Tic-tac: TV White Space Testbed with Robust …yueg/PDFs/Tic-tac-EPSRC...References: Tic-tac: TV...
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References: Tic-tac: TV White Space Testbed with Robust Spectrum Sensing Algorithms for M2M Communications Antenna for a WSD Client • Compact and Low Profile ‒ Dimension: 231mm×35mm×0.8mm • Ultra-wideband ‒ Bandwidth: 474MHz-1212MHz • Excellent 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 sampling 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 • Measurement on Real-Time TVWS Signals • Numerical Results Yue Gao, Clive Parini, Zhijin Qin, Qianyun Zhang [email protected] Twitter: @WMCLab TVWS testbed • System Setup ‒ Base station with sector antenna ‒ Client with the developed antenna • Measurement Locations ‒ Moving client to different locations, 7 links representing various communication scenarios are tested • Measurement Results ‒ Proposed algorithms have been verified by real-time signals collected by RFeye node Fig. 7. Measurement setup with RFeye node at QMUL Fig. 6. Compressive Spectrum Sensing Model Fig. 1. Antenna for WSD client Fig. 2. WSD Client Fig. 3. Base station Fig. 4. In-building and between-buildings TVWS communications Contributions This project goes beyond state-of-the-art and developed: • Robust compressive sensing algorithms [1][2] • A compact antenna for white space devices (WSDs) [3] • A real-time TV white space (TVWS) testbed for M2M/IoT [4][5] Fig. 8. Simulation results of proposed compressive spectrum sensing algorithms Fig. 5. 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 ’+’ (a). Detection probabilitycomparisonofproposed robust compressive spectrum sensing algorithm under different compression ratios and SNR values (c). Detection probabilitycomparisonofproposed geolocation database assisted compressive spectrum sensingalgorithm under different compressionratios (d). Detection probability comparisonof proposed malicious user detection framework under different compression ratios 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-location database Fusion center SU 1 SU 2 SU 3 SU 4 Spectrum of interest Spectrumsensing TV white space data dissemination (web portal) TV white space recording Low Noise Amplifier (LNA) RF signal logger Compact and wideband antennas Advanced spectrum sensing algorithms Existing Geo-location database models UHF 470-790MHz DTT WSDs PMSE DTT Proposed solution Existing solution Validation and integration Hybrid approach Overview 1 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 ITL Building EE ground Up 357 EE Building Link2 CS hub f r f s f w 1 F 4) x u Px1 Nx1 Nx1 K<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 P d 0 0.2 0.4 0.6 0.8 1 Theory 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 0.1 0 2 Sparsity level 4 6 8 0.9 1 0.6 0.7 0.8 P d 0.65 0.7 0.75 0.8 0.85 0.9 0.95 Compression ratio 10 -1 10 0 P d 0 0.2 0.4 0.6 0.8 1 Sensing only, IRLS Hybrid framework, DNRLS 7% 20% Compression ratio 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 P d 0 0.2 0.4 0.6 0.8 1 P d without malicious user P f without malicious user P d with malicious user P f with malicious user (b). Detection probabilitycomparisonofproposed robust compressive spectrum sensing algorithm with different sparsity levels and compression ratios 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 and C. Parini, “Wideband Spectrum Sensing on Real-time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes,” IEEE Trans. Signal Process., vol. 64, no. 12, pp. 3106– 3117, Jun. 2016. [2] Z. Qin, Y. Gao, and C. G. Parini, “Data-assistedlow complexity compressive spectrum sensingon real-time signals under sub-nyquistrate,” IEEE Trans. Wireless Commun.,vol. 15, no. 2, pp. 1174–1185, Feb. 2016. [3] Q. Zhang, Y. Gao and C. Parini, “Compact U-shapeUltra-widebandAntenna with CharacteristicModeAnalysis for TV White Space Communications,” The IEEE InternationalSymposiumon Antennas and Propagation,Jun. 2016. [4] Y. Gao, Z. Qin, Z. Feng, Q. Zhang, O. Holland, M. Dohler, “Scalable & Reliable IoT Enabled By Dynamic Spectrum Management for M2M in LTE-A”, IEEE Internet of Things Journal, 2016. (In press) [5] Q. Zhang, X. Zhang, Y. Gao, O. Holland, M. Dohler, P. Chawdhry,J. Chareau,“TV White Space Network Provisioningwith Directional and Omni-directional Terminal Antennas,” the IEEE Vehicular Technology Conference, VTC2016-Fall in Montréal,Canada,Sept. 2016.
Transcript of Tic-tac: TV White Space Testbed with Robust …yueg/PDFs/Tic-tac-EPSRC...References: Tic-tac: TV...
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
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TVwhitespacedatadissemination(webportal)
TVwhitespacerecordingLowNoise
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RFsignallogger
Compactand
widebandantennas
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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
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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 ’+’.
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ANTENNAS & ELECTROMAGNETICS RESEARCH GROUP
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(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
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(b).Detectionprobabilitycomparisonofproposedrobustcompressivespectrumsensingalgorithmwithdifferentsparsitylevelsandcompressionratios
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[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.
