Prediction Evaluation for RSSI Data Generated from Leaky Coaxial Cables over Indoor Environment

Pengcheng Hou, Junjie Zhu, Kenta Nagayama, Yafei Hou, Satoshi Denno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Consumer Electronics, ICCE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665441544
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States
Duration: Jan 7 2022Jan 9 2022

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2022-January
ISSN (Print)0747-668X

Conference

Conference2022 IEEE International Conference on Consumer Electronics, ICCE 2022
Country/TerritoryUnited States
CityVirtual, Online
Period1/7/221/9/22

Keywords

  • leaky coaxial cable
  • monopole antenna
  • probabilistic neural network
  • RSSI prediction

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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