Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach

• Published in: IEEE open journal of vehicular technology Vol. 2; pp. 272 - 288
• Main Authors: Ye, Qiang, Shi, Weisen, Qu, Kaige, He, Hongli, Zhuang, Weihua, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: IEEE 2021
• Subjects: Autonomous vehicular networks; computing task offloading; cooperative multi-agent deep <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q -learning; diverse QoS provisioning; Edge computing; learning-assisted hierarchical approach; Load management; Processor scheduling; Quality of service; RAN slicing; Resource management; resource sharing; Servers; Task analysis; task scheduling
• ISSN: 2644-1330; 2644-1330
• DOI: 10.1109/OJVT.2021.3089083

In this paper, a two-timescale radio access network (RAN) slicing and computing task offloading problem is investigated for a cloud-enabled autonomous vehicular network (C-AVN). We aim at jointly maximizing the communication and computing resource utilization with diverse quality-of-service (QoS) guarantee for autonomous driving tasks. Specifically, to capture the small-timescale network dynamics, a computing task scheduling problem is formulated as a stochastic optimization program, for maximizing the long-term network-wide computation load balancing with minimum task offloading variations. Due to the large problem size and unavailable network state transition probabilities, we employ cooperative multi-agent deep Q -learning (MA-DQL) with fingerprint to solve the problem by learning the set of stationary task offloading policies with stabilized convergence. Given the task offloading decisions, we further study a RAN slicing problem in a large timescale, which is formulated as a convex optimization program. We focus on optimizing the radio resource slicing ratios among base stations, to maximize the aggregate network utility with statistical QoS provisioning for autonomous driving tasks. Based on the impact of radio resource slicing on computation load balancing, we propose a two-timescale hierarchical optimization framework to maximize both communication and computing resource utilization. Extensive simulation results are provided to demonstrate the effectiveness of the proposed framework in comparison with state-of-the-art schemes.