1. Ayon Chakraborty, Udit Gupta and Samir R. Das WINGS Lab
Benchmarking Resource Usage for Spectrum Sensing on Commodity Mobile Devices
Ayon Chakraborty, Udit Gupta and Samir R. Das
WINGS Lab
ACM HotWireless 2016

2. Building Radio Environment Maps
Estimate the spatial distribution of signal power present in frequency F, a.k.a, Radio Environment Map (REM)
The transmitters operate at a certain frequency F
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3. Building Radio Environment Maps
How?
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4. Building Radio Environment Maps
Need a distributed system of spectrum sensors
Very Few Sensors  Poorer Estimate of the REM
Spectrum Analyzer
Bulky
Expensive
Cannot be Deployed at Scale
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5. Building Radio Environment Maps
More Sensors  Better Estimate of the REM
How About Mobile Spectrum Sensors?
Mobility
Smaller form-factor
Cheaper
Deployed at Scale
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6. Mobile Spectrum Sensor Prototype
Powers Sensing Unit
Computing Device
I/Q Samples
Sensing Unit
Run Signal Detection Algorithms
RTL-SDR
BladeRF
USRP B200
USRP B210
We envision that such sensors will be integrated with the computing device hardware (e.g., smartphone).
Phone
RPi
RPi
RPi
≈ $20
≈ $400
≈ $700
≈ $1200
WINGS Lab

7. Challenges
COTS Samsung Galaxy Phone
Two Main Challenges
Is running spectrum sensing on mobile devices energy efficient?
Is the computational latency incurred in running signal detection algorithms on mobile devices prohibitive?
$40K
Benchmarking such resource consumption will give us better insights about feasibility of mobile spectrum sensors.
≈ $20
RTL-SDR Dongle (cheap spectrum sensor)
WINGS Lab

8. Benchmarking Task Computing Device Sensing Unit Transmitter
Measure Energy
Measure Latency
I/Q Samples
Computing Device
Sensing Unit
Transmitter
Signal Detection
Run Signal Detection Algorithms
Energy Based
Feature Based
Autocorrelation Based
Measure Latency
Measure Energy
Computation
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9. Signal Detection Algorithms
Energy Based
(ATSC TV Signal)
Feature Based (ATSC TV Signal)
Autocorrelation Based (ATSC TV Signal)
Prob. of Detection
Prob. of False Alarm
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10. Result: Latency in Sensing Unit
Change sensing parameters
“Cold Boot”
Changing sensing parameters incurs only a few milliseconds delay, however starting the device can incur two orders of magnitude more delay.
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11. Result: Latency in Computing Device
Raspberry Pi
Phone
Running signal detection algorithms on CPU requires relatively longer time. A GPU-version of the implementation improved latency by a factor of 1.5X – 9X.
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12. Result: Energy Consumption in Sensing Unit
Raspberry Pi
Phone
One minute usage: Energy used in the sensing job is approximately 5X – 6X times lower compared to very typical phone applications.
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13. Takeaways
Mobile spectrum sensing enables crowdsourcing spectrum measurements resulting in more granular estimate of spectrum maps.
Benchmarking results on our prototype platforms with optimization (GPU) show feasibility of such sensors.
Energy consumption on our prototype platform is modest. 5X – 6X lesser than typical smartphone apps.
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14. Thanks! Questions?
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