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Embedded Signal Processing

Embedded Signal Processing. Prof. Brian L. Evans. http://www.ece.utexas.edu. November 21, 2003. Signals and Systems Pack Symbolic analysis of signals and systems in Mathematica By product of my PhD work On market since 1995 Ptolemy Classic Mixes models of computation Untimed dataflow

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Embedded Signal Processing

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  1. Embedded Signal Processing Prof. Brian L. Evans http://www.ece.utexas.edu November 21, 2003

  2. Signals and Systems Pack Symbolic analysis of signals and systems in Mathematica By product of my PhD work On market since 1995 Ptolemy Classic Mixes models of computation Untimed dataflow Process network Discrete-event Untimed dataflow synthesis Source code powers Agilent Advanced Design System On My Way to Austin… Rambling Wreck 1987-1993 Cal 1993-1996

  3. Develop and Disseminate Theoretical bounds on signal/image quality Optimal and low-complexity algorithms using bounds Algorithm suites and fixed-point, real-time prototypes Analog/Digital IIR Filter Design for Implementation Butterworth and Chebyshev filters are special cases of Elliptic filters Minimum order does not always give most efficient implementation Control quality factors Embedded Signal Processing Lab

  4. Students & Alumni ADSL/VDSL Transceiver Design Real-Time Imaging Ph.D. students: Gregory E. Allen(UT Applied Research Labs)Serene BanerjeeMS students: Vishal Monga Ph.D. graduates: Thomas D. Kite (Audio Precision)Niranjan Damera-Venkata (HP Labs)MS graduates: Young Cho (UCLA) Ph.D. students: Dogu AriflerMing Ding Ph.D. graduates: Güner Arslan (Cicada)Biao Lu (Schlumberger)Milos Milosevic (Schlumberger) Wireless Communications Ph.D. students: Kyungtae Han Zukang ShenMS students: Ian Wong (NI Summer Intern) Ph.D. graduate: Murat Torlak (UT Dallas)MS graduates: Srikanth K. Gummadi (TI) Amey A. Deosthali (TI) Image Analysis Ph.D. graduates: Dong Wei (SBC Research)K. Clint Slatton(University of Florida)Wade C. Schwartzkopf (Integrity Applications) Wireless Networking and Comm. Group: http://www.wncg.org Center for Perceptual Systems: http://www.cps.utexas.edu

  5. an a*(t) Impulse modulator d[n] Serial/parallelconverter Map to 2-D constellation 1 J Impulse modulator Bit stream bn b*(t) Pulse shaper gT(t) FIR filter Delay s(t) + Local Oscillator Transmitted signal FIR filter 90o FIR filter Pulse shaper gT(t) Senior Real-time DSP Lab Elective • Lab #6: Quadrature Amplitude Modulation Transmitter

  6. Senior Real-time DSP Lab Elective • Deliverable: V.22bis Voiceband Modem • Design of sinusoidal generators, filters, etc. • Program in C on TI DSP processor using Code Composer Studio • Test implementation with spectrum analyzers, etc. • Reference Design in LabVIEW Allows Students To • Explore communication performance tradeoffs vs. parameters • See relationships among modem subsystems in block diagram • LabVIEW DSP Integration Toolkit 2.0 for Spring 2004 • Interacts with Code Composer Studio for real-time debugging info • Enables all test and measurement to be performed on desktop PC • Course alumni Prethi Gopinath and Newton Petersen at NI

  7. LabVIEW Interface Control Panel QAM Passband Signal Eyediagram

  8. Multicarrier Modulation • Divide broadband channel into narrowband subchannels • No inter-symbol interference if constantsubchannel gain and ideal sampling • Based on fast Fourier transform (FFT) • Standardized in ADSL/VDSL (wired)and IEEE 802.11a/g & 802.16a (wireless) DTFT-1 sinc pulse w k -wc wc channel carrier magnitude subchannel frequency In ADSL/VDSL, each subchannel is 4.3 kHz wide andcarries a QAM encoded subsymbol

  9. conventional ADSL equalizer structure ADSL Transceiver: Data Transmission N/2 subchannels N real samples S/P quadrature amplitude modulation (QAM) encoder mirror data and N-IFFT add cyclic prefix P/S D/A + transmit filter Bits 00110 TRANSMITTER channel RECEIVER N/2 subchannels N real samples P/S time domain equalizer (FIR filter) QAM demoddecoder N-FFT and remove mirrored data S/P remove cyclic prefix receive filter + A/D invert channel = frequency domain equalizer

  10. Contributions by Research Group • New Time-Domain Equalizer Design Methods • Maximum Bit Rate gives an upper bound • Minimum Inter-Symbol Interference method(amenable to real-time, fixed-point implementation) • Minimum Inter-Symbol Interference Method • Reduces number of TEQ taps by a factor of ten over Minimum Mean Squared Error method for same bit rate • Implemented in real-time on Motorola 56300, TI TMS320C6200 and TI TMS320C5000 DSPs http://www.ece.utexas.edu/~bevans/projects/adsl

  11. Wireless Multicarrier Modulation S/P quadrature amplitude modulation (QAM) encoder N-point inverse FFT add cyclic prefix P/S D/A + transmit filter Bits 00110 TRANSMITTER multipath channel RECEIVER receive filter + A/D P/S QAM demod decoder S/P remove cyclic prefix freq. domain equalizer N-point FFT Orthogonal frequency division multiplexing (OFDM)

  12. OFDM Simulation in LabVIEW • IEEE 802.16a Standard • Fixed broadband wireless system • High speed wireless access from home or office • IEEE 802.16a Simulation • Physical layer communication • Realistic channel models • Channel estimation • Authored by Alden Doyle, Kyungtae Han, Ian Wong www.ece.utexas.edu/~iwong/Research.htm

  13. Possible LabVIEW Extensions • Add communication system design/simulation support for • Drop down and “click to configure” communication building blocks • Multicarrier systems and error control coding • Performance visualization mechanisms for communication systems performance analysis (BER curves, eye diagrams, etc.) • Text-based algorithm design environment • For quick calculations and parameter calculations • Implement a text-to-VI translation tool, e.g. convert math script“x = [1:10]; y = fft(x)” to a VI implementation • Improve optimization toolkit • Make it easier to use • Add supports for more extensive set of algorithms

  14. Fixed-Point Wordlength Optimization • Problem: Manual floating-to-fixed pointconversion for digital hardware implementation • Design time grows exponentially with number of variables • Time consuming • Error prone • Goal: Develop fast algorithm tooptimize fixed-point wordlengths • Minimize hardware complexity • Maximize application performance • Solution: Simulation-based search • Determine minimum wordlength • Greedy search algorithm • Complexity-and-distortion measure Optimum wordlength Error [1/performance] Complexity Wordlength(w)

  15. Design Wordlength Optimization In LabVIEW • Use broadband wireless access demodulator design • Pick four variables and build fixed-point type • Manually estimate maximum and minimum values of these variables for integer wordlength determination • Optimize these variables using Greedy search algorithm with complexity-and-distortion measure

  16. Design Possible LabVIEW Extensions • Add fixed-point data type • Build fixed-point arithmetic operations, filtering operations, etc. • Estimate implementation complexity as function of input wordlengths in blocks • Automatically estimate or log max and min values on arcs • Implement wordlength search algorithms

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