RF Design And Test Using MATLAB And NI Tools

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RF Design and Test UsingMATLAB and NI ToolsTim Reeves – treeves@mathworks.comChen Chang - chen.chang@ni.com 2015 The MathWorks, Inc.1

What are we going to talk about? How MATLAB and Simulink can be used in a wireless system designworkflowWireless Scenario SimulationEnd-to-end Simulation of mmWave Communication Systems with HybridBeamformingDeveloping Power Amplifier models and DPD algorithms in MATLABUse of National Instruments PXI for PA characterization with DPD2

Common Platform for 5G DevelopmentUnified Design and SimulationMobile and Connectivity StandardsBasebandPHYRFFront EndDeepLearningC-V2XMIMO &AntennasChannels &PropagationPrototyping and Testing WorkflowsOTAWaveformTx/RxDeploy toC/C ModelBasedDesign3

What differentiates high data rate 5G systems from previouswireless system iterations? High data rates ( 1 Gbps) requires use of previously “under-used” (mmWave) frequencybands mmWave requires MIMO architectures to achieve same performance as sub-6GHz– Lower device power and high channel attenuation Antenna array, RF, and digital signal processing cannot be designed separately!– Large communication bandwidth digital signal processing is challenging– High-throughput DSP linearity requirements imposed over large bandwidth– Wavelength 1mm small devices, many antennas packed in small areas4

How is the presentation set up?Link Level ModelingScenario ModelingTRANSMITTERBasebandDigitalFront EndDACPAChannelDigital PHYBasebandAntennaRF Front EndDigitalFront EndADCLNARECEIVERHardware5

What is the most basic way we can look at a wireless link?Scenario Modeling Scenario Level Modeling– RF propagation– Multi-transmitter scenarios– Coverage6

What relevant items need to be included to analyze a realistic5G coverage scenario? Multiple Transmitter Scenario for analyzing SINRFrequency 4GHzTX power 44dBmAntenna height 25m Model 19 adjacent cellsEach cell has 3 sectors7

What are the different scenarios that can be analyzed? Select unique RF propagation scenariossuch as ‘Close-in’ and ‘Rain’ propagationmodels. Choose different antenna elements andarray configurations to maximizecoverage.8

What are the different use cases for Antenna Toolbox?Antenna Element and Array DesignVisualization and Analysis of 3rd partyAntenna DataRF Propagation Visualization and Analysis9

What type of fidelity do we want to add to a physical layermodel?Link Level Modeling RF Front End–––– TRANSMITTERNoise budgetGainNon-linearityTx ont EndDACPAChannelDigital PHYBasebandAntennaRF Front EndDigitalFront EndADCLNARECEIVERArraysBeamformingPropagation effectsLoading10

Why do link level modeling for a 5G mmWave system?11

What needs to be included in a 5G system model to describetypical operation? Include fidelity that comprises of array behavior, channel modeling, spatialmultiplexing and pre-coding and basic hybrid beamformingSystem level design considerationsDesign an arrayAdd channel modelSpatial multiplexing/PrecodingHybrid beamforming12

What comprises the behavior between the Tx and Rxantenna? Channel and RF propagation behaviorSignal AttenuationWideband performanceScatter-rich propagation13

What is Hybrid Beamforming?RFRFHBasebandRFBasebandRFBeamforming done in two stages:– RF Beamforming (phase shifters in RF front ends)– Digital Beamforming (digital filtering of baseband signal)14

Why do you want to add RF (System-Level) models to yourPHY layer model? Design the architecture and define the specs of the RF componentsIntegrate RF front ends with adaptive algorithms such as DPD, AGC, beamformingTest and debug the implementation of the transceiver before going in the labUse models and measured data to gain insights in your designProvide a model of the RF transceiver to your colleagues and customers15

Equivalent BasebandfreqCircuit EnvelopefreqDCCarrier 1Carrier 2True Pass-BandSpectrumCarrierSpectrumSpectrumSimulation speedCircuit Envelope to Trade-off Fidelity and SpeedfreqModeling fidelity16

PA Linearization: Digital Pre Distortion (DPD) in PracticeUp-conversionBasebandDPDPARFAntenna loadingAdaptive coefficientsTimingPout [dBm]Down-conversionPA characteristic(actual)CompressionMemoryDPD characteristicPin [dBm]17

PA Modeling Workflow Get I/Q (time domain, wideband) measurement data from your PAFit the data with a memory polynomial (extract the coefficients) using MATLABVerify the quality of the polynomial fitting (time, frequency)Memory length Order 18

What resources are available to characterize a PA Model?PA DataMATLAB fitting procedure(White box)PA model coefficientsPA model for circuitenvelope simulation19

Why is static DPD modeling not enough for 5G systems? Circuit Envelope for fast RF simulationLow-power RF and analog components– Up-conversion / down-conversion– Antenna load Digital signal processing algorithm: DPD20

Real-Life Example: AD9371 Transmitter Observer21

From Simulation to Implementation: HDL Code GenerationAutomatically generate synthesizable HDL (Verilog / VHDL) code Make your model hardware “friendly” Estimate utilized resources Optimize model and generated code (speed, cost) Target FPGAs for rapid prototyping22

How do we transition from software models to hardware? Implementing DPD in hardware– Data streaming– Prototype on hardwareHardware23

Connecting System-Level Models to Hardware forDesign and Verification24

NI Front-End Module Test With DPD VST with 1 GHz instantaneous generation and analysis bandwidthFree NI-RFmx SpecAn with LUT, MPM, and GMP DPD modelsFree RFIC Test Software with DPD automation examplesPXI System 1 2Generate reference waveform andacquire distorted waveformCreate predistortion model bycomparing reference waveform todistorted waveformDigitalFrontEnd ModuleFront-EndModuleSMUVSALNAVST3 Apply DPD to reference waveform usingpredistortion model4 Generate predistorted waveform andmake measurementsPAScopeAWGPower PMIC ETModulatorPower Supply25

Traditional T&M Setup for MATLAB Based PACharacterization with DPD Algorithm Running in MATLAB Familiar user experience for many engineersSlower measurement speed, Large physical footprintExpensive to upgrade or replace – even SoftwareDifficult to synchronize for ET & DPDTradeoffs between speed and accuracy26

NI PXI Setup for MATLAB Based PA Characterizationwith DPD & ET Algorithm Running in MATLAB Similar user experience as box-instrumentsFaster and FPGA-accelerated measurement speed, ata fraction of the physical footprintModularity for incremental upgradesNative synchronization technologies at sub nanosecondaccuracyR&D grade measurement accuracy with production testspeed27

Enabling Integrated Semi PA Design & Validation FlowBetween LabVIEW & uliDPDDPDDesign(Sim-only)V&V(T&M hmMATLAB(Custom)RFmx NanoSemiSim onmentMATLABLabVIEWRFICDUTDUTAnalysisDUTAnalysis28

Enabling Integrated Semi PA Design & Validation FlowBetween LabVIEW & uliDPDDPDDesign(Sim-only)V&V(T&M VIEWRFmxMATLABDPDAlgorithmMATLAB(Custom)RFmx NanoSemiMATLAB(Custom)Sim sisRFmx .NET APIDUTAnalysis29

Enabling Integrated Semi PA Design & Validation FlowBetween LabVIEW & uliDPDLabVIEW MATLABScript NodeDPDDesign(Sim-only)V&V(T&M hmMATLAB(Custom)RFmx NanoSemiMATLAB(Custom)MATLAB(Custom)Sim TLABLabVIEWRFICDUTDUTAnalysisDUTAnalysis30

High-Power PA w/ DPD HW Demo SetupPXIe-1078 ChassisPXIe-8840 ControllerPXIe-5840 VSTPXIe-4112 Power Supply31

PA Design Engineer’s View in MATLAB32

Validation Engineer’s View in LabVIEW33

Two Distinct Approaches to PA CharacterizationTraditional Approach Separate workflow for design and validationDifferent waveforms, PA models, analysis algorithmExpensive, large footprint, poor synchronizationPlatform-Based Approach Integrated workflow for design and validationSame waveforms, PA models, analysis algorithmModular, small footprint, sub-nanosecondsynchronization34

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Qualcomm UK Uses MATLAB to Develop 5G RFFront-End Components and AlgorithmsNanoSemi Improves System Efficiencyfor 5G and Other RF ProductsChallengeChallenge10x more waveform combinations in 5G than in LTE,making device validation much more complex and timeconsumingAccelerate design and verification of RF power amplifierlinearization algorithms used in 5G and Wi-Fi 6 devicesSolutionUse MATLAB to simulate hardware-accurate Tx and Rxpaths to predict system performance and optimizedesign parameters.Results Fully model RF transceiver and components Securely release sensitive IP Eliminate the cost of developing separate test suitesSolutionUse MATLAB to characterize amplifier performance,develop predistortion and machine learning algorithms,and automate standard-compliant test proceduresResults Development time reduced by 50% Iterative verification process accelerated Early customer validation enabled“We use MATLAB models tooptimize and verify the 5G RFfront end through all phasesof development.”Sean LynchQualcomm 5G RF front end prototypeQualcomm UK, Ltd.NanoSemi linearization IP developmentand verification using MATLAB.“With MATLAB, our team can deliverleading-edge IP faster, enabling ourcustomers to increase bandwidth,push modulation rates higher, andreduce power consumption.”Nick KarterNanoSemi36

Wrap up How MATLAB and Simulink can be used in a wireless system designworkflowWireless Scenario SimulationEnd-to-end Simulation of mmWave Communication Systems with HybridBeamformingDeveloping Power Amplifier models and DPD algorithms in MATLABUse of National Instruments PXI for PA characterization with DPD37

Learn More Where can you get more information about MathWorks tools for wirelesssystem modelling? MATLAB and Simulink for 5G Development White paper: RF PA and DPD linearization using MATLAB and Simulink White paper: Hybrid Beamforming for 5G Systems38

RF Design and Test Using MATLAB and NI Tools . Antenna array, RF, and digital signal processing cannot be designed separately! – Large communication bandwidth digital signal processing is challenging – High-throughput DSP linearity requirements imposed over large bandwidth

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