Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

被引:119
作者
Dang Van Huynh [1 ]
Khosravirad, Saeed R. [2 ]
Masaracchia, Antonino [1 ]
Dobre, Octavia A. [3 ]
Duong, Trung Q. [1 ]
机构
[1] Queens Univ Belfast, Belfast BT7 1NN, Antrim, North Ireland
[2] Nokia Bell Labs, Chicago, IL USA
[3] Mem Univ, St John, NL A1C 5S7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Task analysis; Optimization; Ultra reliable low latency communication; Real-time systems; Industrial Internet of Things; Digital twins; Computational modeling; Digital twin; metaverse; mobile edge computing; ultra-reliable and low latency communications; SHORT BLOCKLENGTH REGIME; RESOURCE-ALLOCATION;
D O I
10.1109/LWC.2022.3179207
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this letter, we propose a novel digital twin scheme supported metaverse by jointly considering the integrated model of communications, computing, and storage through the employment of mobile edge computing (MEC) and ultra-reliable and low latency communications (URLLC). The MEC-based URLLC digital twin architecture is proposed to provide powerful computing infrastructure by exploring task offloading, and task caching techniques in nearby edge servers to reduce the latency. In addition, the proposed digital twin scheme can guarantee stringent requirements of reliability and low latency, which are highly applicable for the future networked systems of metaverse. For this first time in the literature, our paper addresses the optimal problem of the latency/reliablity in digital twins-enabled metaverse by optimizing various communication and computation variables, namely, offloading portions, edge caching policies, bandwidth allocation, transmit power, computation resources of user devices and edge servers. The proposed scheme can improve the quality-of-experience of the digital twin in terms of latency and reliability with respect to metaverse applications.
引用
收藏
页码:1733 / 1737
页数:5
相关论文
共 15 条
[1]  
3GPP, 2018, Technical Report (TR) 38.913
[2]  
Ben-Tal Aharon, 2001, MPS-SIAM Series on Optimi, DOI 10.1137/1.9780898718829
[3]   Digital Twin-Aided Intelligent Offloading With Edge Selection in Mobile Edge Computing [J].
Tan Do-Duy ;
Huynh, Dang Van ;
Dobre, Octavia A. ;
Canberk, Berk ;
Duong, Trung Q. .
IEEE WIRELESS COMMUNICATIONS LETTERS, 2022, 11 (04) :806-810
[4]   Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin [J].
Dong, Rui ;
She, Changyang ;
Hardjawana, Wibowo ;
Li, Yonghui ;
Vucetic, Branka .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2019, 18 (10) :4692-4707
[5]  
Grant M., 2009, CVX: Matlab software for disciplined convex programming
[6]  
Huynh D. V., 2022, P IEEE INT C COMM IC, P1
[7]  
Huynh D. V., IET Signal Process
[8]  
Li Y., IET SIGNAL PROCESS
[9]   Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing [J].
Liu, Chen-Feng ;
Bennis, Mehdi ;
Debbah, Merouane ;
Poor, H. Vincent .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2019, 67 (06) :4132-4150
[10]   Digital-Twin-Assisted Task Offloading Based on Edge Collaboration in the Digital Twin Edge Network [J].
Liu, Tong ;
Tang, Lun ;
Wang, Weili ;
Chen, Qianbin ;
Zeng, Xiaoping .
IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (02) :1427-1444