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 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 Resource Allocation and Workload diStribution (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.

I. Introduction

Recent technology advances for vehicular communication networks have laid a solid foundation for diverse Internet of vehicles (IoV) services, e.g., autonomous driving [1], [2]. It is predicted that autonomous vehicles will represent 25% of the automotive market, which is valued up to 77 billion US dollars by 2025 [3]. When a vehicle is on the road, a large number of computation-intensive tasks of IoV services are required to be processed. Processing such computation-intensive tasks by vehicles requires expensive on-board computing facilities and degrades fuel efficiency [4]. As a remedy to these limitations, a potential solution is to explore the multi-access edge computing (MEC) paradigm [5], [6], in which vehicles can offload these computation tasks to computation-powerful roadside radio access networks (RANs) for prompt processing.