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Linear Combination of Exponential Moving Averages for Wireless Channel Prediction
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  • Gabriele Formis,
  • Stefano Scanzio ,
  • Gianluca Cena,
  • Adriano Valenzano
Gabriele Formis
Politecnico di Torino, National Research Council of Italy (CNR-IEIIT)
Stefano Scanzio
National Research Council of Italy (CNR-IEIIT)
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Gianluca Cena
National Research Council of Italy (CNR-IEIIT)
Adriano Valenzano
National Research Council of Italy (CNR-IEIIT)


The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.
13 Dec 2023Submitted to TechRxiv
18 Dec 2023Published in TechRxiv