmission scheduling model and formulate the SDVN transmission scheduling issue as a linear programming problem. To obtain the optimized scheduling policies and guarantee the effectiveness of transmission control, we further propose a Cybertwin-driven and Deep Reinforcement Learning based transmission control solution (TcpCDRL). Specifically, TcpCDRL is featured with: (i) using deep reinforcement learning (DRL) to adaptively adjust transmission control policy, (ii) using cybertwin-driven transmission controlling to improve the policy-making effectiveness and timeliness. Simulation results show that the proposed TcpCDRL approach outperforms the single well-known transmission control approach (i.e., TcpWestwood, TcpBic, TcpVeno and TcpVegas) in terms of network throughput and RTT. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 157008048
- Full Text :
- https://doi.org/10.1109/TVT.2022.3151750