2023/12/27 by Ruikang Zhong, Zhong, Ruikang, Xidong Mu +7
Computer Science · Engineering · Neuroscience · #Brain Tumor Detection and Classification #FOS: Computer and information sciences #FOS: Electrical engineering #IoT and Edge/Fog Computing #Molecular Communication and Nanonetworks #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.08662
openalex publication_date 2023/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. Various distributed deployment schemes of the GAI model are proposed to alleviate the immense network load and long user queuing times for accessing GAI models. Two MEG frameworks are proposed, namely the single-ES framework and the multi-ESs framework. 1) A one-to-one joint generation framework between an ES and a UE is proposed, including four specific single-ES MEG protocols. These protocols allow distributed GAI models to transmit seeds or sketches for delivering information efficiently. 2) Several protocols are proposed for multi-ESs MEG, which enable multiple ESs to perform the generation task cooperatively or in parallel. Finally, a case study of a 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 the proposed protocols are able to generate high-quality images at extremely low signal-to-noise ratios. The proposed protocols can significantly reduce the communication overhead compared to the centralized model.