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With the significant advancements in artificial intelligence (AI)technologies and powerful computational capabilities, generative AI (GAI) hasbecome a pivotal digital content generation technique for offering superiordigital services. However, directing GAI towards desired outputs still sufferthe inherent instability of the AI model. In this paper, we design a novelframework that utilizes wireless perception to guide GAI (WiPe-GAI) forproviding digital content generation service, i.e., AI-generated content(AIGC), in resource-constrained mobile edge networks. Specifically, we firstpropose a new sequential multi-scale perception (SMSP) algorithm to predictuser skeleton based on the channel state information (CSI) extracted fromwireless signals. This prediction then guides GAI to provide users with AIGC,such as virtual character generation. To ensure the efficient operation of theproposed framework in resource constrained networks, we further design apricing-based incentive mechanism and introduce a diffusion model basedapproach to generate an optimal pricing strategy for the service provisioning.The strategy maximizes the user's utility while enhancing the participation ofthe virtual service provider (VSP) in AIGC provision. The experimental resultsdemonstrate the effectiveness of the designed framework in terms of skeletonprediction and optimal pricing strategy generation comparing with otherexisting solutions.

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