A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication

• URL: http://arxiv.org/abs/2310.17705v2
• Date: Wed, 29 May 2024 10:05:14 GMT
• Title: A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication
• Authors: Runze Cheng, Yao Sun, Dusit Niyato, Lan Zhang, Lei Zhang, Muhammad Ali Imran,
• Abstract summary: Generative AI applications have been recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC) It is challenging to provide qualified AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources. We propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework.
• Score: 53.78269720999609
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI applications have been recently catering to a vast user base by creating diverse and high-quality AI-generated content (AIGC). With the proliferation of mobile devices and rapid growth of mobile traffic, providing ubiquitous access to high-quality AIGC services via wireless communication networks is becoming the future direction. However, it is challenging to provide qualified AIGC services in wireless networks with unstable channels, limited bandwidth resources, and unevenly distributed computational resources. To tackle these challenges, we propose a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework, where only semantic information of the content rather than all the binary bits should be generated and transmitted by using SemCom. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver thereby allowing adjustment of computational resource utilization in edge and local. In addition, a Resource-aware wOrk lOad Trade-off (ROOT) scheme is devised to intelligently make workload adaptation decisions for the transceiver, thus efficiently generating, transmitting, and fine-tuning content as per dynamic wireless channel conditions and service requirements. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.

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