Abstract
Indoor location information is increasing in importance in contemporary communication services and applications. In this paper, we discuss the long short-term memory (LSTM) performance for indoor localization in non-line-of-sight (NLoS) conditions using the received signal strength (RSS) and channel state information (CSI) obtained from Wi-Fi signals. As such, we describe the CSI and RSS acquisition system that is used to build a rich dataset to experiment with classical machine learning and deep learning models. The distance range error matrix is combined with the confusion matrix to obtain the distance range error probability where we have demonstrated that the LSTM model exhibits a maximum range error of less than 5 m with 4% probability.
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