Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing fram...

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Bibliographic Details
Published in:IEEE journal on selected areas in communications 2021-07, Vol.39 (7), p.2076-2089
Main Authors: Wu, Wen, Chen, Nan, Zhou, Conghao, Li, Mushu, Shen, Xuemin, Zhuang, Weihua, Li, Xu
Format: Article
Language:English
Subjects:
Algorithms
constrained reinforcement learning
Constraints
coupled constraint
Delays
Dynamic scheduling
Heuristic algorithms
Internet of Vehicles
Machine learning
Optimization
Quality of service
Radio spectra
RAN slicing
Resource allocation
Resource management
Roadsides
Slicing
Task analysis
Traffic volume
Vehicle dynamics
Vehicles
vehicular networks
Workload
workload distribution
Workloads
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Summary:In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicated coupled constraints among decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose a two-layer constrained RL algorithm, named R esource A llocation and W orkload di S tribution (RAWS) to solve them. Specifically, an outer layer first makes the resource allocation decision via an RL algorithm, and then an inner layer makes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.
ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2020.3041405