Predicting Wireless Channel Quality by means of Moving Averages and
  Regression Models
Gabriele Formis Stefano Scanzio Gianluca Cena Adriano Valenzano
The ability to reliably predict the future quality of a wireless channel, as
seen by the media access control layer, is a key enabler to improve performance
of future industrial networks that do not rely on wires. Knowing in advance how
much channel behavior may change can speed up procedures for adaptively
selecting the best channel, making the network more deterministic, reliable,
and less energy-hungry, possibly improving device roaming capabilities at the
same time.
  To this aim, popular approaches based on moving averages and regression were
compared, using multiple key performance indicators, on data captured from a
real Wi-Fi setup. Moreover, a simple technique based on a linear combination of
outcomes from different techniques was presented and analyzed, to further
reduce the prediction error, and some considerations about lower bounds on
achievable errors have been reported. We found that the best model is the
exponential moving average, which managed to predict the frame delivery ratio
with a 2.10\% average error and, at the same time, has lower computational
complexity and memory consumption than the other models we analyzed.