A semi-supervised learning framework using crowdsourced UE data and building information improves RF building loss classification accuracy by up to 12.6% for outdoor-to-indoor cases over supervised learning alone.
Building penetration loss measurements and modelling in the 900 and 2100 mhz band for smart meter installation
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Machine-Learning-Based Classification of Radio Frequency Building Loss
A semi-supervised learning framework using crowdsourced UE data and building information improves RF building loss classification accuracy by up to 12.6% for outdoor-to-indoor cases over supervised learning alone.