• F. Carpi, L. Davoli, M. Martalò, A. Cilfone, Y. Yu, Y. Wang and G. Ferrari. RSSI-based Methods for LOS⁄NLOS Channel Identification in Indoor Scenarios. In 2019 16th International Symposium on Wireless Communication Systems (ISWCS). pp. 171–175, 2019. bib | doi ]
  • @inproceedings{cadamaciyuwafe:2019:iswcs,
        author = {{Carpi}, Fabrizio and {Davoli}, Luca and {Martalò}, Marco and {Cilfone}, Antonio and {Yu}, Yingjie and {Wang}, Yi and {Ferrari}, Gianluigi},
        booktitle = {2019 16th International Symposium on Wireless Communication Systems (ISWCS)},
        title = {{RSSI-based Methods for LOS/NLOS Channel Identification in Indoor Scenarios}},
        year = {2019},
        volume = {},
        number = {},
        pages = {171--175},
        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.},
        doi = {10.1109/ISWCS.2019.8877315},
        issn = {},
        month = {Aug},
        location = {Oulu, Finland}