A Novel Atmosphere-informed Data-driven Predictive Channel Modeling for B5G/6G Satellite-terrestrial Wireless Communication Systems at Q-band

Lu Bai, Qian Xu, Shangbin Wu, Spiros Ventouras, George Goussetis

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Abstract

This paper proposes a novel atmosphere-informed predictive satellite channel model for beyond the fifth-generation (B5G)/the sixth-generation (6G) satellite-terrestrial wireless communication systems at Q-band to model/predict channel attenuation at any specific time. The proposed channel model is a data-driven model based on either of two deep learning networks, i.e., multi-layer perceptron (MLP) and long short-term memory (LSTM). The accuracy of the proposed channel model is measured by cumulative density function (CDF) of absolute error and mean square error (MSE) between modeled/predicted and measured channel attenuation. The complexity of the proposed channel model is assessed by the training time, loading time, and test time of deep learning networks. To further improve the accuracy of the proposed channel model, weather classification is developed at the stage of database construction. Based on our established channel and weather measurement campaign, the performance of the proposed data-driven channel model based on different deep learning networks, e.g., MLP and LSTM, with or without the weather classification is investigated and analyzed comprehensively. Finally, the close agreement is achieved between the channel attenuation modeled/predicted from the proposed atmosphere-informed predictive satellite channel model and the one from real channel measurements, verifying the utility of proposed channel model.

Original languageEnglish
JournalIEEE Transactions on Vehicular Technology
Early online date10 Nov 2020
DOIs
Publication statusE-pub ahead of print - 10 Nov 2020

Keywords

  • Atmospheric measurements
  • Atmospheric modeling
  • Attenuation
  • B5G/6G satellite-terrestrial wireless communications
  • Channel modeling and measurement
  • Channel models
  • datadriven
  • deep learning networks
  • Meteorology
  • Q-band
  • Satellites

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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