An overview of machine learning techniques for radiowave propagation modeling
We give an overview of recent developments in the modeling of radiowave propagation,
based on machine learning (ML) algorithms. We identify the input and output specification …
based on machine learning (ML) algorithms. We identify the input and output specification …
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 …
types of inputs, in order to perform path loss predictions in urban areas. Different types of …
Enhancing machine learning models for path loss prediction using image texture techniques
SP Sotiroudis, K Siakavara… - IEEE Antennas and …, 2021 - ieeexplore.ieee.org
The performance of machine learning (ML)-based path loss models relies heavily on the
data they use at their inputs. Feature engineering is, therefore, essential for the model's …
data they use at their inputs. Feature engineering is, therefore, essential for the model's …