Diffusion Model-based Incentive Mechanism with Prospect Theory for Edge AIGC Services in 6G IoT

TL;DR

This paper proposes a novel incentive mechanism for edge AIGC services in 6G IoT networks, integrating Prospect Theory and diffusion-based reinforcement learning to optimize contracts and motivate edge service providers.

Contribution

It introduces a contract theory-based incentive framework incorporating Prospect Theory and a diffusion-based soft actor-critic algorithm for optimal contract design in edge AIGC services.

Findings

The proposed scheme effectively incentivizes ASPs to provide high-quality AIGC services.

The diffusion-based algorithm outperforms traditional reinforcement learning methods.

Numerical results validate the scheme's effectiveness in 6G IoT scenarios.

Abstract

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.