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Report
A Wireless Spectrum Analyzer in Your Pocket
Tan Zhang, Ashish Patro, Ning Leng, Suman Banerjee
University of Wisconsin-Madison
Tan Zhang / Snoopy / HotMobile 2015
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What is Spectrum Sensing?
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Power (dBm)
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Frequency (MHz)
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Application of Spectrum Sensing
Whitespace spectrum
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Power (dBm)
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Frequency (MHz)
Spectrum Vacancy
Tan Zhang / Snoopy / HotMobile 2015
Network Diagnosis
Device Management
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Problem of A Cathedral Approach
Spectrum Analyzer
Cumbersome
Expensive
Sophisticated
(up to 30kg)
(10k – 50k)
(RF/IF gain, filter bw)
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Opportunity of Mobile Phone Sensing
Can we use smartphones and tablets for spectrum sensing?
Compact
Tan Zhang / Snoopy / HotMobile 2015
Cheap
Easy to use
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Opportunity of Mobile Phone Sensing
Spatial
Distribution
Androidat
and
IPhonescale
devices
Enable
spectrumofanalytics
massive
New York
Tan Zhang / Snoopy / HotMobile 2015
Los Angeles
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Opportunity of Mobile Phone Sensing
Airshark
WiFi
Chipset
Power
• Approach 1 – leverage subcarrier energy sample from
built-in WiFi chipsets, e.g., Atheros 92xx
Subcarrier
Frequency
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Opportunity of Mobile Phone Sensing
• Approach 2 – attach customized analyzer dongle
WiSpy (WiFi band)
RTL-SDR (TV band)
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Limitation of Existing Mobile Phone Sensing
• Narrow frequency range
– WiFi or TV band
• Low spectrum resolution
– 64 subcarriers over a 20MHz band
– 1000 times worse than spectrum analyzer
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Goal
Develop hardware attachment and signal processing
technique to enhance spectrum sensing on mobile devices
Spectrum Knowledge out of Your Pocket
Snoopy
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Outline
• Smartphone based sensing platform – Snoopy
– Frequency translator
– Spectrum sensing on WiFi cards
– Statistical feature based signal detection
• Implementation
• Evaluation
• Future work
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Snoopy Overview
Power
Power
Power
WiFi band
FFTs
Frequency
(2.4GHz)
Antenna
Tan Zhang / Snoopy / HotMobile 2015
Signal Type, Power
Frequency
Translator
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Frequency Translator
Tune
to let
Input
Output
Frequency Mixer
Frequency
Synthesizer
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Spectrum Collection on WiFi Cards
• Recent 802.11 WiFi chipsets expose subcarrier
energy samples
– e.g., Intel 5300, Atheros 92xx and 93xx
Metric
Performance
Capture bandwidth
20/40MHz
Capture delay
120us
Spectrum resolution
312KHz
Challenging to determine vacant spectrum
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Challenge of Determining Whitespaces
from WiFi Spectrum Scan
Pilot
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Frequency (MHz)
Frequency (MHz)
Noise fluctuation reduce
peak detection accuracy
WiFi Scan
Pilot
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Power (dBm)
Power (dBm)
-70
-90
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Frequency (MHz)
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Noise (whitespace)
Need to detect TV Tone
and wireless microphone
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FFT
Microphone
Power (dBm)
-100
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Tone
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Power (dBm)
Power (dBm)
-90
Spectrum Analyzer
Power (dBm)
-90dBm power
FFT TV
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Frequency (MHz)
Tan Zhang / Snoopy / HotMobile 2015
Frequency (MHz)
Frequency (MHz)
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Statistical Spectrum Feature
Apply Fourier Transform on spectrum to collect entire shape features
Co-efficient
Coefficients can help signal detection
FFT Index
FFT over FFT
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Statistical Spectrum Feature
-90dBm power
WiFi Scan
Noise (whitespace)
Microphone
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Power (dBm)
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Power (dBm)
Power (dBm)
TV
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578 579 580 581 582 583 584
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518 519 520 521 522 523 524
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Frequency (MHz)
Frequency (MHz)
Use SVM classifier to learn the
importance of individual coefficients
FFT over FFT
Frequency (MHz)
0.4
0.2
Dominant peak
0.3
0.2
0
0
0
2
4
6
8 10 12 14 16 18
FFT Index
Tan Zhang / Snoopy / HotMobile 2015
Constant offset
0.3
0.2
0.1
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0.1
0.4
Co-efficient
Square envelop
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Co-efficient
Co-efficient
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FFT Index
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FFT Index
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Implementation
• Frequency translation hardware
– Wide band digital radio
RF
RF chain
chain 11
RF chain 2
• 30MHz – 7.5GHz frequency range
• 1ms frequency switching delay
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Implementation
• Software
– Patch Ath9k driver to enable spectrum scan in the 2.4GHz
– WiSense – Android based application for WiFi band sensing
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Experiment
• Experiment setup
– Used a Linux based router with an Atheros 9280 card for
spectrum sensing
– Connect Snoopy and a ThinkRF WSA4000 analyzer to the
same antenna
Linux
Router
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Accuracy in Detecting Primary Signals
• Collect spectrum data in 8 UHF channels
• Use a RF attenuator to capture TV and microphone
signals from -50dBm to -90dBm
Mis-detection Rate (%)
25
20
15
Snoopy(statistic)
Snoopy(peak)
Analyzer(statistic)
Analyzer(peak)
1-5% gain of
statistical feature
15%
<10% worse than ThinkRF
10
5
7%
3%
0
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Signal Power (dBm)
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Accuracy of Measuring Channel Power
Absolute Power Error (dB)
• Measure in-band power of 6MHz TV channels
• Calculate absolute power difference between Snoopy
and ThinkRF analyzer in each channel
14
12
10
<4dB median error in
lower UHF band
Higher error due to
translator distortion
8
6
4
2
0
26
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Channel Number
49
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Challenges and Future Work
• Connect off-the-shelf mobile devices
– WiFi dongle with SMA connector
– WiFi repeater to relay translated signal
• Reduce size and cost of translator
– Translator for receiving only
– 0.1 – 4GHz frequency range and $48
TL-WN722N
EST-1W
ZX05 – U432H
• Improve measurement accuracy
– Collaborative sensing with longitudinal measurements
– Leverage phase information from I, Q samples
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Conclusion
 Designed a smartphone based spectrum sensing platform to
enable citizen-contributed spectrum analytics.
 Designed statistical spectrum features to improve signal
detection accuracy from low-resolution spectrum.
 Built driver hooks and an Android application to enable
spectrum sensing on smartphones and tablets.
WiSense: http://research.cs.wisc.edu/wings/projects/wisense/
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WiSense Demo
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Thanks for your attention!
[email protected]
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