Download - Tic-tac: TV White Space Testbed with Robust …yueg/PDFs/Tic-tac-EPSRC...References: Tic-tac: TV White Space Testbed with Robust Spectrum Sensing Algorithms for M2M Communications

References:

Tic-tac: TV White Space Testbed with Robust Spectrum Sensing Algorithms for M2M Communications

Antennafor aWSD Client• CompactandLowProfile‒ Dimension:231mm×35mm×0.8mm

• Ultra-wideband‒ Bandwidth:474MHz-1212MHz

• Excellent Performance‒ Radiationpattern:omnidirectional‒ Averagegain:1.68dBioverTVWS‒ Radiationefficiency:over80%

RobustSensingAlgorithms• SpectrumUsage‒ Spectrumshortageandspectrumwaste‒ OpeningoflicenceexemptuseofTVWS

• CognitiveRadio‒ Widebandspectrumsensing:highsamplingrate‒ Compressivespectrumsensing:sub-Nyquistsampling

• ProposedAlgorithms‒ Robustcompressivespectrumsensingwithlowcomplexity‒ Geolocationdatabaseassistedcompressivespectrum

sensingwithlowcomplexity‒ Malicioususerdetectionbasedonmatrixcompletion

• MeasurementonReal-TimeTVWSSignals

• NumericalResults

Yue Gao, Clive Parini, Zhijin Qin, Qianyun [email protected] Twitter: @WMCLab

TVWStestbed• SystemSetup‒ Basestationwithsectorantenna‒ Clientwiththedevelopedantenna

• MeasurementLocations‒ Movingclienttodifferentlocations,7linksrepresenting

variouscommunicationscenariosaretested

• MeasurementResults

‒ Proposedalgorithmshavebeenverifiedbyreal-timesignalscollectedbyRFeye node

Fig.7.MeasurementsetupwithRFeye nodeatQMUL

Fig.6.CompressiveSpectrumSensingModelFig.1.AntennaforWSD client

Fig.2.WSD Client Fig.3.Basestation

Fig.4.In-buildingandbetween-buildingsTVWScommunications

ContributionsThisprojectgoesbeyondstate-of-the-artanddeveloped:• Robustcompressivesensingalgorithms[1][2]• Acompactantenna for white space devices (WSDs)[3]• Areal-timeTVwhitespace(TVWS) testbedforM2M/IoT [4][5]

Fig.8.Simulationresultsofproposedcompressivespectrumsensingalgorithms

Fig.5.Measurementsignal-to-interference-plus-noiseratios(SINRs) oflink1,4,and7withclientantennafacingdifferentdirections.SINRofuplinksignalisnotedby’o’andthatofdownlinkisnotedby’+’

(a).DetectionprobabilitycomparisonofproposedrobustcompressivespectrumsensingalgorithmunderdifferentcompressionratiosandSNRvalues

(c).Detectionprobabilitycomparisonofproposedgeolocationdatabaseassistedcompressivespectrumsensingalgorithmunderdifferentcompressionratios

(d).Detectionprobabilitycomparisonofproposedmalicioususerdetectionframeworkunderdifferentcompressionratios

TV Database

DTV1 coverage area channels

20,21,39

DTV2 coverage area

channels 25,26,40

CR coverage area channels 20,21,25,26,39,40

Maximum Allowable Transmission Power

0.1w,0.07w,2.3w,0.5w,3w,0.2w

Geo-locationdatabase

FusioncenterSU1

SU2 SU3

SU4

SpectrumofinterestSpectrumsensing

TVwhitespacedatadissemination(webportal)

TVwhitespacerecordingLowNoise

Amplifier(LNA)

RFsignallogger

Compactand

widebandantennas

Advancedspectrumsensing

algorithms

ExistingGeo-locationdatabasemodels

UHF470-790MHz

DTT

WSDs

PMSE

DTT

Proposedsolution

Existingsolution

Validationandintegration

Hybridapproach

Overview

TV White Space Testbed with Robust Spectrum Sensing Algorithms For M2M Communications (Tic-tac) Yue Gao, Zhijin Qin, Qianyun Zhang

Antenna for slave WSDs • Compact and Low Profile

‒ Dimension: 231mm×35mm×0.8mm • Ultra-wideband

‒ Bandwidth: 474MHz-1212MHz • Good Performance

‒ Radiation pattern: omnidirectional ‒ Average gain: 1.68dBi over TVWS ‒ Radiation efficiency: over 80%

Robust Sensing Algorithms • Spectrum Usage

‒ Spectrum shortage and spectrum waste. ‒ Opening of licence exempt use of TVWS.

• Cognitive Radio ‒ Wideband spectrum sensing: high sensing rate ‒ Compressive spectrum sensing: sub-Nyquist sampling

• Proposed Algorithms ‒ Robust compressive spectrum sensing with low complexity. ‒ Geolocation database assisted compressive spectrum

sensing with low complexity. ‒ Malicious user detection based on matrix completion.

• Measurements on Real-Time TV Signals

• Numerical Results

http://tic-tac.eecs.qmul.ac.uk

TVWS testbed • System Setup

‒ Base station with sector antenna ‒ Client with home-made antenna

• Measurement Locations ‒ Moving client to different locations, seven links represents

various communication scenarios are tested

• Measurement Results

‒ All proposed algorithms have been verified by real-time TV signals RFeye node.

Fig. 2. Measurement setup with RFeye node at QMUL

Fig. 1. Compressive Spectrum Sensing Model

Fig. 4. Antenna for slave WSDs

Fig. 5. Client Fig. 6. Base station

Fig. 7. In-building and between-buildings TVWS communications

Contributions: This project goes beyond state-of-the-art and developed:

• Robust compressive sensing algorithms • Develop novel compact and wideband antennas • A real-time TV white space testbed for M2M/IoT

1

Fig. 3. Simulation results of proposed compressive spectrum sensing algorithms

Fig. 8. Measurement signal-to-interference-plus-noise ratios (SINRs) of link 1, 4, and 7 with client antenna facing different directions. SINR of uplink signal is noted by ’o’ and that of downlink is noted by ’+’.

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link1

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link4

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link7

ANTENNAS & ELECTROMAGNETICS RESEARCH GROUP

fr

fs fw

1F �4 )x

u �

K non-zero entries

PxNPx1

Nx1 Nx1P<k<NK<P<N Signal recovery

Decision making

SU

Sub-Nyquist sampling

Receiver

x

Measurement matrix ϴ

K non-zero entries

Nx1 Nx1

Px1 PxN

(a). Detection probability comparison of proposed robust compressive spectrum sensing algorithm under different compression ratios and SNR values

(b). Detection probability comparison of proposed robust compressive spectrum sensing algorithm with different sparsity levels and compression ratios

(c). Detection probability comparison of proposed geolocation database assisted compressive spectrum sensing algorithm under different compression ratios

(d). Detection probability comparison of proposed malicious user detection framework under different compression ratios

Up

Construction Yard

Construction Yard

Bancroft Road

EE hub

358

353

LINK3

LINK1

LINK4

LINK5

LINK6

lab

Section III

Section II

Section I

People’s Palace

CS Building

LINK7

ITLBuilding

EE ground

Up

357

EE Building

Link2

CS hub

fr

fs fw

1F �4 ) ��x

u �

K non‐zero entries

PxNPx1

Nx1 Nx1P<k<NK<P<N Signal recovery

Decision making

SU

Sub-Nyquist sampling

Receiver

x

Measurement matrix ϴ

K non‐zero entries

Nx1 Nx1

Px1 PxN

SNR-20 -15 -10 -5 0

Pd

0

0.2

0.4

0.6

0.8

1Theory

SS without CS

Robust CS based SS 25%

Tradtional CS based SS 25%

Robust CS based SS 10%

Tradtional CS based SS 10%

Compression ratio

0.5

0.4

0.3

0.2

0.102Sparsity level

46

8

0.9

1

0.6

0.7

0.8Pd

0.65

0.7

0.75

0.8

0.85

0.9

0.95

Compression ratio

10-1 100

Pd

0

0.2

0.4

0.6

0.8

1

Sensing only, IRLS

Hybrid framework, DNRLS

7% 20%

Compression ratio0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Pd

0

0.2

0.4

0.6

0.8

1

Pd without malicious user

Pf without malicious user

Pd with malicious user

Pf with malicious user

(b).Detectionprobabilitycomparisonofproposedrobustcompressivespectrumsensingalgorithmwithdifferentsparsitylevelsandcompressionratios

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link1

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link4

5

10

15

20

25

30

35

40

30

210

60

240

90

270

120

300

150

330

180 0

Link7

[1]Z.Qin,Y.Gao,M.Plumbley andC.Parini,“WidebandSpectrumSensingonReal-timeSignalsatSub-NyquistSamplingRatesinSingleandCooperativeMultipleNodes,”IEEETrans.SignalProcess.,vol.64,no.12,pp.3106– 3117,Jun.2016.[2]Z.Qin,Y.Gao,andC.G.Parini,“Data-assistedlowcomplexitycompressivespectrumsensingonreal-timesignalsundersub-nyquist rate,”IEEETrans.WirelessCommun.,vol.15,no.2,pp.1174–1185,Feb.2016.[3]Q.Zhang,Y.GaoandC.Parini,“CompactU-shapeUltra-widebandAntennawithCharacteristicModeAnalysisforTVWhiteSpaceCommunications,” TheIEEEInternationalSymposiumonAntennasandPropagation,Jun.2016.[4]Y.Gao,Z.Qin,Z.Feng,Q.Zhang,O.Holland,M.Dohler,“Scalable&ReliableIoT EnabledByDynamicSpectrumManagementforM2MinLTE-A”,IEEEInternetofThingsJournal,2016.(Inpress)[5]Q.Zhang,X.Zhang,Y.Gao,O.Holland,M.Dohler,P.Chawdhry,J.Chareau,“TVWhiteSpaceNetworkProvisioningwithDirectionalandOmni-directionalTerminalAntennas,”theIEEEVehicularTechnologyConference,VTC2016-FallinMontréal,Canada,Sept.2016.