用AGI驱动的生成式语义通信，让信息传输更高效智能。
AGI-Driven Generative Semantic Communications: Principles and Practices
- 基于大模型和生成模型构建新一代语义通信框架
- 支持人类易懂的图文视频输出，适应未知任务场景
- 适合未来AGI应用，推动智能通信落地
语义通信利用人工智能技术提取语义信息以实现高效数据传输，显著降低通信成本。随着向通用人工智能（AGI）演进，对AGI服务日益增长的需求给语义通信带来新挑战。在此背景下，AGI应用通常定义为涵盖广泛甚至未预见目标的一般性任务，并需通过人类友好的界面（如视频、图像或文本）进行交互。为此，我们提出一种支持AGI应用的新型通信范式——生成式语义通信（GSC）。首先阐述GSC的基本概念及其与现有语义通信的区别，随后介绍基于基础模型和生成模型的通用GSC框架。通过两个案例研究验证了GSC的优势。最后讨论开放挑战与新研究方向，以激发该领域研究并推动实际应用。
原文摘要 · Abstract (English)
Semantic communications leverage artificial intelligence (AI) technologies to extract semantic information for efficient data delivery, thereby significantly reducing communication cost. With the evolution towards artificial general intelligence (AGI), the increasing demands for AGI services pose new challenges to semantic communications. In this context, an AGI application is typically defined on a general-sense task, covering a broad, even unforeseen, set of objectives, as well as driven by the need for a human-friendly interface in forms (e.g., videos, images, or text) easily understood by human users.In response, we introduce an AGI-driven communication paradigm for supporting AGI applications, called generative semantic communication (GSC). We first describe the basic concept of GSC and its difference from existing semantic communications, and then introduce a general framework of GSC based on advanced AI technologies including foundation models and generative models. Two case studies are presented to verify the advantages of GSC. Finally, open challenges and new research directions are discussed to stimulate this line of research and pave the way for practical applications.
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