发表机构
Cooperative Medianet Innovation Center and the School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（合作中位网创新中心和上海交通大学信息科学与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文介绍用于6G网络的生成式通信范式GenCom，大型人工智能模型驱动语义理解等并嵌入通信，使发送方传最少信息，接收方合成输出，重新定义通信。提出两层架构，经分析其在多场景有优势，还概述了未来研究方向。
AI 中文摘要
生成式人工智能的突破性发展正在迅速提升生成图像和视频等内容的能力，重塑通信范式。本文介绍了生成式通信（GenCom），这是一种用于6G网络的新颖范式，其中大型人工智能模型驱动语义理解、推理和内容生成，并将其嵌入通信过程。与严格追求准确比特传输的传统系统不同，GenCom使发送方仅传达最少但足够的信息，而接收方利用共享的生成先验和知识库来合成预期输出。通信因此被重新定义为受控生成而非数据再现。我们形式化了GenCom的概念，阐明其原生人工智能和生成驱动的特性，并展示其核心机制。提出了由关键使能技术支持的两层GenCom架构，对四个代表性应用场景的分析表明，GenCom提供了超高效传输、语义级鲁棒性和新的网络功能。最后，我们概述了未来的研究方向，包括基础理论和实时处理，突出了通往6G网络的一条有前景的途径。
英文摘要
The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process. Unlike traditional systems that strictly pursue accurate bit transmission, GenCom enables transmitters to convey only minimal yet sufficient information, while receivers leverage shared generative priors and knowledge bases to synthesize the intended output. Communication is thus redefined as controlled generation rather than data reproduction. We formalize the concept of GenCom, clarify its AI-native and generation-driven properties, and present its core mechanisms. A two-layer GenCom architecture supported by key enabling technologies is proposed, and analysis of four representative application scenarios demonstrates that GenCom offers ultra-efficient transmission, semantic-level robustness, and new network functions. Finally, we outline future research directions, including foundational theory and real-time processing, highlighting a promising pathway toward 6G networks.
Commentsaccepted by IEEE Wireless Communications Magazine
