Towards Commoditized Real -time Spectrum Monitoring

1. Towards Commoditized Real-time Spectrum Monitoring Ana Nika, Zengbin Zhang, Xia Zhou*, Ben Y. Zhao and HaitaoZheng Department of Computer Science, UC Santa Barbara *Department of Computer Science, Dartmouth College

2. 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?

3. 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

4. Our Approach: Real-time, CrowdsourcedSpectrum 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

5. Outline • Introduction • Spectrum Monitoring System • Crowdsourced Framework • Commoditized Platform • Feasibility Results • Additional Challenges

6. CrowdsourcedMeasurement 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

7. 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

8. 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?

9. Impact of Sensing Sensitivity

10. Noise Measurements RTL-SDR based platforms report higher noise variance With sensing duration ≥1ms,RTL-SDR based platforms perform similarly to USRP

11. 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

12. 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

13. 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

14. Impact of Sensing Bandwidth

15. Scanning Delay • 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

16. Impact on Spectrum Monitoring • RTL-SDR/smartphone achieves <10% detection error (for 24MHz band) • As the band becomes wider (120MHz), error rate can reach 35%

17. 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

18. Remaining ChallengesCoverage • 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

19. Remaining ChallengesMeasurement 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

20. Remaining ChallengesMeasurement 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 Kalmanfilters

21. Thank you!Questions?