학술논문

Modeling the Effects of Gaseous Absorption and Cloud Attenuation for V-band using Deep Learning
Document Type
Conference
Source
2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on. :1589-1590 Jul, 2020
Subject
Fields, Waves and Electromagnetics
Deep learning
Cloud computing
Atmospheric modeling
Neural networks
Terrestrial atmosphere
Predictive models
Attenuation
Neural Networks
Millimeter wave propagation
Machine Learning
Atmospheric losses
Cloud attenuation
Language
ISSN
1947-1491
Abstract
A deep neural network is proposed to study the attenuating effects of the Earth's atmosphere on the W/V-bands of the millimeter wavelength portion of the spectrum. Validation of atmospheric propagation models in the W/V-bands has become an increasingly important subject of communications research, but presents significant hurdles when testing these models due to the great variability in the atmospheric parameters that influence propagation attenuation. The effects of molecular gas resonances and hydrosols become very pronounced due to the short wavelength in these bands. This research employs a multilayered deep learning model to learn and predict the attenuation at V-band frequencies. The data is collected from the ongoing W/V-band Terrestrial Link Experiment (WTLE) in Albuquerque, NM. WTLE uses weather sensors and the received power data across the link to study the effects of atmosphere on V-band propagation.