Statistical Modeling of Signal Variation for Propagation Along a Lift Shaft

Temporal variation on the tapped amplitude of propagating waves along a lift shaft in a complex campus environment is presented and modeled statistically in this letter. By using the Akaike's Information Criterion (AIC)-based method, distributions of amplitude of signals from different propagation mechanisms are studied separately. If the channel is considered as a whole, the Weibull function is identified as the best model for describing the temporal variation. The Kolmogorov–Smirnov (KS) test is applied to verify the selection of the Weibull model. Through the analysis of the Weibull
$b$
-parameter, the severe temporal variations caused by the opening and closing of the lift door and the movement of the lift car within the lift shaft can clearly be identified. Signals reflected from buildings and walls in the intermediate and far regions do not suffer from much temporal variations. The Weibull function is ideal for the statistical modeling of channel propagation along the lift shaft. Using the Weibull
$b$
-parameter, the dynamic channel along the lift shaft and the static channel from the intermediate and far regions can easily be classified.