TY - GEN
T1 - Prediction Evaluation for RSSI Data Generated from Leaky Coaxial Cables over Indoor Environment
AU - Hou, Pengcheng
AU - Zhu, Junjie
AU - Nagayama, Kenta
AU - Hou, Yafei
AU - Denno, Satoshi
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the JSPS KAKENHI Grand Number 20K04484 and the Telecommunications Advancement Foundation (TAF).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Using a probabilistic neural network (PNN) based predictor, this paper investigates the prediction accuracy of the time-series received signal strength indicator (RSSI) data generated over an indoor environment configured by multiple Leaky coaxial cable (LCX). In addition, it also compares the prediction results of RSSI data from the same environment but configured with the conventional monopole antenna. The results indicate that the RSSI data generated by the LCX antenna has better prediction accuracy over multi-path rich environment than that of using the conventional monopole antenna, which can be used for many linear-cell based wireless coverage.
AB - Using a probabilistic neural network (PNN) based predictor, this paper investigates the prediction accuracy of the time-series received signal strength indicator (RSSI) data generated over an indoor environment configured by multiple Leaky coaxial cable (LCX). In addition, it also compares the prediction results of RSSI data from the same environment but configured with the conventional monopole antenna. The results indicate that the RSSI data generated by the LCX antenna has better prediction accuracy over multi-path rich environment than that of using the conventional monopole antenna, which can be used for many linear-cell based wireless coverage.
KW - leaky coaxial cable
KW - monopole antenna
KW - probabilistic neural network
KW - RSSI prediction
UR - http://www.scopus.com/inward/record.url?scp=85127026958&partnerID=8YFLogxK
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U2 - 10.1109/ICCE53296.2022.9730575
DO - 10.1109/ICCE53296.2022.9730575
M3 - Conference contribution
AN - SCOPUS:85127026958
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
BT - 2022 IEEE International Conference on Consumer Electronics, ICCE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Y2 - 7 January 2022 through 9 January 2022
ER -