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

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

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 distinct modes of input information. The work at hand tries to incorporate both modes of input...

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Bibliographic Details
Main Authors: Sotirios P. Sotiroudis, Panagiotis Sarigiannidis, Sotirios K. Goudos, Katherine Siakavara
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Convolutional neural networks
data to image transformation
deep learning
path loss
pseudoimages
radio propagation
Online Access:https://ieeexplore.ieee.org/document/9354618/

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