Microsoft Spectr rum Observatory Ranveer Chandra Microsoft Research - - PowerPoint PPT Presentation

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Microsoft Spectr rum Observatory Ranveer Chandra Microsoft Research - - PowerPoint PPT Presentation

Nov 16, 2023 247 likes •581 views

Microsoft Spectr rum Observatory Ranveer Chandra Microsoft Research Joint work with Techn nology & Policy Group p Gupta, Jason van Eaton, Matt Valerio, P Paul Garnett, Paul Mitchell, David Tennenh owing Demand owing Demand 24 HOURS 20X -

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SLIDE 1

Microsoft Spectr

Ranveer Microsoft Joint work with Techn p Gupta, Jason van Eaton, Matt Valerio, P

rum Observatory

Chandra Research nology & Policy Group Paul Garnett, Paul Mitchell, David Tennenh

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SLIDE 2
  • wing Demand
  • wing Demand

20X - 40X

OVER THE NEXT

FIVE YEARS

24 HOURS

UPLOADED EVERY

60 SECONDS FIVE YEARS 60 SECONDS

*See Ericsson Press Release, quoting its President and Chief Executive Officer Hans Vestberg, April 13, 2010, available at

http://www.ericsson.com/thecompany/press/releases/2010/04/1403231 **. Federal Communications Commission, Staff Technical Paper, Mobile Broadband: The Benefits of Additional Spectrum, OB Technical Paper No. 6 (Oct. 2010).

50 BILLION

CONNECTED DEVICES

BY 2020

35X

2009 LEVELS

BY 2014 BY 2020 BY 2014

BI

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SLIDE 3
  • wing Demand
  • wing Demand

20X - 40X

OVER THE NEXT

FIVE YEARS

24 HOURS

UPLOADED EVERY

60 SECONDS FIVE YEARS 60 SECONDS

*See Ericsson Press Release, quoting its President and Chief Executive Officer Hans Vestberg, April 13, 2010, available at

http://www.ericsson.com/thecompany/press/releases/2010/04/1403231 **. Federal Communications Commission, Staff Technical Paper, Mobile Broadband: The Benefits of Additional Spectrum, OB Technical Paper No. 6 (Oct. 2010).

50 BILLION

CONNECTED DEVICES

BY 2020

35X

2009 LEVELS

BY 2014 BY 2020 BY 2014

Industry Forecasts of Mobile Data Traffic

45X 50X 15X 20X 25X 30X 35X 40X 45X fic Relative to 2009 Cisco Coda Yankee Group Average BI 0X 5X 10X 15X 2009 2010 2011 2012 2013 2014 Traff

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SLIDE 4

ectrum Allocation in ectrum Allocation in the US the US

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SLIDE 5

contrast... contrast...

Large portions of spectrum is unu Large portions of spectrum is unu utilized utilized

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SLIDE 6

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency q y

cess (DSA) cess (DSA)

PU4

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SLIDE 7

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency

  • Determine available spe

q y

cess (DSA) cess (DSA)

PU4

ectrum (white spaces)

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SLIDE 8

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency

  • Determine available spe
  • Transmit in “available fre

q y

cess (DSA) cess (DSA)

PU4

ectrum (white spaces) equencies”

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SLIDE 9

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency

  • Determine available spe
  • Transmit in “available fre

q y

  • Detect if primary user a

cess (DSA) cess (DSA)

PU4

ectrum (white spaces) equencies” appears

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SLIDE 10

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency

  • Determine available spe
  • Transmit in “available fre

q y

  • Detect if primary user a
  • Move to new frequencie

cess (DSA) cess (DSA)

PU4

ectrum (white spaces) equencies” appears es

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SLIDE 11

namic Spectrum Acc namic Spectrum Acc

PU1 PU3 Power PU2 P Frequency

  • Determine available spe
  • Transmit in “available fre

q y

  • Detect if primary user a
  • Move to new frequencie
  • Adapt bandwidth and p
  • Adapt bandwidth and p

cess (DSA) cess (DSA)

PU4

ectrum (white spaces) equencies” appears es

  • wer levels
  • wer levels
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SLIDE 12

SR KNOWS Program SR KNOWS Program

1: Ad hoc networking in TV white 1: Ad hoc networking in TV white

Capable of sensing TV signals, hardware fun

2 I f t t b d t ki 2: Infrastructure based networkin

Capable of sensing TV signals & microphone

3: Campus-wide WhiteFi network

Deployed on campus, and provide coverage

4: White spaces beyond TV spect

Spectrum measurements to identify addition Spectrum measurements to identify addition

m (2005 – …) m (2005 …)

e spaces

DySPAN 2007, MobiHoc 2007, LANMAN

e spaces

nctionality

(Whit Fi)

DySPAN 2007, MobiHoc 2007, LANMAN

ng(WhiteFi)

es, deployed in lab

SIGCOMM 2008, SIGCOMM 2009 (Best

k + geolocation

e in MS Shuttles

trum

nal white spaces

DySPAN 2010 (Top 3 paper), CoNEXT 2011 (Top

nal white spaces

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SLIDE 13
  • blem Statement

For a given region, which spectru For a given region, which spectru networks? m bands are best to form DSA m bands are best to form DSA

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SLIDE 14
  • blem Statement

For a given region, which spectru For a given region, which spectru networks?

Industrial/ Military Radars Industrial/ NASA Deep GPS Radio‐navigation Aviation Maritime Mobile Satellite Services Diverse set of primary licen h schemes, covera

m bands are best to form DSA m bands are best to form DSA

/Business Radio /Business Radio p Space Network Fixed Service Satellites e

….

nsees, different transmission i t age regions etc…

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SLIDE 15

ur Approach ur Approach

Mi Mic Clou <$5,000 Academics ft Regulators White Space devices Users crosoft ud Service Wireless Operators …

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SLIDE 16

ur Approach ur Approach

M A C $ <$5,000 Microsoft Azure loud Service

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SLIDE 17

ur Approach ur Approach

M A C $ <$5,000 Local Station

DATA Control

Local Processing: Averaging, Sampling, Feature extraction Microsoft Azure loud Service

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SLIDE 18

ur Approach ur Approach

M A C $ <$5,000 Local Station

DATA Control

Local Processing: Averaging, Sampling, Feature extraction Microsoft Azure loud Service

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SLIDE 19

ur Approach ur Approach

M A C $ <$5,000 Local Station

DATA Control

Local Processing: Averaging, Sampling, Feature extraction Microsoft Azure loud Service

Policy Makers DSA Users Re

  • Real‐time/History Occu
  • User Signal Feature

Visualize Cmds

End User End User

  • User Signal Feature
  • Other Information…
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SLIDE 20

r Approach r Approach

Fixed Spectrum Analyzer Measurements Mobile Spectrum Measurements Spectru FCC Spectrum hb d Dashboard um Occupancy

Smart Visualizations

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SLIDE 21

r Approach r Approach

Fixed Spectrum Analyzer Measurements Mobile Spectrum Measurements Spectru FCC Spectrum hb d Dashboard um Occupancy

Smart Visualizations

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SLIDE 22

mo mo

http://observatory.microsoftspect http://observatory.microsoftspect trum.com trum.com

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SLIDE 23

e cases e cases

Policy makers: Policy makers:

  • Identify unused portions of spectru
  • Detect rogue transmissions

g

  • Identify locations of transmitters (in

White space devices:

  • Dynamically consult with database t

Academics/Researchers:

  • Modeling the real world

m, i.e. good for DSA developing countries) to decide spectrum for communicatio

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SLIDE 24

ata-driven DSA: Syste ata driven DSA: Syste em Design em Design

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SLIDE 25

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier

em Design em Design

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SLIDE 26

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier

em Design em Design

  • Transmitter periodicity
  • Transmitter bandwidth
  • Mobile transmitters ov

DSA Database DSA Database

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SLIDE 27

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier Second

em Design em Design

  • Transmitter periodicity
  • Transmitter bandwidth
  • Mobile transmitters ov

DSA Database DSA Database dary user

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SLIDE 28

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier Second

em Design em Design

  • Transmitter periodicity
  • Transmitter bandwidth
  • Mobile transmitters ov

DSA Database DSA Database

Frequency?

dary user

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SLIDE 29

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier Second

em Design em Design

  • Transmitter periodicity
  • Transmitter bandwidth
  • Mobile transmitters ov

DSA Database DSA Database

Frequency?

  • Freq. range X
  • Freq. range Y
  • Freq. range Z

dary user

  • Freq. range Z
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SLIDE 30

ata-driven DSA: Syste ata driven DSA: Syste

Transmitter Identifier Transmitter Identifier

Frequency X requ Transmit

Second

em Design em Design

  • Transmitter periodicity
  • Transmitter bandwidth
  • Mobile transmitters ov

DSA Database DSA Database

Frequency?

  • Freq. range X
  • Freq. range Y
  • Freq. range Z

satisfies application uirements. at frequency X.

dary user

  • Freq. range Z

q y

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SLIDE 31

mmary mmary

http://observatory.microsoftspectru p // y p

  • 6+ active stations, many more in the fu
  • Please contact us if you are willing to hos
  • Exciting announcement coming next we

Exciting announcement coming next we

Ongoing Research

  • Fast scanning algorithms (with MIT)
  • Detecting unknown transmitters (with U
  • Space-Time-Frequency occupancy usin

Space Time Frequency occupancy usin IIT-Delhi)

  • DSA metric to identify spectrum most s
  • …

m.com

uture

st a station, or interested in analysis

eek at WSRD workshop! eek at WSRD workshop! UCSB) ng mobile spectrum measurements (with ng mobile spectrum measurements (with suitable for DSA (with Stanford)