Network Slicing Enabled Resource Management for Service-Oriented Ultra-Reliable and Low-Latency Vehicular Networks

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 7; pp. 7847 - 7862
• Main Authors: Chen, Yuanbin, Wang, Ying, Liu, Man, Zhang, Jiayi, Jiao, Lei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Algorithms; Computer simulation; latency; Mixed integer; Network latency; Network slicing; Optimization; Parameters; Power control; Queues; reliability; Resource management; Stability analysis; stochastic optimization; Ultra reliable low latency communication; Vehicles; Vehicular networks
• DOI: 10.1109/TVT.2020.2991723
• ISSN: 0018-9545

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a <inline-formula><tex-math notation="LaTeX">[ {{\mathcal O}({1/V}),{\mathcal O}(V)} ]</tex-math></inline-formula> trade-off with the control parameter <inline-formula><tex-math notation="LaTeX">V</tex-math></inline-formula>. Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters.