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Xuhang Ying, Jincheng Zhang, Lichao Yan Guanglin Zhang, Minghua Chen Ranveer Chandra Exploring Indoor White Spaces in Metropolises.

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Presentation on theme: "Xuhang Ying, Jincheng Zhang, Lichao Yan Guanglin Zhang, Minghua Chen Ranveer Chandra Exploring Indoor White Spaces in Metropolises."— Presentation transcript:

1 Xuhang Ying, Jincheng Zhang, Lichao Yan Guanglin Zhang, Minghua Chen Ranveer Chandra Exploring Indoor White Spaces in Metropolises

2 Skyrocketing Wireless Data Demand 2 Source: Cisco VNI Global Mobile Data Traffic Forecast,

3 A Vision: Improve Spectrum Utilization to Satisfy the Growing Demand Most spectrum are licensed but underutilized 3 Spectrum Occupancy 15%

4 A Trend: Explore TV White Spaces 4 White Spaces are unoccupied TV channels – FCC allows unlicensed devices to operate in white spaces (2008, 2010) TV White Space dbm Frequency White spaces 470 MHz800 MHz

5 0 MHz 7000 MHz TV ISM (Wi-Fi) TV White Space Networking Scenario Signal Strength Frequency Signal Strength Vacant Spectrum up to 3x of g

6 Prior Works and Our Observation 6 MeasurementIdentification 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 7 MeasurementIdentification Medium Access Network Design OutdoorChicago[1, 2], etc. Cabric[7], Murty[10], etc. Yuan[11], Bahl[13], etc. Murty[10], Bahl[13], etc. IndoorThis work afUpcoming 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

8 How much more white spaces are indoor? What are their characteristics?

9 White Space Availability in Hong Kong A Large-scale measurement study in Hong Kong – Outdoor white space ratio: 50% – Indoor white space ratio: 70% 9 Hardware : USRP + Antenna + Laptop Principle TV Station Fill-in TV Station Measurement Location 31 measurement locations

10 Experiment Scenario: – 7 th 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

11 11 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

12 12 TV signal strengths show strong correlation across channels and locations Indoor White Space Correlation

13 How to identify the indoor white spaces?

14 Intuition: Exploiting indoor white space correlation to save sensor cost! 14 Approach False Alarm Rate White Space Loss Rate Total Cost Geo-databaseLowHighLow Outdoor-Sensing-OnlyLowHighLow One-Time-Profiling-OnlyHigh Low Sensor-All-Over-The-PlaceLow High WISER (This work)Low Design Space and Solution Comparison

15 15 Outdoor Sensor Server Indoor Sensor Profiled Location Indoor Positioning System WISER Architecture

16 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

17 17 Compute the proximity matrix Merge two closest clusters Until k clusters Channel-Location Clustering

18 18 Channel 3,4 Channel 1,2 Compute the proximity matrix Merge two closest channel clusters Repeat procedure for simple case Channel-Location Clustering

19 How well does WISER work?

20 WISER Experimentation WISER identifies 30%-50% more indoor white space as compared to baseline approaches. 20 Implement a WISER prototype on the 7 th 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 21 How Many Indoor Sensors is Enough? Balance between system performance and the total sensor cost

22 Conclusions 22 MeasurementIdentification Medium Access Network Design OutdoorChicago[1, 2], etc. Cabric[7], Murty[10], etc. Yuan[11], Bahl[13], etc. Murty[10], Bahl[13], etc. IndoorThis work afUpcoming 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 23

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

25 Thank you! Jincheng Zhang


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