Exploring Indoor White Spaces in Metropolises

Author:

Ying Xuhang1,Zhang Jincheng2,Yan Lichao2,Chen Yu3ORCID,Zhang Guanglin4,Chen Minghua2,Chandra Ranveer5

Affiliation:

1. University of Washington

2. The Chinese University of Hong Kong

3. University of California, San Diego

4. Donghua University

5. Microsoft Research, Redmond

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.

Funder

University Grants Committee of the Hong Kong Special Administrative Region, China

General Research

National Basic Research Program of China

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Theoretical Computer Science

Reference48 articles.

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