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

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) gu...

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Autores principales:	Qiang Ye, Weisen Shi, Kaige Qu, Hongli He, Weihua Zhuang, Xuemin Shen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Autonomous vehicular networks computing task offloading RAN slicing resource sharing task scheduling 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</italic>-learning Transportation engineering TA1001-1280 Transportation and communications HE1-9990
Acceso en línea:	https://doaj.org/article/bd1691e880df4f74b0820bacb4926341
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spelling	oai:doaj.org-article:bd1691e880df4f74b0820bacb49263412021-11-19T00:07:13ZJoint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach2644-133010.1109/OJVT.2021.3089083https://doaj.org/article/bd1691e880df4f74b0820bacb49263412021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9454395/https://doaj.org/toc/2644-1330In 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 <italic>Q</italic>-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.Qiang YeWeisen ShiKaige QuHongli HeWeihua ZhuangXuemin ShenIEEEarticleAutonomous vehicular networkscomputing task offloadingRAN slicingresource sharingtask schedulingcooperative 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</italic>-learningTransportation engineeringTA1001-1280Transportation and communicationsHE1-9990ENIEEE Open Journal of Vehicular Technology, Vol 2, Pp 272-288 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Autonomous vehicular networks computing task offloading RAN slicing resource sharing task scheduling 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</italic>-learning Transportation engineering TA1001-1280 Transportation and communications HE1-9990
spellingShingle	Autonomous vehicular networks computing task offloading RAN slicing resource sharing task scheduling 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</italic>-learning Transportation engineering TA1001-1280 Transportation and communications HE1-9990 Qiang Ye Weisen Shi Kaige Qu Hongli He Weihua Zhuang Xuemin Shen Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
description	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 <italic>Q</italic>-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.
format	article
author	Qiang Ye Weisen Shi Kaige Qu Hongli He Weihua Zhuang Xuemin Shen
author_facet	Qiang Ye Weisen Shi Kaige Qu Hongli He Weihua Zhuang Xuemin Shen
author_sort	Qiang Ye
title	Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
title_short	Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
title_full	Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
title_fullStr	Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
title_full_unstemmed	Joint RAN Slicing and Computation Offloading for Autonomous Vehicular Networks: A Learning-Assisted Hierarchical Approach
title_sort	joint ran slicing and computation offloading for autonomous vehicular networks: a learning-assisted hierarchical approach
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/bd1691e880df4f74b0820bacb4926341
work_keys_str_mv	AT qiangye jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach AT weisenshi jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach AT kaigequ jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach AT honglihe jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach AT weihuazhuang jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach AT xueminshen jointranslicingandcomputationoffloadingforautonomousvehicularnetworksalearningassistedhierarchicalapproach
_version_	1718420631782948864

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

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