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.

翻译：随着人工智能（AI）技术的显著进步和强大计算能力的涌现，生成式AI（GAI）已成为提供卓越数字服务的关键数字内容生成技术。然而，引导GAI生成预期输出仍面临AI模型固有稳定性的挑战。本文设计了一种新型框架，利用无线感知引导GAI（WiPe-GAI），为资源受限的移动边缘网络提供数字内容生成服务（即AI生成内容，AIGC）。具体而言，我们首先提出一种新的序列化多尺度感知（SMSP）算法，基于从无线信号中提取的信道状态信息（CSI）预测用户骨架。该预测进而引导GAI为用户提供AIGC（如虚拟角色生成）。为确保所提框架在资源受限网络中的高效运行，我们进一步设计了一种基于定价的激励机制，并引入基于扩散模型的方法生成服务提供的最优定价策略。该策略在最大化用户效用的同时，增强了虚拟服务提供商（VSP）参与AIGC提供的积极性。实验结果表明，与现有其他解决方案相比，所设计框架在骨架预测与最优定价策略生成方面均具有有效性。