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

TL;DR

This paper proposes a two-layer constrained reinforcement learning framework called RAWS for dynamic RAN slicing in vehicular networks, optimizing resource allocation and workload distribution to meet QoS needs efficiently.

Contribution

It introduces a novel two-layer constrained RL algorithm for joint resource allocation and workload distribution in RAN slicing, addressing complex coupled constraints.

Findings

RAWS reduces system cost significantly compared to benchmarks.

The framework effectively satisfies QoS requirements with high probability.

Simulation results demonstrate improved adaptability to vehicle traffic dynamics.

Abstract

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.