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Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities

Xiaoxia Xu, Ruikang Zhong, Xidong Mu, Yuanwei Liu, Kaibin Huang

TL;DR

A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT), which enables distributed on-device generation at mobile edge networks for real-time DT applications, is proposed and the convergence between MEG and DT is highlighted.

Abstract

A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT) is proposed, which enables distributed on-device generation at mobile edge networks for real-time DT applications. First, an MEG-DT architecture is put forward to decentralize generative artificial intelligence (GAI) models onto edge servers (ESs) and user equipments (UEs), which has the advantages of low latency, privacy preservation, and individual-level customization. Then, various single-user and multi-user generation mechanisms are conceived for MEG-DT, which strike trade-offs between generation latency, hardware costs, and device coordination. Furthermore, to perform efficient distributed generation, two operating protocols are explored for transmitting interpretable and latent features between ESs and UEs, namely sketch-based generation and seed-based generation, respectively. Based on the proposed protocols, the convergence between MEG and DT are highlighted. Considering the seed-based image generation scenario, numerical case studies are provided to reveal the superiority of MEG-DT over centralized generation. Finally, promising applications and research opportunities are identified. Code is available at https://github.com/xiaoxiaxusummer/MEG_DT

Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities

TL;DR

A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT), which enables distributed on-device generation at mobile edge networks for real-time DT applications, is proposed and the convergence between MEG and DT is highlighted.

Abstract

A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT) is proposed, which enables distributed on-device generation at mobile edge networks for real-time DT applications. First, an MEG-DT architecture is put forward to decentralize generative artificial intelligence (GAI) models onto edge servers (ESs) and user equipments (UEs), which has the advantages of low latency, privacy preservation, and individual-level customization. Then, various single-user and multi-user generation mechanisms are conceived for MEG-DT, which strike trade-offs between generation latency, hardware costs, and device coordination. Furthermore, to perform efficient distributed generation, two operating protocols are explored for transmitting interpretable and latent features between ESs and UEs, namely sketch-based generation and seed-based generation, respectively. Based on the proposed protocols, the convergence between MEG and DT are highlighted. Considering the seed-based image generation scenario, numerical case studies are provided to reveal the superiority of MEG-DT over centralized generation. Finally, promising applications and research opportunities are identified. Code is available at https://github.com/xiaoxiaxusummer/MEG_DT
Paper Structure (23 sections, 6 figures)

This paper contains 23 sections, 6 figures.

Figures (6)

  • Figure 1: The vision of the proposed MEG-DT architecture.
  • Figure 2: Single-user generation mechanisms for MEG-DT.
  • Figure 3: Multi-user generation mechanisms for MEG-DT.
  • Figure 4: Sketch-based and seed-based generation protocols.
  • Figure 5: Suitable MEG mechanisms/protocols for various use cases and application scenarios.
  • ...and 1 more figures