An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection

Lu Bai, Qian Xu, Ziwei Huang, Shangbin Wu, Spiros Ventouras, George Goussetis, Xiang Cheng

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
14 Downloads (Pure)

Abstract

This article proposes a novel atmospheric data-driven Q-band satellite channel model using two artificial neural networks, i.e., multilayer perceptron and long short-term memory (LSTM), to estimate real-time channel attenuation at Q-band via a set of atmospheric parameters. Seven atmospheric parameters for modeling satellite channel attenuation are selected by the least absolute shrinkage and selection operator (LASSO) algorithm from 14 commonly used atmospheric parameters. Simulation results demonstrate that the multilayer perceptron-based atmospheric data-driven Q-band satellite channel model via those seven atmospheric parameters is more accurate and less complex than that via the 14 atmospheric parameters. Meanwhile, the accuracy performance of multilayer perceptron- and LSTM-based atmospheric data-driven Q-band satellite channel models, such as absolute errors and mean-squared errors (MSEs), is discussed and analyzed. The complexity of multilayer perceptron and LSTM in this model, such as training time, loading time, and estimation time, is also investigated. It can be seen that the estimated channel attenuation can well align with the measured channel attenuation.

Original languageEnglish
Pages (from-to)4002-4013
Number of pages12
JournalIEEE Transactions on Antennas and Propagation
Volume70
Issue number6
Early online date28 Dec 2021
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Data driven
  • Q-band
  • feature selection
  • key atmospheric parameters
  • satellite communication channel attenuation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection'. Together they form a unique fingerprint.

Cite this