Optimizing Resource Allocation for Multi-modal Semantic Communication in Mobile AIGC Networks: A Diffusion-based Game Approach

TL;DR

This work tackles resource allocation and semantic transmission efficiency in mobile AIGC networks by introducing GM-SemCom, a diffusion-model–based multi-modal SemCom framework with a controllable extraction module. It defines Age of Semantic Information (AoSI) to measure semantic freshness and formulates a Stackelberg game between MASP and users, incorporating semantic size and QoE. To solve the game under incomplete information, the authors develop a GDM-based DRL algorithm that uses a diffusion model to generate robust policies and a value network to guide learning, demonstrating faster convergence and closer proximity to the Stackelberg equilibrium than baselines. Numerical results show improved reconstruction quality (SSIM/PSNR) and efficient resource use across varying compression rates, channel conditions, and user counts, highlighting the practical potential of diffusion-based, multi-modal SemCom in edge-enabled AIGC services.

Abstract

Mobile Artificial Intelligence-Generated Content (AIGC) networks enable massive users to obtain customized content generation services. However, users still need to download a large number of AIGC outputs from mobile AIGC service providers, which strains communication resources and increases the risk of transmission failures. Fortunately, Semantic Communication (SemCom) can improve transmission efficiency and reliability through semantic information processing. Moreover, recent advances in Generative Artificial Intelligence (GAI) further enhanced the effectiveness of SemCom through its powerful generative capabilities. However, how to strike a balance between high-quality content generation and the size of semantic information transmitted is a major challenge. In this paper, we propose a Generative Diffusion Model (GDM)-based multi-modal SemCom (GM-SemCom) framework. The framework improves the accuracy of information reconstruction by integrating GDMs and multi-modal semantic information and also adopts a controllable extraction module for efficient and controllable problems of unstable data recovery and slow decoding speed in GAI-enabled SemCom. Then, we introduce a novel metric called Age of Semantic Information (AoSI) based on the concept of Age of Information (AoI) to quantify the freshness of semantic information. To address the resource trading problem within the framework, we propose a Stackelberg game model, which integrates the AoSI with psychological factors to provide a comprehensive measure of user utility. Furthermore, we propose a GDM-based algorithm to solve the game under incomplete information. Compared with the traditional deep reinforcement learning algorithms, numerical results demonstrate that the proposed algorithm converges faster and is closer to the Stackelberg equilibrium.

Figures (11)

• Figure 1: The GM-SemCom framework for mobile AIGC networks. Specifically, we introduce several examples of vision-based mobile AIGC services supported by GM-SemCom. Then, based on the framework, we model the resource trading problem as a Stackelberg game with incomplete information. Finally, we propose a GDM-based DRL algorithm to solve the optimal solution of the Stackelberg game with incomplete information.
• Figure 2: The architecture of GDM-based multi-modal SemCom.
• Figure 3: Performance comparison of the proposed method with other compression algorithms in a channel with SNR = 2 dB.
• Figure 4: Performance comparison of the proposed method with other compression algorithms in a channel with SNR = 5 dB.
• Figure 5: SSIM corresponds to different diffusion steps under different compression rates.

Theorems & Definitions (5)

• Definition 1: Stackelberg Equilibrium
• Theorem 1
• proof
• Theorem 2
• proof