SpecNet: Spectrum Sensing Sans Frontières

SpecNet: Spectrum Sensing Sans Frontières

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Anand Iyer

April 01, 2011
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  1. 1.

    SpecNet: Spectrum Sensing Sans Frontières Anand Iyer*, Krishna Chintalapudi*, Vishnu

    Navda*, Ramachandran Ramjee*, Venkata N. Padmanabhan* and Chandra R. Murthy+ *Microsoft Research India +Indian Institute of Science
  2. 2.

    • McHenry “NSF Spectrum Occupancy Measurement Project Summary” - Average

    occupancy ~5.2% in 30MHz – 3GHz • McHenry et.al. “Chicago Spectrum Occupancy Measurements & Analysis” [TAPAS 2006] - 17% occupancy in Chicago, 13% in New York • China [MobiCom 2009], Singapore [CrownCom 2008], Germany, New Zealand, Spain… Spectrum Measurement Studies 2
  3. 3.

    • McHenry “NSF Spectrum Occupancy Measurement Project Summary” - Average

    occupancy ~5.2% in 30MHz – 3GHz • McHenry et.al. “Chicago Spectrum Occupancy Measurements & Analysis” [TAPAS 2006] - 17% occupancy in Chicago, 13% in New York • China [MobiCom 2009], Singapore [CrownCom 2008], Germany, New Zealand, Spain… Spectrum Measurement Studies Spectrum heavily underutilized 3 FM TV GSM CDMA Spectrum Occupancy in Bangalore, India
  4. 4.

    Impact Nov 4, 2008: FCC voted 5-0 to approve Opportunistic

    Spectrum Access (OSA) in licensed bands Sep 23, 2010: FCC determines final rules for the use of whitespaces. Removes mandatory sensing requirement 4
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    • Studies conducted only at a handful of locations -

    Till date, only the US has allowed OSA • Represent static spectrum occupancy - Future OSA devices may require dynamic spatio-temporal occupancy information • Through evaluation of OSA proposals from the research community is hard - Little or no access to real-world data from cross-geographic locations However… 5
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    • Studies conducted only at a handful of locations -

    Till date, only the US has allowed OSA • Represent static spectrum occupancy - Future OSA devices may require dynamic spatio-temporal occupancy information • Through evaluation of OSA proposals from the research community is hard - Little or no access to real-world data from cross-geographic locations However… 6 No infrastructure for measuring real-time spectrum occupancy across vast regions
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    Remote User Spectrum Analyzer “A first-of-its-kind platform that allows spectrum

    analyzers around the world to be networked and efficiently used in a coordinated manner for spectrum measurement as well as implementation and evaluation of distributed sensing applications” SpecNet 7
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    SpecNet Conduct remote spectrum measurements Construction & maintenance of spatio-temporal

    usage maps Deploy & evaluate real-time distributed sensing applications 8
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    9 Challenges • Expensive ($10K - $40K) • Limited availability

    • Support user demands • Applications require quick detection Complete tasks in minimal time
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    • Motivation • SpecNet – Architecture – Components – Programmability

    • Spectrum Analyzer Primer • Key Challenge – Resource Management • Applications Overview 10
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    SpecNet Operation Master Server Slave Servers import xmlrpclib; APIServer =

    xmlrpclib.ServerProxy(http://bit.ly/Sp ecNetAPI, allow_none=True); devices = APIServer.GetDevices(None, None); Users Low-level GetDevices ReserveDevices RunCommandOnDevice High-level GetOccupancy GetPowerSpectrum FindPowerAtLocation LocalizeTransmitter 11
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    Programmability • Sophisticated Users – ReserveDevices – RunCommandOnDevice • Policy

    Users – GetPowerSpectrumHistory – GetOccupancyHistory • Others (E.g. network operators) – LocalizeTransmitter – FindPowerAtLocation – GetPowerSpectrum – GetOccupancy
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    • Used to measure the spectral composition of waveforms •

    Frequency span (Q) and Resolution Bandwidth (RBW, ρ) Spectrum Analyzer Primer -120.00 -110.00 -100.00 -90.00 -80.00 -70.00 -60.00 -50.00 -40.00 702 702.1 702.2 702.3 702.4 Received Signal Power (dBm) Frequency (MHz) 1MHz 30KHz 10KHz 1KHz 15 Noise Floor
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    • Used to measure the spectral composition of waveforms •

    Frequency span (Q) and Resolution Bandwidth (RBW, ρ) Spectrum Analyzer Primer -120.00 -110.00 -100.00 -90.00 -80.00 -70.00 -60.00 -50.00 -40.00 702 702.1 702.2 702.3 702.4 Received Signal Power (dBm) Frequency (MHz) 1MHz 30KHz 10KHz 1KHz 16 Noise Floor Lowering RBW reveals details about the signal, and lowers noise floor
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    Spectrum Analyzer Primer • Often users are interested in determining

    which parts of the spectrum are in use. - Distinguish between signal and noise 17
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    Spectrum Analyzer Primer • Often users are interested in determining

    which parts of the spectrum are in use. - Distinguish between signal and noise Lowering noise floor helps in reliably detecting transmissions 18
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    Spectrum Analyzer Primer • Noise floor determines the detection range

    of a spectrum analyzer 19 d ) log( 10 0 d P P d    Lowering noise floor helps in detecting transmitters farther away
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    • Motivation • SpecNet – Architecture – Components – Programmability

    • Spectrum Analyzer Primer • Key Challenge – Resource Management – When multiple devices are available, how should the scanning task be scheduled? • Applications Overview 20
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    • Depends on Frequency Span (Q) and RBW (ρ) •

    Linear dependency on span, ∝ Scan Time 0 2 4 6 8 10 12 0 10 20 30 40 50 60 Time to Scan (s) Frequency Span (MHz) Analyzer 1, RBW=3KHz Analyzer 1, RBW=1KHz Analyzer 2, RBW=3KHz Analyzer 2, RBW=1KHz 21
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    • In theory inversely proportional to RBW, ∝ 1 Scan

    Time 0.01 0.1 1 10 100 1 10 100 1000 10000 100000 1000000 Time to scan (s) Resolution Bandwidth (Hz) Analyzer 1 Analyzer 2 Analyzer 3 In practice… piece-wise linear! 22
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    a. Spectral Load Sharing 1 and 2 split the frequency

    span among themselves If is the minimum scanning time per MHz for = max 1 1 , 2 2 1 ∶ 2 = 1 1 : 1 2 1 2 23
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    SpecNet uses a numerical approximation to Voronoi partitioning b. Geographical

    Load Sharing 1 2 1 and 2 partition the region of interest 25
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    SpecNet uses a numerical approximation to Voronoi partitioning b. Geographical

    Load Sharing 1 2 1 and 2 partition the region of interest Scan time depends on detection range as: ∝ T decreases super-linearly 26
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    • Motivation • SpecNet – Architecture – Components – Programmability

    • Spectrum Analyzer Primer • Key Challenge – Resource Management • Applications – Remote Measurements – Primary Coverage Estimation – Spectrum Cop Overview 34
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    #1. Doing Simple Scans GetDevices([lat,lng,r]) GetPowerSpectrum(device_id,Fs,Fe,Nf) (Lat, Lng) r •

    SpecNet maps the required noise floor to the resolution bandwidth • Schedules scan tasks at each analyzer • Runs the job and returns the results GetDevices([lat,lng,r]) GetPowerSpectrum(device_id,Fs,Fe,Nf) 35
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    #2. Spectrum Cop • Quickly detect violators - Simplicity in

    writing complex real-time sensing applications requiring coordination  Use GetOccupancy to get an occupancy list in the desired frequency span  For each occupied frequency band, do finer scans using GetPowerSpectrum by setting a lower RBW,  Feed the results to LocalizeTransmitter to locate the transmitter. 39
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    #2. Spectrum Cop • Quickly detect violators - Simplicity in

    writing complex real-time sensing applications requiring coordination 40
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    Limitations 41 • Benefit to owners – Expensive devices •

    Attenuation – 5-20 dB attenuation due to buildings • Privacy/Security concerns – Fine-grained traffic monitoring/user-tracking not possible
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    Conclusion • FCC ruling has spurred tremendous interest, both in

    academia and industry • Key requirement is a measurement infrastructure that provides real data • SpecNet fulfills this need by enabling a geographically distributed spectrum analyzer network SpecNet requests your participation! Please contact Anand Iyer (v-anandi@microsoft.com) or Krishna Chintalapudi (krchinta@microsoft.com) http://bit.ly/SpecNet 42