In this paper, we investigate classification methods aiming at identifying the Line-Of-Sight (LOS) or Non-LOS (NLOS) condition of a wireless channel. Our approach is based on the computation of statistical features over N consecutive channel measurements at the receiver (namely, N Received Signal Strength Indicator, RSSI, values). First, threshold classification criteria, on the considered features, are derived in order to perform LOS/NLOS identification. The thresholds’ values are tuned according to the "behaviour" of the statistical features in the considered environment. This method is compared to a sample-based (whose aim is to detect the data distribution) and a machine learning-based approaches. Although our approach is general, we present experimental results for IEEE 802.11 indoor channels. Our results show that simple threshold-based classification criteria on the considered statistical features may yield approximately 85÷90% LOS/NLOS classification accuracy, making them an attractive strategy for future 5G systems.

RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios / CARPI, FABRIZIO; Davoli, Luca; Martalo, Marco; Cilfone, Antonio; Yu, Yingjie; Wang, Yi; Ferrari, Gianluigi. - ELETTRONICO. - (2019), pp. 171-175. ((Intervento presentato al convegno 2019 16th International Symposium on Wireless Communication Systems (ISWCS) tenutosi a Oulu, Finland nel 27-30 August 2019 [10.1109/ISWCS.2019.8877315].

RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios

CARPI, FABRIZIO;Davoli, Luca;Martalo, Marco;Cilfone, Antonio;Ferrari, Gianluigi
2019

Abstract

In this paper, we investigate classification methods aiming at identifying the Line-Of-Sight (LOS) or Non-LOS (NLOS) condition of a wireless channel. Our approach is based on the computation of statistical features over N consecutive channel measurements at the receiver (namely, N Received Signal Strength Indicator, RSSI, values). First, threshold classification criteria, on the considered features, are derived in order to perform LOS/NLOS identification. The thresholds’ values are tuned according to the "behaviour" of the statistical features in the considered environment. This method is compared to a sample-based (whose aim is to detect the data distribution) and a machine learning-based approaches. Although our approach is general, we present experimental results for IEEE 802.11 indoor channels. Our results show that simple threshold-based classification criteria on the considered statistical features may yield approximately 85÷90% LOS/NLOS classification accuracy, making them an attractive strategy for future 5G systems.
978-1-7281-2527-5
RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios / CARPI, FABRIZIO; Davoli, Luca; Martalo, Marco; Cilfone, Antonio; Yu, Yingjie; Wang, Yi; Ferrari, Gianluigi. - ELETTRONICO. - (2019), pp. 171-175. ((Intervento presentato al convegno 2019 16th International Symposium on Wireless Communication Systems (ISWCS) tenutosi a Oulu, Finland nel 27-30 August 2019 [10.1109/ISWCS.2019.8877315].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2865120
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