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Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

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1 Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3059589

Abstract: Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these… read more here.

Keywords: input; loss prediction; tabular data; loss ... See more keywords

PL-GAN: Path Loss Prediction Using Generative Adversarial Networks

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1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3201643

Abstract: Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we… read more here.

Keywords: gan path; loss prediction; loss; generative adversarial ... See more keywords

Pseudo Ray-Tracing: Deep Leaning Assisted Outdoor mm-Wave Path Loss Prediction

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2 Published in 2022 at "IEEE Wireless Communications Letters"

DOI: 10.1109/lwc.2022.3175091

Abstract: In this letter we present our results on how deep learning can be leveraged for outdoor path loss prediction in the 30GHz band. In particular, we exploit deep learning to boost the performance of outdoor… read more here.

Keywords: ray tracing; loss prediction; deep learning; path loss ... See more keywords