Mobile Edge Generation: A New Era to 6G

A conception of mobile edge generation (MEG) is proposed, where generative artificial intelligence (GAI) models are distributed at edge servers (ESs) and user equipment (UE), enabling joint execution of generation tasks. The overall object of MEG is to alleviate the immense network load caused by GA...

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Bibliographic Details
Published in:IEEE network 2024-09, Vol.38 (5), p.47-55
Main Authors: Zhong, Ruikang, Mu, Xidong, Zhang, Yimeng, Jaber, Mona, Liu, Yuanwei
Format: Article
Language:English
Subjects:
5G mobile communication
Computational modeling
Data models
Edge computing
Feature extraction
Generators
Multi-access edge computing
Protocols
Servers
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Summary:A conception of mobile edge generation (MEG) is proposed, where generative artificial intelligence (GAI) models are distributed at edge servers (ESs) and user equipment (UE), enabling joint execution of generation tasks. The overall object of MEG is to alleviate the immense network load caused by GAI service and to reduce user queuing times for accessing GAI service. Two MEG protocols are proposed, namely the seed-based MEG protocol and the sketch-based MEG protocol, which enable efficient information exchange and joint generation among ESs and UE. Based on the MEG protocols, various model deployment strategies are proposed to arrange the distribution of the GAI model among UE and ESs. Furthermore, model deployment problems in multi-ES cases are discussed, where several deployment strategies are proposed for parallel and cooperative multi-ES MEG. Finally, a case study, the text-guided- image-to-image generation is provided, where a latent diffusion model is distributed at an ES and a UE. The simulation results demonstrate that both the proposed protocols are able to generate high-quality images at extremely low signal-tonoise ratios, and they also significantly reduce the communication overhead compared to the centralized model. Finally, open research problems for MEG are highlighted.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.2024.3420240