Prediction of Channel Excess Attenuation for Satellite Communication Systems at Q-Band Using Artificial Neural Network

Lu Bai, Cheng-Xiang Wang, Qian Xu, Spiros Ventouras, George Goussetis

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This letter proposes the use of an artificial neural network (ANN) for estimating the fading of a Q-band (39.402 GHz) satellite channel exploiting the knowledge of its previous state, as well as the present weather conditions. The ANN is trained using weather data and propagation measurements at the Q-band obtained during a period of nine months by the Aldo Paraboni receivers of RAL Space, Chilbolton, Hampshire, U.K. Subsequently, the estimation obtained by the ANN is compared with actual propagation measurements on data obtained over a period of three months. Statistical analysis demonstrates an agreement between the ANN estimation and the measurement within a 1 dB range with a probability exceeding 98.8%. The significance of this letter lies with the opportunities it raises to deliver real-time fading estimations using low-cost weather sensors combined with feedback on the channel state from the return link, which can be used in the deployment of propagation impairment mitigation techniques.

Original languageEnglish
Pages (from-to)2235-2239
Number of pages5
JournalIEEE Antennas and Wireless Propagation Letters
Issue number11
Early online date2 Aug 2019
Publication statusPublished - Nov 2019



  • Artificial neural network (ANN)
  • Channel excess attenuation
  • Satellite communication systems
  • Weather conditions

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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