Linear-complexity subcarrier selection strategy for fast preprocessing of CSI in passive Wi-Fi sensing classification tasks

Abstract

Deployment of low-cost Wi-Fi sensing applications may impose strict constraints on available computational power, thereby making the reduction of time required to process channel state information (CSI) measurements a matter of interest. Most works on passive sensing for classification tasks achieve this via dimensionality reduction of CSI data prior to the training of machine learning models, which in itself still imposes some computational burden. Subcarrier selection, which is a faster approach and is widely used in another application domain (i.e., vital signal monitoring), is seldom considered; in the few works where it is used, only a variance-based unsupervised strategy is applied. In this letter, a supervised linear-complexity subcarrier selection strategy is proposed for enhanced sensing classification accuracy. The approach is validated through practical experiments whose results show that the classification performance can approach or even surpass that obtained via dimensionality reduction, with substantial time savings.