Towards Commoditized
Real-time Spectrum
Monitoring
Ana Nika, Zengbin Zhang, Xia Zhou*,
Ben Y. Zhao and Haitao Zheng
Department of Computer Science, UC Santa Barbara
*Department of Computer Science, Dartmouth College
Spectrum as a Valuable Resource
• Billions of $ spent on spectrum auctions
• Efficient utilization is critical
• Malicious users can “misuse” spectrum without
authorization
• Increasingly feasible via cheaper, smarter hardware
• Active, comprehensive monitoring a necessity and
challenge
• Spectrum usage density will continue to grow
 current monitoring tools do not scale
Spectrum enforcement: how do we detect and locate unauthorized users?
1
Challenges in Spectrum Enforcement
• Coverage
• Large and growing deployments, small/fixed
measurement area
• Abstract models impractical in outdoor settings
• Responsiveness requires “real-time” measurements
• Periodic spectrum scans?
• Offline data processing likely insufficient
• Infrastructure cost and availability
• State of art: bulky, expensive spectrum analyzers
• Alternative: USRP GNU radios
2
Our Approach:
Real-time, Crowdsourced Spectrum Monitoring
• Crowdsourcing measurement platform
• Scales up in coverage and measurement frequency
• Scales with demand/impact
• Higher density usage areas ->
• Low-cost commoditized platform
• Explore replacement of specialized H/W with
commody
• Reduced cost, availability (integrated w/ next gen
phones?)
• Compensate for lower accuracy with redundancy
3
Outline
• Introduction
• Spectrum Monitoring System
• Crowdsourced Framework
• Commoditized Platform
• Feasibility Results
• Additional Challenges
4
Crowdsourced Measurement Framework
• Approach
• Individual users monitor and collect spectrum activities in local
neighborhood
• Submit real-time results in to (centralized) spectrum monitoring
agency
• Agency aggregates/disambiguates consensus monitoring results
5
Commoditized Measurement Platform
• Two hardware components
• Commodity mobile device
(smartphone)
• Cheap & portable Realtek Software
Defined Radio (RTL-SDR)
• RTL-SDR as “spectrum analyzer”
•
•
•
•
DVB-T USB-connected dongle
Frequency range: 52-2200MHz
Max sample rate: 2.4MHz
Cheap: <$20 per device
• Mobile host serves as “data
processor”
• Translates raw data into data stream
6
Key goal:
Evaluate feasibility of SDR platform
• Sensing sensitivity
• 8-bit I/Q samples (vs. USRP @14-bit)  Missing weak
signals
• How significant are errors (relative to alternatives)
• Net impact on event detection?
• Sensing bandwidth
• Up to 2.4MHz bandwidth (vs. USRP @ 20MHz)
• Must sweep wider bands sequentially
• Max frequency of sensing operation?
7
Impact of Sensing Sensitivity
8
Noise Measurements
Stddev of Received Power
(dBm)
1.4
RTL/laptop
1.2
RTL/smartphone
1
USRP/laptop
0.8
0.6
0.4
0.2
0
0
0.5
1
1.5
2
Sensing Duration (ms)
• RTL-SDR based platforms report higher noise variance
• With sensing duration ≥1ms, RTL-SDR based platforms perform
similarly to USRP
9
Receiver SNR (dB)
Signal Measurements
USRP/laptop
RTL/laptop
RTL/smartphone+ext. power
RTL/smartphone
50
45
40
35
30
25
20
15
10
5
0
20
25
30
35
40
45
Signal SNR (dB)
• RTL-SDR platforms report lower SNR values compared to USRP
platform
• Smartphone’s microUSB interface does not provide enough
power to RTL- SDR radio
10
Misdetection Rate (%)
Impact on Spectrum Monitoring
RTL/smartphone
RTL/laptop
USRP/laptop
100
80
60
40
20
0
-5
5
15
25
SNR (dB)
• Signal detection:
• USRP platform, SNR ≥ -2dB
• RTL-SDR/laptop, SNR ≥ 7dB
• RTL-SDR/smartphone, SNR ≥ 10dB
• For 1512MHz band, 12dB difference  ~50% loss in distance
11
Addressing Sensitivity Issues
• Deploy many monitoring devices with crowdsourcing
• Redundant sensors increases probability of nearby
sensor to target transmitters
• Look at specific signal features
• Pilot tones
• Cyclostationary features
• Pro: more reliable than energy readings
• Con: additional complexity on mobile sensing
devices
12
Impact of Sensing Bandwidth
13
Scanning Delay
4
RTL/smartphone
USRP/laptop, 2.4MHz
Scan Delay (s)
3
USRP/laptop, 20MHz
2
1
0
0
50
100
150
200
250
Total Bandwidth (MHz)
• RTL-SDR scan delay is two times higher than USRP (2.4MHz)
because its frequency switching delay is higher
• RTL-SDR radios can finish scanning a 240MHz band within 2s
14
Impact on Spectrum Monitoring
RTL2.4 120MHz
Detection Error (%)
50
USRP2.4, 120MH
40
RTL2.4, 24MHz
30
USRP20, 120MHz
20
USRP2.4, 24MHz
10
0
1
3
5
7
9
ON-OFF Period (s)
• RTL-SDR/smartphone achieves <10% detection error (for 24MHz
band)
• As the band becomes wider (120MHz), error rate can reach 35%
15
Overcoming Bandwidth Limitation
• Leverage crowdsourcing
• either divide wide-band into narrow-bands and
assign users to specific narrow-bands
• aggregate results from multiple users
w/asynchronous scans
• Use novel sensing techniques
• QuickSense
• BigBand
• Challenge: how to realize these sophisticated
algorithms on RTL-SDR/smartphone devices
16
Remaining Challenges
Coverage
• Solution
• Passive measurements from wireless service
provider’s own user population
• On-demand measurements from users of other
networks
• Leverage incentives and on-demand crowdsourcing
model
17
Remaining Challenges
Measurement Overhead
• Spectrum monitoring overhead
• Energy consumption
• Bandwidth usage
• Solution
• Energy consumption: schedule measurements based
on user context, e.g. location, device placement,
movement, etc.
• Bandwidth: secure in-network data aggregation and
compression
18
Remaining Challenges
Measurement Noise
• Accuracy of spectrum monitoring affected by
• Noise into monitoring data
• Potential human operation errors
• Solution
• Expect/model noisy data
• Use models for signal estimation: Gaussian process,
Bayesian and Kalman filters
19
Thank you!
Questions?
20