Linear Combination of Exponential Moving Averages for Wireless Channel
  Prediction
Gabriele Formis Stefano Scanzio Gianluca Cena Adriano Valenzano
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.