Generative Communications: Overview, Technologies, and Trends
For the wireless communications community, this paper proposes a novel paradigm shift from bit transmission to controlled generation, potentially enabling more efficient 6G networks.
This paper introduces generative communications (GenCom) as a new paradigm for 6G networks, where large AI models enable semantic understanding and content generation, allowing transmitters to send minimal information while receivers generate the intended output. The proposed architecture demonstrates ultra-efficient transmission and semantic-level robustness across four application scenarios.
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.