"Modelling V-band Atmospheric Loss with Deep Learning" by Ralph Lyndon Gesner
 

Electrical and Computer Engineering ETDs

Publication Date

Fall 12-15-2024

Abstract

Modelling atmospheric loss is fundamental to implementation of future mm-wave terrestrial and earth space communication links. Atmospheric losses have traditionally been estimated by calculating the contributions of individual weather phenomena through in-situ meteorological measurements and statistically derived models for estimating localized atmospheric water content. Collecting data for all points along the propagation path and calculating the physical effects of gases, clouds, and precipitation individually is both time consuming and impractical for large-scale use cases.

In response to these present limitations, this work proposes the use of novel deep learning models for estimating atmospheric losses from tabular meteorological sensor data and doppler weather radar images. These models were validated on a multi-year propagation measurement campaign operating at 72 GHz and when combined with a natural gradient boosting algorithm to provide a unique probabilistic estimator. This new fusion deep learning and gradient boosting model can predict atmospheric loss based on current meteorological conditions along with the model’s confidence.

Keywords

Propagation, V-band, Deep Learning

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Christos Christodoulou

Second Committee Member

Mark Gilmore

Third Committee Member

Trilce Estrada

Fourth Committee Member

Steven Lane

Download

Share

COinS