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Estimation of site-specific radio propagation loss with minimal information

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    • Date
    15 December 2025
    Timeframe
    09:00 - 10:00 CET Geneva
    Duration
    60 minutes

    Stable mobile communication requires understanding radio propagation at specific areas, especially when using high-frequency bands like millimeter waves, which are highly affected by environmental factors such as buildings. Direct measurement of propagation characteristics across areas and frequencies is impractical due to cost and effort. To address this, AI/ML-based methods can estimate area-wide propagation using limited measurement data and environmental information like building layouts. Effective application of this approach involves not only building AI/ML models but also selecting the most relevant data to improve estimation accuracy. This challenge invites participants to explore AI/ML model and data selection methods using provided propagation loss data and 3D maps.

    In this webinar, the top three teams from those who participated in the challenge will present their proposed approaches. Various strategies have been suggested to solve problems in the challenge, and we believe that participants will gain new insights into the application of AI/ML for radio wave propagation estimation. Additionally, KDDI Research has allocated a prize of 3,000 CHF for the challenge. Along with an explanation of the evaluation results, the prize amounts will also be announced during the session. Please note that the technical evaluation results for the submitted teams are available under the “Results” tab at the following URL: https://challenge.aiforgood.itu.int/match/matchitem/112.

     

    Session Objectives:

    By the end of this session, participants will be able to:

    • Comparison of methods for selecting radio propagation data to improve the accuracy of area-wide radio propagation estimation using AI/ML models.
    • Compare the AI/ML modelling techniques for radio propagation.
    • Design and construct models using datasets.
    • Explain the results, inferences and findings from the various design iterations and models.

     

    Recommended Mastery Level / Prerequisites:

    • Basic understanding of wireless networks, the radio propagation estimation problems in general.
    • Basic understanding of the characteristics of the next generation wireless networks, such as 5G and 6G.
    • Basic data analysis and AI/ML modelling techniques.

     

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    Discovery - AI/ML in 5G
    Estimation of site-specific radio propagation loss with minimal information

    Estimation of site-specific radio propagation loss with minimal information

    15 December 2025 - 09:00 to 10:00

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