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Long short-term memory for indoor localization using WI-FI received signal strength and channel state information

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Bencharif, L., Ahmed Ouameur, M. et Massicotte, D. (2021, October 13-15). Long short-term memory for indoor localization using WI-FI received signal strength and channel state information. Dans 2021 IEEE 4th 5G World Forum (5GWF), Montreal, Canada.

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Résumé

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.

Type de document: Document issu d'une conférence ou d'un atelier
Mots-clés libres: Indoor localization Deep learning (DL) Wi-Fi Receiver signal strength (RSS) Channel state information (CSI) Long short-term memory (LSTM) Error probability
Date de dépôt: 09 mai 2022 15:44
Dernière modification: 09 mai 2022 15:44
URI: https://depot-e.uqtr.ca/id/eprint/10128

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