用语义通信优化AI生成内容传输，动态调节计算负载提升效率
A semantic communication-based workload-adjustable transceiver for wireless AI-generated content (AIGC) delivery
- 通过语义通信优先传输关键信息，降低带宽需求
- 动态调整扩散模型的计算负荷与语义密度，减少传输失真
- 适合边缘计算和移动设备上高可靠AI内容分发场景
随着生成式人工智能（GAI）的发展和移动设备的普及，通过无线网络提供高质量AI生成内容（AIGC）服务已成为未来趋势。然而，无线网络中AIGC服务面临信道不稳定、带宽有限及计算资源分布不均等挑战。本文在基于扩散模型的GAI中引入语义通信（SemCom），提出一种资源感知的可工作负载调节收发器（ROUTE），用于动态无线网络中的AIGC传输。具体而言，为缓解通信资源瓶颈，采用语义通信优先传输生成内容的关键语义信息；为提升边缘与本地计算资源利用率并降低传输过程中的语义失真，对扩散模型进行改进，实现协同生成中计算负载与语义密度的动态调节。仿真结果表明，相较于传统AIGC方法，所提ROUTE在延迟和内容质量方面均具明显优势。
原文摘要 · Abstract (English)
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.
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