Article (Périodiques scientifiques)
SCMA-Enabled Multi-Cell Edge Computing Networks: Design and Optimization
Liu, Pengtao; An, Kang; Lei, Jing et al.
2023In IEEE Transactions on Vehicular Technology, 72 (6), p. 7987 - 8003
Peer reviewed vérifié par ORBi Dataset
 

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Mots-clés :
binary offloading; Internet of things; multi-access edge computing (MEC); partial offloading; resource management; sparse code multiple access (SCMA); Binary offloading; Edge computing; Energy-consumption; Multi-access edge computing; Multiaccess; Multiple access; Non-orthogonal; Non-orthogonal multiple access; Optimisations; Partial offloading; Resource management; Sparse code multiple access; Sparse codes; Task analysis; Automotive Engineering; Aerospace Engineering; Computer Networks and Communications; Electrical and Electronic Engineering
Résumé :
[en] Multi-access edge computing (MEC) is regarded as a promising approach for providing resource-constrained mobile devices with computing resources through task offloading. Sparse code multiple access (SCMA) is a code-domain non-orthogonal multiple access (NOMA) scheme that can meet the demands of multi-cell MEC networks for high data transmission rates and massive connections. In this paper, we propose an optimization framework for SCMA-enabled multi-cell MEC networks. The joint resource allocation and computation offloading problem is formulated to minimize the system cost, which is defined as the weighted energy cost and latency. Due to the nonconvexity of the proposed optimization problem induced by the coupled optimization variables, we first propose an algorithm based on the block coordinate descent (BCD) method to iteratively optimize the transmit power and edge computing resources allocation by deriving closed-form solutions, and further develop an improved low-complexity simulated annealing (SA) algorithm to solve the computation offloading and multi-cell SCMA codebook allocation problem. To solve the problem of partial state observation and timely decision-making in long-term optimization environment, we put forward a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and distributed execution. Furthermore, we extend the framework to the partial offloading case and propose an algorithm based on alternating convex search for solving the task offloading ratio. Numerical results show that the proposed multi-cell SCMA-MEC scheme achieves lower energy consumption and system latency in comparison to the orthogonal frequency division multiple access (OFDMA) and power-domain (PD) NOMA techniques.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
Liu, Pengtao ;  National University of Defense Technology, College of Electronic Science and Technology, Changsha, China
An, Kang ;  National University of Defense Technology, Sixty-Third Research Institute, Nanjing, China
Lei, Jing ;  National University of Defense Technology, College of Electronic Science and Technology, Changsha, China
Liu, Wei ;  National University of Defense Technology, College of Electronic Science and Technology, Changsha, China
Sun, Yifu;  National University of Defense Technology, College of Electronic Science and Technology, Changsha, China
Zheng, Gan ;  University of Warwick, School of Engineering, Coventry, United Kingdom
CHATZINOTAS, Symeon  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom ; Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
SCMA-Enabled Multi-Cell Edge Computing Networks: Design and Optimization
Date de publication/diffusion :
juin 2023
Titre du périodique :
IEEE Transactions on Vehicular Technology
ISSN :
0018-9545
Maison d'édition :
Institute of Electrical and Electronics Engineers Inc.
Volume/Tome :
72
Fascicule/Saison :
6
Pagination :
7987 - 8003
Peer reviewed :
Peer reviewed vérifié par ORBi
Organisme subsidiant :
National Natural Science Foundation of China
U.K. EPSRC
Disponible sur ORBilu :
depuis le 30 novembre 2023

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