Deep learning for indoor localization based on bi-modal CSI data

In this chapter, we incorporate deep learning for indoor localization based on channel state information with commodity 5GHz WiFi. We first introduce the state-of-the-art deep learning techniques including deep autoencoder network, convolutional neural network, and recurrent neural network. We then present a deep learning based algorithm to leverage bi-modal CSI data, i.e., average amplitudes and estimated angle of arrivals (AOA), in both offline and online stages of fingerprinting. The proposed scheme is validated with extensive experiments. Finally, we examine several open research problems for indoor localization based on deep learning techniques.