Predicting Wireless Channel Quality by means of Moving Averages and
Regression Models
Abstract
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 verage,
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.