Xing Mei ; Xun Sun ; Mingcai Zhou ; Shaohui Jiao ; Haitao Wang ; Xiaopeng Zhang
Towards Commoditized Real-time Spectrum Monitoring Ana Nika, Zengbin Zhang, Xia Zhou *, Ben Y. Zhao...
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Transcript of Towards Commoditized Real-time Spectrum Monitoring Ana Nika, Zengbin Zhang, Xia Zhou *, Ben Y. Zhao...
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 scale2
Spectrum enforcement: how do we detect and locate unauthorized users?
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
3
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
4
Outline
• Introduction
• Spectrum Monitoring System
• Crowdsourced Framework
• Commoditized Platform
• Feasibility Results
• Additional Challenges
5
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
6
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
7
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? 8
Impact of Sensing Sensitivity
9
Noise Measurements
• RTL-SDR based platforms report higher noise variance• With sensing duration ≥1ms, RTL-SDR based platforms
perform similarly to USRP
10
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
0.2
0.4
0.6
0.8
1
1.2
1.4RTL/laptop
RTL/smartphone
USRP/laptop
Sensing Duration (ms)
Std
dev
of
Receiv
ed
Pow
er
(dB
m)
Signal Measurements
• RTL-SDR platforms report lower SNR values compared to USRP platform
• Smartphone’s microUSB interface does not provide enough power to RTL- SDR radio
11
20 25 30 35 40 4505
101520253035404550 USRP/laptop
RTL/laptopRTL/smartphone+ext. power
Signal SNR (dB)
Re
ceiv
er
SN
R (
dB
)
Impact on Spectrum Monitoring
• 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
12
-5 -0 5 10 15 20 25 300
20
40
60
80
100 RTL/smartphoneRTL/laptopUSRP/laptop
SNR (dB)
Mis
de
tect
ion
Ra
te
(%)
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
devices13
Impact of Sensing Bandwidth
14
Scanning Delay
15
0 50 100 150 200 2500
1
2
3
4RTL/smartphoneUSRP/laptop, 2.4MHzUSRP/laptop, 20MHz
Total Bandwidth (MHz)
Sca
n D
ela
y (
s)
• 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
Impact on Spectrum Monitoring
16
1 2 3 4 5 6 7 8 9 100
10
20
30
40
50 RTL2.4 120MHz
USRP2.4, 120MH
RTL2.4, 24MHz
ON-OFF Period (s)
Dete
ctio
n E
rror
(%)
• RTL-SDR/smartphone achieves <10% detection error (for 24MHz band)
• As the band becomes wider (120MHz), error rate can reach 35%
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
17
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
18
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 19
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
20
Thank you!
Questions?
21