xuhang ying, jincheng zhang, lichao yan guanglin zhang, minghua chen ranveer chandra exploring...
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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
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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
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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
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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
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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
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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
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