Effective Quantised CSI‐Fingerprint for DL‐Based Indoor Localisation

ABSTRACT In recent years, indoor localisation based on channel state information (CSI) fingerprint has been actively researched because of the rapid growth of the Internet of Things (IoT). In addition, various deep learning (DL) methods such as deep neural networks (DNN) and convolutional neural networks (CNN) have been widely discussed for the indoor localisation. The CSI‐fingerprint can be produced by continuous and quantised values. For the CSI‐fingerprint using quantised values, good performance is achieved. However, since quantised data for the optimal level has not been sufficiently discussed, the best performance of quantisation is not indicated. Therefore, in this paper, we propose an effective quantised CSI‐fingerprint for DL‐based indoor localisation.