A Unified Framework for Guiding Generative AI with Wireless Perception in Resource Constrained Mobile Edge Networks

TL;DR

This paper introduces WiPe-GAI, a framework that uses wireless perception to guide generative AI for digital content creation in resource-limited mobile edge networks, improving prediction accuracy and service pricing.

Contribution

It presents a novel wireless perception-guided framework with a new multi-scale perception algorithm and an optimal pricing strategy for resource-constrained environments.

Findings

Effective skeleton prediction from wireless signals

Enhanced user utility through optimal pricing

Improved participation of virtual service providers

Abstract

With the significant advancements in artificial intelligence (AI) technologies and powerful computational capabilities, generative AI (GAI) has become a pivotal digital content generation technique for offering superior digital services. However, directing GAI towards desired outputs still suffer the inherent instability of the AI model. In this paper, we design a novel framework that utilizes wireless perception to guide GAI (WiPe-GAI) for providing digital content generation service, i.e., AI-generated content (AIGC), in resource-constrained mobile edge networks. Specifically, we first propose a new sequential multi-scale perception (SMSP) algorithm to predict user skeleton based on the channel state information (CSI) extracted from wireless signals. This prediction then guides GAI to provide users with AIGC, such as virtual character generation. To ensure the efficient operation of the proposed framework in resource constrained networks, we further design a pricing-based incentive mechanism and introduce a diffusion model based approach to generate an optimal pricing strategy for the service provisioning. The strategy maximizes the user's utility while enhancing the participation of the virtual service provider (VSP) in AIGC provision. The experimental results demonstrate the effectiveness of the designed framework in terms of skeleton prediction and optimal pricing strategy generation comparing with other existing solutions.