Estimation of Site-specific radio propagation loss with minimal information
0Stable 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 models but also selecting the most relevant data to improve estimation accuracy. This challenge invites participants to explore modeling and data selection methods using provided propagation loss data and 3D maps. Approaches may include propagation-based modeling or data-driven analysis, with or without 3D maps. Top-performing participants will be awarded a total prize of 3,000 CHF by KDDI Research, Inc.
Learning Objectives:
Compare the modelling techniques for radio propagation
Design and construct models using datasets
Construct and demonstrate the models using applications and code.
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 ML modelling techniques
Speakers:
Kazuki Takezawa
Researcher, KDDI Research, Inc.
Moderators:
Vishnu Ram OV
Independent Research Consultant, International Telecommunication Union (ITU)
Jiaying Meng
Junior Programme Officer, International Telecommunication Union (ITU)
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