Joint Task Offloading and Caching for Massive MIMO-Aided Multi-Tier Computing Networks

doi:10.1109/TCOMM.2022.3142162

	Joint Task Offloading and Caching for Massive MIMO-Aided Multi-Tier Computing Networks
	Wang, Kunlun 1; Chen, Wen 2; Li, Jun 3; Yang, Yang4 ; Hanzo, Lajos 5
	2022-03-01
会议录名称	IEEE TRANSACTIONS ON COMMUNICATIONS
ISSN	0090-6778
卷号	70
期号	3
页码	1820-1833
DOI	10.1109/TCOMM.2022.3142162
摘要	In this paper, a massive multiple-input multiple-output (MIMO) relay assisted multi-tier computing (MC) system is employed to enhance the task computation. We investigate the joint design of the task scheduling, service caching and power allocation to minimize the total task scheduling delay. To this end, we formulate a robust non-convex optimization problem taking into account the impact of imperfect channel state information (CSI). In particular, multiple task nodes (TNs) offload their computational tasks either to computing and caching nodes (CCN) constituted by nearby massive MIMO-aided relay nodes (MRN) or alternatively to the cloud constituted by nearby fog access nodes (FAN). To address the non-convexity of the optimization problem, an efficient alternating optimization algorithm is developed. First, we solve the non-convex power allocation optimization problem by transforming it into a linear optimization problem for a given task offloading and service caching result. Then, we use the classic Lagrange partial relaxation for relaxing the binary task offloading as well as caching constraints and formulate the dual problem to obtain the task allocation and software caching results. Given both the power allocation, as well as the task offloading and caching result, we propose an iterative optimization algorithm for finding the jointly optimized results. The simulation results demonstrate that the proposed scheme outperforms the benchmark schemes, where the power allocation may be controlled by the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR). © 1972-2012 IEEE.
关键词	Channel estimation Channel state information Communication channels (information theory) Convex optimization Iterative methods Linear programming MIMO systems Multitasking Scheduling Scheduling algorithms Signal interference Wave interference Delay Massive multiple-input multiple-output Multi-tier Multi-tier computing Optimisations Processor scheduling Resource management Service caching Software Task analysis
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20220311480187
EI主题词	Signal to noise ratio
EISSN	1558-0857
EI分类号	716.1 Information Theory and Signal Processing ; 722.4 Digital Computers and Systems ; 912.2 Management ; 921.6 Numerical Methods
原始文献类型	Conference article (CA)
引用统计
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/178458
专题	信息科学与技术学院_PI研究组_杨旸组
通讯作者	Li, Jun
作者单位	1.School of Communication and Electronic Engineering, East China Normal University, Shanghai, China; 2.Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China; 3.School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China; 4.Shanghai Institute of Fog Computing Technology (SHIFT), ShanghaiTech University, Shanghai, China; 5.Department of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
推荐引用方式 GB/T 7714	Wang, Kunlun,Chen, Wen,Li, Jun,et al. Joint Task Offloading and Caching for Massive MIMO-Aided Multi-Tier Computing Networks[C]:Institute of Electrical and Electronics Engineers Inc.,2022:1820-1833.