The fusion of the Internet of Things (IoT) with Sixth-Generation (6G) technology has significant potential to revolutionize the IoT landscape. With the ultra-reliable and low-latency communication capabilities of 6G, 6G-IoT networks can transmit high-quality and diverse data to enhance edge learning. Artificial Intelligence-Generated Content (AIGC) harnesses advanced AI algorithms to automatically generate various types of content. The emergence of edge AIGC integrates with edge networks, facilitating real-time provision of customized AIGC services by deploying AIGC models on edge devices. However, the current practice of edge devices as AIGC Service Providers (ASPs) lacks incentives, hindering the sustainable provision of high-quality edge AIGC services amidst information asymmetry. In this paper, we develop a user-centric incentive mechanism framework for edge AIGC services in 6G-IoT networks. Specifically, we first propose a contract theory model for incentivizing ASPs to provide AIGC services to clients. Recognizing the irrationality of clients towards personalized AIGC services, we utilize Prospect Theory (PT) to capture their subjective utility better. Furthermore, we adopt the diffusion-based soft actor-critic algorithm to generate the optimal contract design under PT, outperforming traditional deep reinforcement learning algorithms. Our numerical results demonstrate the effectiveness of the proposed scheme.

翻译：物联网与第六代移动通信技术的融合具有革新物联网生态的巨大潜力。凭借6G的超高可靠与超低时延通信能力，6G-IoT网络能够传输高质量、多样化的数据以增强边缘学习效能。人工智能生成内容通过先进的AI算法自动生成多模态内容。边缘AIGC的出现与边缘网络深度融合，通过在边缘设备部署AIGC模型，实现了定制化AIGC服务的实时供给。然而，当前边缘设备作为AIGC服务提供方的实践缺乏有效激励，在信息不对称环境下阻碍了高质量边缘AIGC服务的可持续供给。本文构建了一种面向6G-IoT网络边缘AIGC服务的用户中心化激励机制框架。具体而言，我们首先提出基于契约理论的模型以激励AIGC服务提供商向客户提供服务。考虑到客户对个性化AIGC服务存在的非理性决策特征，采用前景理论更精准地刻画其主观效用。进一步，我们采用基于扩散模型的柔性演员-评论家算法生成前景理论下的最优契约设计方案，其性能优于传统深度强化学习算法。数值仿真结果验证了所提方案的有效性。