Deep learning for indoor localization based on bimodal CSI data

Abstract

In this chapter, we incorporate deep learning for indoor localization utilizing channel state information (CSI) with commodity 5 GHz Wi-Fi. We first introduce the state-ofthe-art deep-learning techniques including deep autoencoder network, convolutional neural network (CNN), and recurrent neural network (RNN). We then present a deep-learning-based algorithm to leverage bimodal CSI data, i.e., average amplitudes and estimated angle of arrivals (AOA), for indoor fingerprinting. The proposed scheme is validated with extensive experiments. Finally, we discuss several open research problems for indoor localization based on deep-learning techniques.