Modem Design, Implementation, and Testing Using NI’s LabVIEW

Prof. Brian L. EvansEmbedded Signal Processing Laboratory

The University of Texas at Austinbevans@ece.utexas.edu

Contributions by Vishal Monga, Zukang Shen, Ahmet Toker, and Ian Wong, UT Austin

http://www.wncg.org http://www.ece.utexas.edu

2

Outline

Real-Time DSP Course

Single Carrier Transceiver

Sinusoidal Generation

Digital Filters

Data Scramblers

Pulse Amplitude Modulation

Quadrature Amplitude Modulation

Multicarrier Transceiver

Conclusion

3

Real-Time DSP Course: Overview

Objectives of junior-level class Build intuition for signal processing concepts Translate signal processing concepts into

real-time digital communications software

Lecture: breadth (three hours/week) Digital signal processing algorithms Digital communication systems Digital signal processor architectures

Laboratory: depth (three hours/week) Deliver voiceband modem “Design is the science of tradeoffs” (Yale Patt) Test/validate implementation

Over 500 served

since 1997

http://www.ece.utexas.edu/~bevans/courses/realtime/

4

Real-Time DSP Course: Which DSP?

Students are third-year undergraduates Fixed-point DSPs for high-volume products

Battery-powered: cell phones, digital still cameras … Wall-powered: ADSL modems, cellular basestations …

Fixed-point issues Using non-standard C extensions for fractional data Converting floating-point programs to fixed-point Manual tracking of binary point prone to error

Floating-point DSPs Feasibility for fixed-point DSP realization Shorter prototyping time

Program TI TMS320C67x DSP in C Code Composer Studio 2.2

5

Real-Time DSP Course: Textbooks

C. R. Johnson, Jr., and W. A.Sethares, TelecommunicationBreakdown, Prentice Hall, 2004. Intro to digital communications

and transceiver design Matlab examples

S. A. Tretter, Comm. System Design usingDSP Algorithms with Lab Experiments forthe TMS320C6701 & TMS320C6711, 2003. Assumes DSP theory and algorithms Assumes access to C6000 reference manuals Errata/code: http://www.ece.umd.edu/~tretter

Bill Sethares (Wisconsin)

Rick Johnson (Cornell)

Steven Tretter (Maryland)

6

Lab 1. QAM Transmitter DiagramLab 4Rate

Control

Lab 6 QAM

Encoder

Lab 3Tx Filters

Lab 2 Passband

Signal

LabVIEW demo by Zukang Shen (UT Austin)

7

Lab 1. QAM Transmitter Diagram

LabVIEW Control

PanelQAM

PassbandSignal

Eye Diagram

LabVIEW demo by Zukang Shen (UT Austin)

8

square root raise cosine, roll-off = 0.75, SNR =

raise cosine, roll-off = 1, SNR = 30 dB

passband signal for 1200 bps mode

passband signal for 2400 bps mode

Lab 1. QAM Transmitter Diagram

9

Lab 2. Sine Wave Generation

Aim: Evaluate three waysto generate sine waves Function call Lookup table Difference equation

Three output methods Polling data transmit register Software interrupts Direct memory access (DMA) transfers

Expected outcomes are to understand Signal quality vs. implementation complexity tradeoff C6701 EVM board’s stereo codec operation Interrupt mechanisms and DMA transfers

10

Lab 2. Sine Wave Generation

Evaluation procedure Validate sine wave frequency on scope, and test for

various sampling rates (14 sampling rates on board) Method 1 with interrupt priorities Method 1 with different DMA initialization(s)

LabVIEW DSP Test Integration Toolkit 2.0

Code Composer Studio 2.2

C6701

Fall 2003

HP 60 MHz

Digital Storage

Oscilloscope

Spring 2004

11

Lab 3. Digital Filters

Aim: Evaluate four ways to implementdiscrete-time linear time-invariant filters FIR filter: convolution in C and assembly IIR Filter: direct form and cascade of biquads, both in C

IIR filter design gotchas: oscillation & instability In classical designs, poles sensitive to perturbation Quality factor measures sensitivity of pole pair:

Q [ ½ , ) where Q = ½ dampens and Q = oscillates

Elliptic analog lowpass IIR filter p = 0.21 at p = 20 rad/s and s = 0.31 at s = 30 rad/s [Evans 1999]

Q poles zeros

1.7 -5.3533±j16.9547 0.0±j20.2479

61.0 -0.1636±j19.9899 0.0±j28.0184clas

sica

l

Q poles zeros

0.68 -11.4343±j10.5092 -3.4232±j28.6856

10.00 -1.0926±j21.8241 -1.2725±j35.5476 opti

miz

ed

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Lab 3. Digital Filters

IIR filter design for implementation Butterworth/Chebyshev filters special

cases of elliptic filters Minimum order not always most efficient

Filter design gotcha: polynomial inflation Polynomial deflation (rooting) reliable in floating-point Polynomial inflation (expansion) may degrade roots Keep native form computed by filter design algorithm

Expected outcomes are to understand Speedups from convolution assembly routine vs. C Quantization effects on filter stability (IIR) FIR vs. IIR: how to decide which one to use

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Lab 3. Digital Filters

Test Equipment Agilent Function Generator HP 60 MHz Digital Storage Oscilloscope Spectrum Analyzer

Evaluation Procedure Sweep filters with sinusoids to construct magnitude and

phase responses• Manually using test equipment, or• Automatically by LabVIEW DSP Test Integration Toolkit

Check filter output for cut-off frequency, roll-off factor… FIR: Compare execution times (in Code Composer) of

• C without compiler optimizations• C with compiler optimizations• C callable assembly language routine

IIR: Compute execution times (in Code Composer)

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Lab 4. Data Scramblers

Aim: Generate pseudo-random bit sequences Build data scrambler for given connection polynomial Descramble data via descrambler Obtain statistics of scrambled binary sequence

Expected outcomes are to understand Principles of pseudo-noise (PN) sequence generation Identify applications in communication systems

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Lab 4. Data Scramblers

Evaluation procedure Check scrambler output for various deterministic

sequences as input(s) Descrambler must recover input sequence from

scrambled one Test for sequence period, autocorrelation and other

significant statistical properties

Using DSP Test & Measurement Toolkit instead Compute autocorrelation of PN sequence Compare this autocorrelation with that of white noise

generated by LabVIEW to measue PN sequence quality

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Lab 5. Digital PAM Transceiver

Aim: Develop PAM transceiver blocks in C Amplitude mapping to PAM levels Interpolation filter bank for pulse shaping filter Clock recovery via phase locked loops

gT,0[n]

gT,1[n]an

D/A TransmitFilter

Filter bank implementation

4-PAM

d

-d

-3 d

3 d

D/A TransmitFilter

an gT[n] L

L samples per symbol

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Lab 5. Digital PAM Transceiver

Expected Outcomes are to understand Basics of PAM modulation Zero inter-symbol interference condition Clock synchronization issues

Test Equipment: Same as Lab 3 Evaluation Procedure

Generate eye diagram to visualize PAM signal quality Observe spectrum of modulated signal Prepare DSP modules to test symbol clock frequency

recovery subsystem

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Lab 6. Digital QAM Transmitter

Aim: Develop QAM transmitter blocks in C Differential encoding of digital data Constellation mapping to QAM levels Interpolation filter bank for pulse shaping filter

D/A

an gT[m] L

+cos(w0 n)

bn gT[m] L

sin(w0 n)

Serial/parallel

converter1

Bit stream

Map to 2-D constellationJ

L samples per symbol

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Lab 6. Digital QAM Transmitter

Expected outcomes are to understand In-phase and quadrature modulation principles Bandwidth efficiency issues

Test equipment: same as Lab 5 Evaluation procedure

Verify differential encoding and QAM mapping Generate eye diagram to visualize QAM signal quality Observe spectrum of modulated signal

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Lab 7. Digital QAM Receiver – Part 1

Aim: Develop QAM receiver blocks in C Carrier recovery Coherent demodulation Decoding of QAM levels to digital data

Expected outcomes are to understand Carrier detection and phase adjustment Design of receive filter Probability of error analysis to evaluate decoder

Test equipment: Same as Lab 6 Evaluation procedure

Recover and display carrier on scope Regenerate eye diagram and QAM constellation Observe signal spectra at each decoding stage

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Multicarrier modulation divides broadband channel into narrowband subchannels No inter-symbol interference if constant subchannel

gain and ideal sampling Based on fast Fourier transform (FFT)

ADSL/VDSL and IEEE 802.11a/g & 802.16a

subchannel

frequency

magnitude

carrier

channel

Each ADSL/VDSL subchannel is 4.3 kHz wide (about a voice channel) and carries QAM encoded subsymbol

Developed Voiceband Transceiver: Now What? Got Anything Faster?

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Multicarrier Modulation by IFFT

2/NX

x

tfje 12

1X

x

tfje 22

x

tfj Ne 2/2

+

g(t)

2X g(t)

g(t)

x

nN

je

12

1X

x

nN

je

22

x

nN

Nj

e2/

2

+2X

2/NX

Discretetime

g(t) : pulse shaping filter Xi : ith symbol from encoder

I

QiX

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Multicarrier Modulation (ADSL)

N-pointInverse

FastFourier

Transform(IFFT)

X1

X2

X1*

x0

x1

x2

xN-1X2

*

XN/2

XN/2-1*

X0

N real-valuedtime

samplesformsADSL

symbol

N/2 subchannels

(carriers)

QAM Mapping

00101

I

QiX

Mirror complex data (in blue) andtake conjugates:

)cos( 2 jj ee

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Multicarrier Modulation (ADSL)

CP: Cyclic Prefix

N samplesv samples

CP CPs y m b o l ( i ) s y m b o l ( i+1)

copy copy

D/A + transmit filter

ADSL downstream upstream

CP 32 4 N 512 64

Inverse FFT

ADSL frame is an ADSL symbol plus cyclic prefix

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Multicarrier Demodulation (ADSL)

N-pointFast

FourierTransform

(FFT)

N time

samples

N/2 subchannels

(carriers)

S/P

*1

~X

*12

~NX

2

~NX

12

~NX

0

~X

0~x

1~x

2~x

1~

Nx

26

P/S QAM 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

Bits

00110

each block programmed in lab and covered in one full lectureeach block covered in one full lecture

P/S parallel-to-serial S/P serial-to-parallel FFT fast Fourier transform

ADSL Transceiver: Data Xmission

2.208 MHz

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Modem Design, Implementation, and Testing Using NI’s LabVIEW

Dr. Brian L. EvansAssociate Professor

The University of Texas at Austinbevans@ece.utexas.edu

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