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PA
DPD
Pin [dBm]
Pout
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PA characteristic
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Compression
Memory
RF
Baseband
Up-conversion
Down-conversion
Adaptive coefficients
Timing
Antenna loading
DPD characteristic
PA Linearization: Digital Predistortion (DPD) in Practice](https://image.slidesharecdn.com/ww-2023-expo-transforming-wireless-system-design-with-matlab-and-ni-231218164039-85878406/85/transforming-wireless-system-design-with-matlab-and-ni-pdf-11-320.jpg)
transforming-wireless-system-design-with-matlab-and-ni.pdf
- 1. 0 Jeremy Twaits, NI Robin Getz, MathWorks Transforming Wireless System Design with MATLAB and NI
- 2. 1 Share the EXPO experience #MATLABEXPO rgetz robinlgetz rgetz Jeremy Twaits
- 3. 2 2 What are we going to talk about ▪ Learn how to use MATLAB and NI to optimize wireless design processes and improve your product quality ▪ Discover the latest features and updates from both platforms that will help you achieve your design goals ▪ Get insights on how to tackle common wireless design challenges and find innovative solutions
- 4. 3 3 Wireless Standards AI for Wireless Digital, RF, and Antenna Design Hardware Design, Prototyping, and Testing Radar Applications Hands-On Learning Transforming Wireless System Design with MATLAB and NI Design, analyze, and test standards- based 5G, Wi-Fi, LTE, satellite communications, and Bluetooth systems. Apply deep learning, machine learning, and reinforcement learning techniques to wireless communications applications. Jointly optimize digital, RF, and antenna components of an end-to-end wireless communications system. Implement and verify your designs on hardware. Test your algorithms and designs over-the-air with RF instruments and SDRs. Simulate multifunction radars for automotive, surveillance, and SAR applications. Synthesize radar signals to train machine and deep learning models for target and signal classification. Jump-start learning online or in the classroom. Download interactive teaching content developed by MathWorks and educators from leading universities.
- 5. 4 Spectrum is a high demand, non-renewable natural resource
- 6. 5 5 Common Platform for Wireless Development Radar
- 7. 6 Wireless System Design Based on SDRs ▪ A software-defined radio (SDR) is a wireless device that typically consists of a configurable RF front end with an FPGA or programmable system-on-chip (SoC) to perform digital functions. ▪ Prototype: Radio I/O to host ▪ Deployed: Operates independently
- 8. 7 7 High Performance and Inline Processing ATCA-3671 Massive BB Processing USRP E31X & E320 56 MHz BW 6 GHz Fc B200mini 56 MHz BW 6 GHz Fc VST 1 GHz BW 6 GHz Fc High Frequency / Wide Bandwidth Instrument Grade + Calibration mmWave VST 1 GHz BW 44 GHz Fc Deployable USRP 2901 56 MHz BW 6 GHz Fc Host based (USB) Low SWaP USRP X310 160 MHz BW 6 GHz Fc FlexRIO 200 MHz BW 4.4 GHz Fc Large FPGA SDRs N321 200 MHz BW 6 GHz Fc NI Ettus USRP X410 400 MHz BW 8 GHz Fc Stand Alone FPGA + Embedded Processor USRP RX310 Pixus Technologies 3rd-Gen VST 2 GHz BW 23 GHz Fc Wireless Research, Design, Prototyping, and Deployment Portfolio Highly-Portable to High-Performance NI Ettus USRP X440 1.6 GHz BW 4 GHz Fc
- 9. 8 8 NI Ettus USRP X410 Product Overview ▪ Frequency Range: 1 MHz - 8 GHz ▪ Signal Bandwidth: 400 MHz ▪ Receive Channels: 4X ▪ Transmit Channels: 4X ▪ Max TX Power: up to 22 dBm1 ▪ Max RX Power: 0 dBm ▪ Xilinx Zynq UltraScale+ RFSoC – Built-in quad core ARM processor ▪ Onboard IP: Fractional DDC, DUC ▪ Interface options: dual QSFP28 (10G), 1G ▪ Synchronization: 10 MHz / PPS, GPSDO option ▪ Software: – MATLAB, Wireless Testbench – NI-USRP, LabVIEW FPGA – USRP Hardware Driver (UHD) RF Capabilities Digital Capabilities 1 see specification for details
- 10. 9 9 Wireless Testbench plus USRP X410
- 11. 10 10 PA DPD Pin [dBm] Pout [dBm] PA characteristic (actual) Compression Memory RF Baseband Up-conversion Down-conversion Adaptive coefficients Timing Antenna loading DPD characteristic PA Linearization: Digital Predistortion (DPD) in Practice
- 12. 11 11 PA Modeling Workflow ▪ Get I/Q (time domain, wideband) measurement data from your PA ▪ Fit the data with a memory polynomial (extract the coefficients) using MATLAB ▪ Verify the quality of the polynomial fitting (time, frequency) Memory length → Order →
- 13. 12 12 What resources are available to characterize a PA Model? PA Data MATLAB fitting procedure (White box) PA model coefficients PA model for circuit envelope simulation
- 14. 13 13 Why is static DPD modeling not enough for 5G systems? ▪ Circuit Envelope for fast RF simulation ▪ Low-power RF and analog components – Up-conversion / down-conversion – Antenna load ▪ Digital signal processing algorithm: DPD
- 15. 14 14 NI PXI Setup for PA Characterization with DPD & ET Algorithm Running in MATLAB
- 16. 15 Qualcomm UK Uses MATLAB to Develop 5G RF Front-End Components and Algorithms Challenge 10x more waveform combinations in 5G than in LTE, making device validation much more complex and time- consuming Solution Use MATLAB to simulate hardware-accurate Tx and Rx paths to predict system performance and optimize design parameters. Results ▪ Fully model RF transceiver and components ▪ Securely release sensitive IP ▪ Eliminate the cost of developing separate test suites “We use MATLAB models to optimize and verify the 5G RF front end through all phases of development.” Sean Lynch Qualcomm UK, Ltd. Qualcomm 5G RF front end prototype NanoSemi Improves System Efficiency for 5G and Other RF Products Challenge Accelerate design and verification of RF power amplifier linearization algorithms used in 5G and Wi-Fi 6 devices Solution Use MATLAB to characterize amplifier performance, develop predistortion and machine learning algorithms, and automate standard-compliant test procedures Results ▪ Development time reduced by 50% ▪ Iterative verification process accelerated ▪ Early customer validation enabled NanoSemi linearization IP development and verification using MATLAB. “With MATLAB, our team can deliver leading-edge IP faster, enabling our customers to increase bandwidth, push modulation rates higher, and reduce power consumption.” Nick Karter NanoSemi
- 17. 16 16 Development of Radar Systems with MATLAB & Simulink Radar Systems Antenna/RF Signal Processing Data Processing Environment Scenes Scenarios Development Platform Analyze Simulate Design Deploy Integrate Test Resource Management & Controls
- 18. 17 17 Summary: Support Full Radar Life Cycle Concept Exploration Systems Engineering Design and Test Operations and Planning Data Analysis AI for Radar Radar Systems Engineering Radar Scenarios and Data Synthesis Multifunction and Cognitive Radar Phased Arrays
- 19. 18 18 Radar Budget Analysis Stoplight visualizations and metrics SNR available at the input of the radar receiver Detectability threshold for a given Pd, Pfa Metrics minimum detectable signal (MDS) EIRP range and Doppler ambiguity resolution, accuracy track probabilities Warn: violates objective but meets threshold Stoplight chart Pass: measurement meets objective
- 20. 19 19 Land and Sea Surface Models Radar Reflectivity Models Radar Surface Return Power Level Measurement Level Waveform Level Simulating Clutter Returns Test signal and data processing algorithms
- 21. 20 20 Radar Application PRF, frequency and waveform agility Closing the signal processing loop Change signal processing chain when an event is detected ▪ frequency hoping ▪ PRF selection ▪ waveform selection ▪ etc.
- 23. 22 22 RFSG.dll RFSA.dll Software Setup for Radar Prototyping Software stack PXIe Vector Signal Transceiver RFSG RFSA RFSA Driver RFSG Driver RFSA LabVIEW Wrapper RFSG LabVIEW Wrapper NI.Receiver System Object NI.Transmitter System Object MATLAB Example Code Hardware Driver LabVIEW MATLAB Application 23 GHz* VSA with up to 2 GHz Instantaneous BW * 26.5 GHz available in H2.2023
- 24. 23 23 Software Setup Call sequence from MATLAB Start Instantiate NI.Transmitter Instantiate NI.Receiver Transmit Waveform Signal Acquisition Generate Radar Waveform Display Range- Doppler Map Use MATLAB Phased Array Toolbox to generate the desired waveform for transmitting Use NI.Transmitter and NI.Receiver MATLAB System Objects, to instantiate RFSA and RFSG device with the desired parameters Use NI.Transmitter and NI.Receiver MATLAB system Objects to generate and acquire signals. Repeat this section multiple times for higher resolution With MATLAB Phased Array Toolbox to calculate and display range-doppler map using the acquired IQ data array
- 25. 24 ni.com PXIe-5842 Vector Signal Transceiver | Overview 23 GHz* VSG with up to 2 GHz Instantaneous BW * 26.5 GHz available in H2.2023 23 GHz* VSA with up to 2 GHz Instantaneous BW * 26.5 GHz available in H2.2023 High Performance Dual LO Synthesizer Unique LO chains for RF Out and RF In (from PXIe-5655) Multi-Instrument Synchronization Expand channel count with phase coherency LO / REF-sharing and TClk sync across the PXI backplane High speed serial interface MGT - 16 lanes @ 16 Gbps Full Rate (2 GHz BW) IQ Data Streaming to NI FPGA Co-processor (Available H2.2023) Integrated RF Signal Chain Pulse Modulation Allows for optimization of On/Off Ratio versus pulse width (Available H2.2023) Small Footprint Requires only 4 PXIe slots PFI 0 (Trigger / Event)
- 26. 25 25 COTS-Based Active and Passive SAR/ISAR Radar Design and Tests ▪ “One of the most challenging parts of developing any radar system is digital signal processing. In our applications we used real-time SAR processing for an active FMCW radar system and offline processing for passive SAR/ISAR imaging implemented with The MathWorks, Inc. MATLAB® software.” – Dr Piotr Samczyński, Warsaw University of Technology Institute of Electronic Systems https://www.ni.com/en-gb/innovations/case-studies/19/cots- based-active-and-passive-sar-isar-radar-design-and-tests.html
- 27. 26 Electrical / Computer Engineering Education Communications Signal Pro. Computer Eng. Robotics Controls Electrophysics Microelectronics Electrification Wireless Wireline Signal Processing Kinematics/ Dynamics Path Plan Perception Controls Radiowaves Photonics Lasers Optics Quantum Tech. Magnetics MEMS/NEMS Circuits VLSI Power Sys. Power Elec. Semiconductors FPGA/ASIC/SoC ML/DL/AI Comp. Vision Data Science Embedded Microprocessor Cryptography Solar ELECTRICAL & COMPUTER ENGINEERING Renewable Energy ELECTRICAL & COMPUTER ENGINEERING
- 28. 27 27 Specs • Low-cost, all-in-one solution • Frequency Range: 70 MHz – 6 GHz • 50-100 mW output power • USRP B200 / NI USRP 2900 • XC6LX75 FPGA • 1 TX & 1 RX Half or Full Duplex • Up to 56 MHz RF Bandwidth • USB 3.0 Interface, bus powered • 12-bit ADC & DAC • USRP B210 / NI USRP 2901 • XC6LX150 FPGA • 2 TX & 2 RX Half or Full Duplex, Coherent • Up to 30.72 MHz RF Bandwidth in 2x2 • USB 3.0 Interface, External • MICTOR, JTAG, and GPIO connectors Applications • FM, TV Broadcast • Signals Intelligence • Communications Research NI USRP-290X B-Series Overview
- 29. 28 28 Teaching Wireless Communications with USRP ▪ “More than four out of five students, 82 percent, said that in the future they would like to make use of the USRP" – Robert Maunder, University of Southampton ▪ “In lab assignments, we could really test out the theory and gain a deeper understanding of how communication systems work.” – Student, Rutgers University
- 30. 29 29 Transforming Wireless System Design with MATLAB and NI Wireless Standards Design, analyze, and test standards-based 5G, Wi-Fi, LTE, satellite communications, and Bluetooth systems. AI for Wireless Apply deep learning, machine learning, and reinforcement learning techniques to wireless communications applications. Digital, RF, and Antenna Design Jointly optimize digital, RF, and antenna components of an end-to-end wireless communications system. Hardware Design, Prototyping and Testing Implement and verify your designs on hardware. Test your algorithms and designs over-the-air with RF instruments and SDRs. Radar Applications Simulate multifunction radars for automotive, surveillance, and SAR applications. Synthesize radar signals to train machine and deep learning models for target and signal classification. Hands-On Learning Jump-start learning online or in the classroom. Download interactive teaching content developed by MathWorks and educators from leading universities.
- 31. 30 © 2023 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders. Thank you