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Exploring Indoor White Spaces in Metropolises

  • Xuhang YING
  • , Jincheng ZHANG
  • , Lichao YAN
  • , Yu CHEN
  • , Guanglin ZHANG
  • , Minghua CHEN
  • , Ranveer CHANDRA

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

It is a promising vision to exploit white spaces, that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering that 70% of the spectrum demand comes from indoor environment, we carry out a comprehensive study to explore indoor white spaces. We first conduct a large-scale measurement study and compare outdoor and indoor TV spectrum occupancy at 30+ diverse locations in a typical metropolis-Hong Kong. Our results show that abundant white spaces are available in different areas in Hong Kong, which account for more than 50% and 70% of the entire TV spectrum in outdoor and indoor scenarios, respectively. Although there are substantially more white spaces indoors than outdoors, there have been very few solutions for identifying indoor white space. To fill in this gap, we develop the first data-driven, low-cost indoor white space identification system for White-space Indoor Spectrum EnhanceR (WISER), to allow secondary users to identify white spaces for communication without sensing the spectrum themselves. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-40% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.
Original languageEnglish
Article number9
JournalACM Transactions on Intelligent Systems and Technology
Volume9
Issue number1
Online publishedAug 2017
DOIs
Publication statusPublished - Oct 2017
Externally publishedYes

Research Keywords

  • Clustering algorithms
  • Sensor placement
  • Tv white spaces

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