A semantic communication-based workload-adjustable transceiver for wireless AI-generated content (AIGC) delivery
This work addresses the problem of efficient AIGC service delivery for mobile users in dynamic wireless networks, representing an incremental improvement through hybrid methods.
The paper tackles the challenge of delivering high-quality AI-generated content (AIGC) over wireless networks by proposing a semantic communication-based transceiver (ROUTE) that prioritizes semantic information and adjusts computing workload, resulting in reduced latency and improved content quality compared to conventional methods.
With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a Resource-aware wOrkload-adjUstable TransceivEr (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.