Activity‐related multifractal properties of Wi‐Fi signals

In wireless local area networks (WLANs) compliant with the IEEE 802.11n standard, channel state information (CSI) is known to embed information about the surrounding environment, including the positions and movements of people. So far, this information has been inferred by exploiting certain temporal and spectral features of the related WLAN signals. In this letter, their multifractal, i.e. structural properties are examined by means of detrended fluctuation analysis. Preliminary results support the claims that (a) CSI traces exhibit multifractal properties and (b) these are influenced by human presence and activity/posture within the room. Structural properties of IEEE 802.11n Wi‐Fi signals were examined by applying detrended fluctuation analysis to the channel state information traces. The outputs proved to be dependent on both human presence and activity in the room, which enabled to formulate highly distinctive classification criteria.