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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 - PowerPoint PPT Presentation
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http://signal.ece.utexas.edu
http://www.cps.utexas.edu
http://www.wncg.org
Embedded Signal Processing
Prof. Brian L. Evans
November 21, 2003
http://www.ece.utexas.edu
2
On My Way to Austin… 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
1987-1993
1993-1996
3
Embedded Signal Processing Lab 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
4
Image Analysis Ph.D. graduates: Dong Wei (SBC Research) K. Clint Slatton (University of Florida) Wade C. Schwartzkopf (Integrity Applications)
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 Arifler Ming Ding
Ph.D. graduates: Güner Arslan (Cicada) Biao Lu (Schlumberger) Milos Milosevic (Schlumberger)
ADSL/VDSL Transceiver Design
Wireless Communications
Ph.D. students: Kyungtae Han Zukang Shen MS students: Ian Wong (NI Summer Intern)
Ph.D. graduate: Murat Torlak (UT Dallas)MS graduates: Srikanth K. Gummadi (TI) Amey A. Deosthali (TI)
Wireless Networking and Comm. Group: http://www.wncg.org
Center for Perceptual Systems: http://www.cps.utexas.edu
Students & Alumni
5
Senior Real-time DSP Lab Elective
Lab #6: Quadrature Amplitude Modulation Transmitter
Serial/parallelconverter
Map to 2-D constellation
Impulse modulator
Impulse modulator
Pulse shaper gT(t)
Local Oscillator
+
90o
Pulse shaper gT(t)
d[n]an
bn
a*(t)
b*(t)
s(t)
1 J
Delay
Bit stream
FIR filter
FIR filter
FIR filter
Transmitted signal
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 InterfaceControl
Panel
Eyediagram
QAMPassband
Signal
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)
subchannel
frequency
magnitude
carrier
DTFT-1pulse sinc
kcc
kkc
sin
channel
In ADSL/VDSL, each subchannel is 4.3 kHz wide andcarries a QAM encoded subsymbol
9
P/S
QAM demod
decoder
invert channel
=frequency
domainequalizer
S/P
quadrature amplitude
modulation (QAM) encoder
mirrordataand
N-IFFT
add cyclic prefix
P/SD/A +
transmit filter
N-FFTand
removemirrored
data
S/Premove
cyclic prefix
TRANSMITTER
RECEIVER
N/2 subchannels N real samples
N real samplesN/2 subchannels
time domain
equalizer (FIR filter)
receive filter
+A/D
channel
ADSL Transceiver: Data Transmission
Bits
00110
conventional ADSL equalizer structure
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 DSPshttp://www.ece.utexas.edu/~bevans/projects/adsl
11
Wireless Multicarrier Modulation
P/S
QAM demod
decoder
freq. domain
equalizer
S/P
quadrature amplitude
modulation (QAM) encoder
N-pointinverse
FFT
add cyclic prefix
P/SD/A +
transmit filter
N-pointFFT
S/Premove cyclic prefix
TRANSMITTER
RECEIVER
receive filter
+A/D
multipath channel
Bits00110
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 Wordlength(w)
ComplexityError
[1/performance]
Optimumwordlength
15
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
Design
Encoder OFDMModulator
WirelessChannelModel
OFDMDemodulator
ChannelEstimator
DecoderBit error
ratetester
DataSource
ChannelEqualizer
w0w1
w2w3
16
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
Design
wopt
wb
w1
w2
dw 1
dw2
5
5
Max Min IWL
w0 4.8 -4.5 3
W1 3.7 3.7 2
W2 0.8 -0.9 0