SpecNet: Spectrum Sensing Sans Frontires Anand Iyer * , Krishna - - PowerPoint PPT Presentation

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SpecNet: Spectrum Sensing Sans Frontires Anand Iyer * , Krishna - - PowerPoint PPT Presentation

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SpecNet: Spectrum Sensing Sans Frontires Anand Iyer * , Krishna Chintalapudi * , Vishnu Navda * , Ramachandran Ramjee * , Venkata N. Padmanabhan * and Chandra R. Murthy + + Indian Institute of Science * Microsoft Research India Spectrum

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SLIDE 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

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SLIDE 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

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SLIDE 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

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SLIDE 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

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SLIDE 5
  • 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
  • ccupancy 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…

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SLIDE 6
  • 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
  • ccupancy 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…

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No infrastructure for measuring real-time spectrum occupancy across vast regions

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SLIDE 7

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

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SLIDE 8

SpecNet

Conduct remote spectrum measurements Construction & maintenance of spatio-temporal usage maps Deploy & evaluate real-time distributed sensing applications

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SLIDE 9

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Challenges

  • Expensive ($10K - $40K)
  • Limited availability
  • Support user demands
  • Applications require quick detection

Complete tasks in minimal time

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SLIDE 10
  • Motivation
  • SpecNet

– Architecture – Components – Programmability

  • Spectrum Analyzer Primer
  • Key Challenge – Resource Management
  • Applications

Overview

10

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SLIDE 11

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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SLIDE 12

Components

Spectrum Analyzer

DeviceManager

CommunicationManager

Master Server VISA SCPI

Slave Server

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SLIDE 13

Components

CommunicationManager

DatabaseManager

Scheduler ClientManager

Server Engine API Webservice Slave Servers

Users SQL Server

Master Server

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SLIDE 14

Programmability

  • Sophisticated Users

– ReserveDevices – RunCommandOnDevice

  • Policy Users

– GetPowerSpectrumHistory – GetOccupancyHistory

  • Others (E.g. network operators)

– LocalizeTransmitter – FindPowerAtLocation – GetPowerSpectrum – GetOccupancy

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SLIDE 15
  • 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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SLIDE 16
  • 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

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Noise Floor

Lowering RBW reveals details about the signal, and lowers noise floor

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SLIDE 17

Spectrum Analyzer Primer

  • Often users are interested in determining

which parts of the spectrum are in use.

  • Distinguish between signal and noise

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SLIDE 18

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

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SLIDE 19

Spectrum Analyzer Primer

  • Noise floor determines the detection range of

a spectrum analyzer

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d

) log( 10 d P P

d

  

Lowering noise floor helps in detecting transmitters farther away

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SLIDE 20
  • 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

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SLIDE 21
  • Depends on Frequency Span (Q) and RBW (ρ)
  • Linear dependency on span, 𝑈 ∝ 𝑅

Scan Time

2 4 6 8 10 12 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

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SLIDE 22
  • In theory inversely proportional to RBW, 𝑈 ∝ 1

𝜍

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!

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SLIDE 23
  • a. Spectral Load Sharing

𝑇1 and 𝑇2 split the frequency span among themselves

If 𝜐𝑗 is the minimum scanning time per MHz for 𝑇𝑗

𝑈 = max 𝜐1𝑅1, 𝜐2𝑅2 𝑅1 ∶ 𝑅2 = 1

𝜐1 : 1 𝜐2

𝑇1 𝑇2

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SLIDE 24
  • b. Geographical Load Sharing

𝑇1 𝑇2

𝑇1 and 𝑇2 partition the region of interest

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SLIDE 25

SpecNet uses a numerical approximation to Voronoi partitioning

  • b. Geographical Load Sharing

𝑇1 𝑇2

𝑇1 and 𝑇2 partition the region of interest

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SLIDE 26

SpecNet uses a numerical approximation to Voronoi partitioning

  • b. Geographical Load Sharing

𝑇1 𝑇2

𝑇1 and 𝑇2 partition the region of interest

Scan time depends on detection range as:

𝑈 ∝ 𝑒𝛿

T decreases super-linearly

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SLIDE 27
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 28
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 29
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 30
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 31
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 32
  • c. Geo-Spectral Load Sharing

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S2 S1 S3

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SLIDE 33

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Geo-Spectral Performance

Spectral Geographical Geo-Spectral Time to detect (s) 1118 1205 526

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SLIDE 34
  • 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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SLIDE 35

#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)

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SLIDE 36

Remote Measurement Studies

FM Radio GSM

Stony Brook, USA

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SLIDE 37

GSM FM Radio

Remote Measurement Studies

Edinburgh, UK

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SLIDE 38

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Remote Measurement Studies

How does the FM band look like in Bangalore, India NOW?

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SLIDE 39

#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.

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SLIDE 40

#2. Spectrum Cop

  • Quickly detect violators
  • Simplicity in writing complex real-time sensing

applications requiring coordination

40

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SLIDE 41

Limitations

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  • 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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SLIDE 42

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)

  • r Krishna Chintalapudi (krchinta@microsoft.com)

http://bit.ly/SpecNet

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