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?

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

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

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

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

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

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?

Impact of Sensing Sensitivity

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

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

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

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

Impact of Sensing Bandwidth

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

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%

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

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

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

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

Thank you! Questions?