[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82239-en":3,"doc-seo-82239-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82239,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Generative Communications Overview Technologies and Trends","Generative Communications (GenCom) reframes 6G networking by making large AI models drive semantic understanding, reasoning, and content generation inside the communication process. Instead of optimizing for accurate bit transmission, GenCom allows transmitters to convey only minimal but sufficient information, while receivers synthesize outputs using shared generative priors and knowledge bases. A two-layer GenCom architecture and enabling technologies are proposed, and analyses of representative scenarios highlight ultra-efficient transmission, semantic robustness, and new network functions, alongside future research directions.","Generative Communications: Overview, Technologies, and Trends  \nWenjun Zhang, Fellow, IEEE, Zhiyong Chen, Senior Member, IEEE, Tong Wu, Guo Lu, Member, IEEE, Li Song, Senior Member, IEEE, Feng Yang, and Meixia Tao, Fellow, IEEE  \narXiv :2607 .09 183v 1 [ cs .IT] 10 Jul 2026  \nAbstract—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 realtime processing, highlighting a promising pathway toward 6G networks.  \nI. INTRODUCTION  \nAs we advance toward the sixth-generation (6G) mobile communication era, the focus of network design is shifting from device-centric interconnection to intelligent interaction among humans, machines, and environments. 6G is envisioned not only to provide extreme connectivity and ultra-high performance but also to be AI-native, endowing the network with capabilities for semantic understanding, autonomous decisionmaking, and real-time self-adaptation [1] . Consequently, communication is expected to evolve beyond the pursuit of efficient bit transmission, moving toward knowledge-driven and taskoriented information exchange.  \nHowever, the current communication system, fundamentally based on Shannon’s theory, defines the communication process as the accurate transmission of information from transmitter to receiver, with its core design objective being bit transmission efficiency. This framework achieved immense success in traditional communication services. However, its limitations become apparent in intelligent scenarios with growing cognitive demands in the 6G era. Firstly, this paradigm lacks inherent semantic understanding capability, as it focuses exclusively on low-level metrics like bit error rate (BER) and throughput, thereby inherently overlooking the semantic meaning and task relevance of the information. Secondly, communication and intelligence are typically decoupled in existing networks,  \nThe authors are with the Cooperative Medianet Innovation Center and the School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. (e-mail:{zhangwenjun, zhiyongchen, wutong, luguo2014, songli, yangfeng, [mxtao](mxtao}@sjtu.edu.cn)[}](mxtao}@sjtu.edu.cn)[@sjtu.edu.cn](mxtao}@sjtu.edu.cn)). (Corresponding author: Zhiyong Chen, Tong Wu.)  \nActive  \nlinguistic symbols + replication shared context + learned associations  \nFig. 1. An example of human understanding in the brain.  \npreventing systems from actively comprehending, predicting, or generating content during transmission. This decoupling leads to inefficient resource utilization, as the transmission of numerous bits is often irrelevant to the final task objective, resulting in semantic redundancy and wasted bandwidth.  \nFrom a cognitive perspective, human understanding itself is inherently generative [2] . When perceiving or hearing a concept, the b","cbCaimz97JugBrdD","https://ap.wps.com/l/cbCaimz97JugBrdD","pdf",3206488,1,10,"English","en",105,"# Introduction\n## Limitations of Shannon-based communication\n## Cognitive inspiration: generative understanding\n## Toward machine-level generative understanding\n## GenAI as an engine for communication redesign","[{\"question\":\"What is the core idea behind generative communications (GenCom)?\",\"answer\":\"GenCom treats communication as controlled generation rather than data reproduction, using large AI models to perform semantic understanding, reasoning, and content generation as part of the transmission process.\"},{\"question\":\"How does GenCom differ from traditional communication systems?\",\"answer\":\"Traditional systems focus on accurate bit transmission and low-level metrics, while GenCom enables transmitters to send minimal yet sufficient information and lets receivers reconstruct the intended output using shared generative priors and knowledge bases.\"},{\"question\":\"What architecture does the paper propose for GenCom in 6G networks?\",\"answer\":\"The paper proposes a two-layer GenCom architecture supported by key enabling technologies, intended to realize ultra-efficient transmission and semantic-level robustness in multiple application scenarios.\"}]",1784179067,25,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"generative-communications-overview-technologies-and-trends","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/generative-communications-overview-technologies-and-trends/82239/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core idea behind generative communications (GenCom)?","Question",{"text":75,"@type":76},"GenCom treats communication as controlled generation rather than data reproduction, using large AI models to perform semantic understanding, reasoning, and content generation as part of the transmission process.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does GenCom differ from traditional communication systems?",{"text":80,"@type":76},"Traditional systems focus on accurate bit transmission and low-level metrics, while GenCom enables transmitters to send minimal yet sufficient information and lets receivers reconstruct the intended output using shared generative priors and knowledge bases.",{"name":82,"@type":73,"acceptedAnswer":83},"What architecture does the paper propose for GenCom in 6G networks?",{"text":84,"@type":76},"The paper proposes a two-layer GenCom architecture supported by key enabling technologies, intended to realize ultra-efficient transmission and semantic-level robustness in multiple application scenarios.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]