Enabling Distributed Generative Artificial Intelligence in 6G: Mobile-Edge Generation

Abstract

Mobile-edge generation (MEG) is an emerging technology that allows the network to meet the challenging traffic load expectations posed by the rise of generative artificial intelligence (GAI). A novel MEG model is proposed for deploying GAI models on edge servers (ESs) and user equipment (UE) to jointly complete text-to-image generation tasks. In the generation task, the ES and UE will cooperatively generate the image according to the text prompt given by the user. To enable the MEG, a pretrained latent diffusion model (LDM) is invoked to generate the latent feature, and an edge-inferencing MEG protocol is employed for data transmission exchange between the ES and the UE. A compression coding technique is proposed for compressing the latent features to produce seeds. Based on the above seed-enabled MEG model, an image quality optimization problem with energy constraint is formulated. The transmitting power of the seed is dynamically optimized by a deep reinforcement learning (DRL) agent over the fading channel. The proposed MEG-enabled text-to-image generation system is evaluated in terms of image quality and transmission overhead. The numerical results indicate that, compared to the conventional centralized generation-and-downloading scheme, the symbol number of the transmission of MEG is materially reduced. In addition, the proposed compression coding approach can improve the quality of generated images under low signal-to-noise ratio (SNR) conditions, and the DRL-enabled dynamic power control further improves the image quality under the energy constraint compared to static transmit power control.