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

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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

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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

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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

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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

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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?

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