Predicting Wireless Channel Quality by means of Moving Averages and Regression Models
This work addresses the need for more deterministic and reliable wireless networks in industrial settings, but it is incremental as it compares existing methods.
The paper tackled the problem of predicting wireless channel quality to improve industrial network performance, finding that an exponential moving average model achieved a 2.10% average error in predicting frame delivery ratio.
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.