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Workshop
In person
LeadersGoldDiscovery

The future of AI native communication networks

    • Date
    7 July 2026
    Timeframe
    09:00 - 17:15 CEST
    Duration
    8h 15 minutes

      This workshop marks the 4th edition of the MLComm series, continuing the exploration of AI/ML in modern communication networks. As networks evolve towards IMT-2030 (6G), AI/ML is transforming telecom architectures, enabling distributed intelligence, AI-native operations, and multimodal capabilities for automation, optimization, and decision-making. 

      The 2023 edition provided foundational inputs to the Focus Group on Autonomous Networks, while the 2024 edition facilitated collaborations toward the Focus Group on AI-Native Networks. 

      The workshop aligns with key ITU standardization activities, rooted in fundamental specifications from FG ML5G (ITU Y.3172) and FG AN (ITU Y.3061). It has enabled contributions to the ITU Journal for Future and Evolving Technologies, ML5G Challenges, and the Discovery webinar series, promoting research and real-world AI applications in telecom.  

      Several ITU ML5G initiatives, such as AI/ML Challenges and Zindi-hosted competitions, further demonstrated the importance of regional inclusivity, diverse datasets, and research collaborations around AI/ML in networks. Resolution 101 of WTSA-24 (New Delhi, 2024) mentioned standardization in AI-enabled networks, protocols, services, and applications.  

      In the 4th of the MLComms workshop, we will discover breakthroughs like agentic AI, large wireless models, code generation, and digital twins redefining how network services and AI pipelines operate end-to-end. The session also dives into the AINN Sandbox, a testbed for evaluating next-gen mechanisms, automating code generation, and bridging digital and physical network layers.

      Schedule

      This panel discussion will explore how future networks look like, and focus on the technologies that would be applied to future networks. The discussion would highlight technologies such as Gen AI, multi-agent systems, wireless models, and simulation techniques. Perspectives from academia, industry, and operators will be provided.

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      This session is about building terms: a public record for AI training data, what it holds, the key players already building pieces of it, what it asks of the institutions that contribute, and why the window to build it is closing.

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