Xuhang Ying, Jincheng Zhang, Lichao YanGuanglin Zhang, Minghua Chen

Ranveer Chandra

Exploring Indoor White Spaces in Metropolises

2

Skyrocketing Wireless Data Demand

Source: Cisco VNI Global Mobile Data Traffic Forecast, 2012-2017

3

A Vision: Improve Spectrum Utilization to Satisfy the Growing Demand

□ Most spectrum are licensed but underutilizedSpectrum Occupancy

15%

4

A Trend: Explore TV White Spaces

□ “White Spaces” are unoccupied TV channels– FCC allows unlicensed devices to operate in white

spaces (2008, 2010)

TV “White Space”

dbm

Frequency

-60

-100

“White spaces”

470 MHz 800 MHz

0 MHz

7000MHz

TV ISM (Wi-Fi)

700470 2400 51802500 530054-90 174-216

5

TV White Space Networking ScenarioSi

gnal

Str

engt

h

Frequency FrequencySi

gnal

Str

engt

h

Vacant Spectrumup to 3x of 802.11g

6

Prior Works and Our Observation

Measurement Identification Medium Access

Network Design

Outdoor

Chicago [1, 2], Singapore [3], Guangzhou [4],UK [5], Europe [6], etc.

Cabric [7], Kim [8, 9],Murty [10], etc.

Yuan [11]Borth [12]Bahl [13], etc.

Murty [10],Borth [12],Bahl [13],Feng [14], etc.

Indoor ? ? 802.11af ?

□ More than 70% of data demand comes from indoors[15]

□ Most people are indoors 80% of the time[16]

7

Our Contributions

Measurement Identification Medium Access

Network Design

Outdoor Chicago[1, 2], etc.

Cabric[7], Murty[10], etc.

Yuan[11],Bahl[13], etc.

Murty[10],Bahl[13], etc.

Indoor This work This work 802.11af Upcoming

First large scale measurement in metropolises• 50% and 70% of the TV spectrum are

white spaces in outdoors and indoors

WISER design and proto-typing• Data-driven design• WISER prototype identifies 30%~50%

more indoor white spaces compared with alternative approaches

WISER – White-space Indoor Spectrum EnhanceR

How much more white spaces are indoor?

What are their characteristics?

White Space Availability in Hong Kong

□ A Large-scale measurement study in Hong Kong– Outdoor white space

ratio: 50%– Indoor white space

ratio: 70%

9Hardware : USRP + Antenna + Laptop

Principle TV StationFill-in TV StationMeasurement Location

31 measurement locations

□ Experiment Scenario:– 7th floor of a 10-floor office building– 65 measurement locations (cover all rooms and corridors)

□ Measurement– Across four months– One time profiling every day– Record the signal strengths for all channels at all locations

10

Indoor White Space Measurement

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Indoor white spaces show spatial variation – single location

sensing is not enough

Indoor white spaces are long-term unstable – one time

profiling is not enough

Indoor White Space Characteristics

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TV signal strengths show strong correlation across channels and locations

Indoor White Space Correlation

How to identify the indoor white spaces?

Intuition: Exploiting indoor white space correlation to save sensor cost!

14

Approach False Alarm Rate

White Space Loss Rate

Total Cost

Geo-database Low High Low

Outdoor-Sensing-Only Low High Low

One-Time-Profiling-Only High High Low

Sensor-All-Over-The-Place Low Low High

WISER (This work) Low Low Low

Design Space and Solution Comparison

15

Outdoor Sensor

Server

Indoor SensorProfiled Location

Indoor Positioning

System

WISER Architecture

16

Given k sensors to be placed, where are the best locations to place them?

One-time spectrum profiling

Channel-Location clustering

Indoor sensor placement

Get the signal strengths

Compute Channel- Location clusters

Place one sensor per cluster

Key Challenge: Indoor Sensor Placement

□ Simple Case:– One channel, locations– What we want: channel-location clusters

17

Compute the proximity matrix

Merge two “closest” clusters

Until k clusters

Channel-Location Clustering

□ General Case:– channels, locations– channel clusters, channel-location clusters for channel

cluster

18

Channel 3,4

Channel 1,2

Compute the proximity matrix

Merge two “closest” channel

clusters

Repeat procedure for simple case

Channel-Location Clustering

How well does WISER work?

WISER Experimentation

□ WISER identifies 30%-50% more indoor white space as compared to baseline approaches.

20

□ Implement a WISER prototype on the 7th floor of a campus building– 20 indoor sensors and 1

outdoor sensor– 11 experiments across 4

months– Compare WISER, Outdoor

Sensing (OS-only), and One-Time-Profiling (OTP-Only)

21

How Many Indoor Sensors is Enough?

□ Balance between system performance and the total sensor cost

22

Conclusions

Measurement Identification Medium Access

Network Design

Outdoor Chicago[1, 2], etc.

Cabric[7], Murty[10], etc.

Yuan[11],Bahl[13], etc.

Murty[10],Bahl[13], etc.

Indoor This work This work 802.11af Upcoming

First large scale measurement in metropolises• 50% and 70% of the TV spectrum are

white spaces in outdoors and indoors

WISER design and proto-typing• Data-driven design• WISER prototype identifies 30%~50%

more indoor white spaces compared with alternative approaches

WISER – White-space Indoor Spectrum EnhanceR

23

Future Works

□ More measurements at different buildings□ Extending the single-floor design to multi-floor

design □ Building indoor white space network to utilize

the white spaces□ Extend the solution/idea to other spectrum

bands

24

References[1] M. McHenry et al., “Chicago Spectrum Occupancy Measurements & Analysis and A Long-term Studies Proposal”, ACM TAPAS, 2006. [2] T. Taher et al., “Long-term Spectral Occupancy Findings in Chicago”, IEEE DySPAN, 2011. [3] M. Islam et al., “Spectrum Survey in Singapore: Occupancy Measurements and Analyses”, IEEE CrownCom, 2008. [4] D. Chen et al., “Mining Spectrum Usage Data: A Large-scale Spectrum Measurement Study”, ACM MobiCom, 2009. [5] M. Nekovee et al., “Quantifying the Availability of TV White Spaces for Cognitive Radio Operation in the UK”, IEEE ICC joint workshop on cognitive wireless networks and systems, 2009. [6] V. Jaap et al., “UHF White Space in Europe: A Quantitative Study into the Potential of the 470-790MHz band”, IEEE DySPAN, 2011. [7] D. Cabric et al., “Experimental Study of Spectrum Sensing Based on Energy Detection and Network Cooperation”, ACM TAPAS, 2006. [8] H. Kim et al., “Fast Discovery of Spectrum Opportunities in Cognitive Radio Networks”, IEEE DySPAN, 2008.[9] H. Kim et al., “In-band Spectrum Sensing in Cognitive Radio Networks: Energy Detection or Feature Dection?”, ACM MobiCom, 2008.[10] R. Murty et al., “Senseless: A Database-Driven White Space Network”, IEEE Transactions on Mobile Computing, 2012.[11] Y. Yuan et al., “KNOWS: Kognitiv Networking Over White Spaces”, IEEE DySPAN, 2007.[12] R. Borth et al., “Considerations for Successful Cognitive Radio Systems in US TV White Space”, IEEE DySPAN, 2008. [13] P. Bahl et al., “White Space Networking with Wi-Fi Like Connectivity”, ACM Sigcomm, 2009. [14] X. Feng et al., “Database-Assisted Multi-AP Network on TV White Spaces: Architecture, Spectrum Allocation and AP Discovery”, IEEE DySPAN, 2011. [15] V. Chandrasekhar et al., “Femtocell networks: a survey”, IEEE Communications Magazine, 2008. [16] N. Klepeis et al., “The national human activity pattern survey”, Journal of Exposure Analysis and Environmental Epidemiology, 2001.

Thank you!

Jincheng Zhang (zj012@ie.cuhk.edu.hk)http://personal.ie.cuhk.edu.hk/~zj012