1. SpecSense: Crowdsensing for Efficient Querying of Spectrum Occupancy
Md. Shaifur Rahman, Ayon Charkraborty, Himanshu Gupta and Samir R. Das
2017

2. Outline Spectrum Occupancy Query Interpolation Sensor Selection
System Architecture & Benchmarking
Conclusion
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3. Spectrum Occupancy Query
Goal: Build infrastructure to respond to spectrum occupancy query
Spectrum occupancy query: Is channel f at location (x, y) in use?
Why?
To identify spectrum sharing opportunities
Can help in spectrum patrolling
Deeper understanding of spectrum usage for policy formulation
How?: Crowdsensing
Enables large-scale monitoring via many low-cost low-power sensors.
Incentive mechanism.
Goal is to build infra,,,,, to respond to this query
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4. Spectrum Sensing Trade-off
VS.
High Accuracy Expensive Sensor
ThinkRF Realtime Spectrum Analyzer
Low Accuracy Inexpensive Sensor
RTL-Dongle connected to Cellphone
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5. Spectrum Sensing Trade-off
Challenge: Cost of large-scale deployment of spectrum sensors
For a given budget:
Small no. of high-accuracy expensive sensors Or
Large no. of low-accuracy inexpensive sensors?
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6. Estimated signal value at (x, y)
High Level Overview
Sensor
SpecSense System
Sensor Selection
Interpolation
Query Location
Label sensors, query…..not occupied => signal values
Query for
Channel: f
Location: (x, y)
Estimated signal value at (x, y)
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7. Outline Spectrum Occupancy Query Interpolation Sensor Selection
System Architecture & Benchmarking
Conclusion
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8. Interpolation Techniques
Inverse Distance Weighting (IDW): Predicted value = distance-weighted average of neighboring values
Ordinary Kriging (OK):
Takes into accounts structure of the spatial correlation via a “Variogram” function
Predicted value = a weighted average of neighboring values, where the weights come from the variogram
Minimizes the prediction variance
In this work, we improve OK for our context in two ways:
Detrending
Partitioning
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9. Interpolation: Ordinary Kriging
Predicted value = weighted average of neighboring values
Considers spatial correlation
Distance: ~ 1 meter
Difference: ~ 1 unit
Distance: ~ 1 meter
Difference: ~ 1 unit
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10. Variogram Y
(30, 625) 30 15
x13 = 50 3
yij 1
(50, 225)
x12 = 20
x23 = 65 2
(20, 100)
y12 = (15 – 5)2 = 100 5
y13 = (30 – 15)2 = 225
y23 = (30 – 5)2 = 625
(0, 0) h X
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11. OK Interpolation Z4 = λ1.Z1+ λ2.Z2+ λ3.Z3 = 15λ1+5λ2+30λ3
Find λ1, λ2, λ3 such that:
λ1+λ2+λ3 = 1
Expected prediction error = 0
Expected prediction variance is minimum
λ1 λ2 λ3 α = 𝑦11 𝑦12 𝑦13 1 𝑦21 𝑦22 𝑦23 1 𝑦31 𝑦32 𝑦 −1 𝑦14 𝑦24 𝑦34 1
Z4 = 15(0.2)+5(0.15)+30(0.65) = 23.25 30 15 3 1
x14 = 50
x34 = 20 4 2
x24 = 45 ? 5
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12. Improving OK by Detrending
OK requires mean of signal values to be constant across all locations
But that is not the case in our context
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13. Improving OK by Detrending
Solution: Decompose sensed signal values
Signal value = Path-loss + Shadowing
Path-loss Estimation
Assume TX at the sensor with max. signal
Estimate path loss exponent α at sensors
Estimate path-loss at query using α.
Has zero-mean
Use OK interpolation
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14. Improving Ordinary Kriging by Partitioning
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15. Improving Ordinary Kriging by Partitioning
Issue with OK: It assumes that “variance” depends only on distance. Not true in our context (i.e. signal variance depends on terrain)
Solution: Partition the region into sub-region such that:
each sub-region has similar path-loss characteristics
Algorithm (OK with Partitioning):
Divide the region into small cells
Initially, each cell is a separate sub-region
Iteratively merge neighboring sub-regions with similar estimated path-loss exponent.
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16. Results: Kriging Improvement
Dataset
Sensor: RTL-SDR connected to smartphones
Spectrum: AT&T’s LTE Downlink
1500 locations
Spanning indoors and outdoors
15000 m^2 area
Change axis title
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17. Outline Spectrum Occupancy Query Interpolation Sensor Selection
System Architecture & Benchmarking
Conclusion
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18. Sensor Selection
Problem: Given locations of sensors and potential queries, select at most k sensors to minimize “interpolation prediction error”
NP-hard.
Two algorithms:
Nearest Query Cover: In each iteration, for each query, select the closest remaining sensor.
Iterative Query Cover: In each iteration, select the sensor that “covers” most number of queries. Here, “coverage” is based on the variogram.
Above generalize to weighted versions (e.g., weight may be battery-level, precision etc. )
Diagram to ILLUSTRATE the DIFFERENCE between the two algorithms.
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19. Results: Sensor Selection
Pvg prior work..
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20. System Architecture
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21. Results: Benchmarking
Requirements:
Sensing + Query Latency
has to be small enough to serve in real time
Sensing Latency: Very low latency even for high FFT size
Query Latency: Negligible compared to sensing latency
MOTIVATION: …
Sensing Latency
Query Latency
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22. Conclusion Crowdsensing based large scale deployment
Address two problems- interpolation & sensor selection
Improvement of interpolation by detrending and partitioning
Performance comparison on real varied dataset
Benchmarking of the implemented system
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23. End of Presentation
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