AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid

[2608.14627] AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2608.14627 (eess) [Submitted on 22 Jul 2026] Title: AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid Authors: Lopamudra Kundu , Xingqin Lin , Shuvo Chowdhury , Sree Sankar View a PDF of the paper titled AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid, by Lopamudra Kundu and 2 other authors View PDF Abstract: The sixth-generation (6G) of mobile networks will be shaped not only by artificial intelligence (AI)-enabled network automation and optimization, but also by the need to serve AI as a 6G-native workload. Emerging AI services introduce traffic and compute demands that differ from conventional mobile broadband. Their user experience depends on how quickly useful information is delivered, how bursty and asymmetric multimodal flows are handled, and where inference, retrieval, caching, and content processing are executed. This article presents a joint connectivity-compute view of AI-native 6G. We first characterize representative AI service traffic in terms of uplink/downlink throughput skew, burstiness, and token latency. Next, we discuss how fifth-generation extended reality awareness mechanisms can evolve toward AI traffic characteristics awareness in 6G. Finally, we introduce AI Grid as a distributed AI infrastructure platform for placing workloads according to latency, cost, policy, and service-level constraints. Together, AI-aware connectivity and AI Grid enable 6G as a distributed intelligence platform. Comments: 8 pages, 5 figures, 1 table Subjects: Signal Processing (eess.SP) Cite as: arXiv:2608.14627 [eess.SP] (or arXiv:2608.14627v1 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2608.14627 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lopamudra Kundu [ view email ] [v1] Wed, 22 Jul 2026 16:39:29 UTC (560 KB) Full-text links: Access Paper: View a PDF of the paper titled AI-Native 6G for Distributed Intelligence: Traffic Characteristics, Awareness, and AI Grid, by Lopamudra Kundu and 2 other authors View PDF view license Current browse context: eess.SP < prev | next > new | recent | 2026-08 Change to browse by: eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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