A CSI-based Object Detection Scheme using Interleaved Subcarrier Selection in Wireless LAN Systems with Distributed Antennas

Kazuki Noguchi, Osamu Muta, Tomoki Murakami, Shinya Otsuki

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

5 Citations (Scopus)

Abstract

Machine learning based object detection that utilizes channel state information (CSI) in wireless local area network (WLAN) systems is an effective approach for indoor positioning. In this paper, we propose a real-time CSI-based object detection scheme using interleaved subcarrier selection techniques for WLAN systems with distributed antennas, where CSI frames are collected and used as data-set for machine learning and object detection. To improve real-time detection performance, we investigate two approaches; interleaved sampling (IS), and interleaved sampling and clustering (ISC). In the IS scheme, a part of subcarriers are selected among all subcarriers in an interleaved manner to reduce data-set size while maintaining the object detection accuracy. In the ISC scheme, all subcarriers (their CSI) are grouped into several clusters in an interleaved manner and detect a target by integrating cluster-by-cluster machine-learning results. Furthermore, we demonstrate the effectiveness of the proposed approach through real-time experimental evaluations in an indoor environment scenario. Experimental results show that the ISC scheme improves object detection probability than the case without clustering, while the IS scheme is effective in reducing data-set size for obtaining almost the same performance. The results also indicate that the improved detection performance is obtained by using the proposed scheme with a distributed antenna array.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
DOIs
Publication statusPublished - 2021
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: Sept 27 2021Sept 30 2021

Publication series

NameIEEE Vehicular Technology Conference
Volume2021-September
ISSN (Print)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online
Period9/27/219/30/21

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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