Computer Arch. And Embedded Processors Embedded Real-Time Signal Processing Systems Prof. Brian L. Evans The University of Texas at Austin August 26, 2008

Computer Arch. And Embedded Processors

Embedded Real-TimeSignal Processing Systems

Prof. Brian L. EvansThe University of Texas at Austin

August 26, 2008

http://www.ece.utexas.edu

http://signal.ece.utexas.edu

http://www.cps.utexas.edu

http://www.wncg.org


Introduction

• Embedded systemso Implement dedicated, application-specific taskso Work behind the scenes

• Real-time behavioro Guaranteed delivery [Prof. Yale Patt]o Often reactive to environment

• Signal processingo Signals are acquired measurements or modelso Processing transforms input signals to output

signals


Embedded Systems

• Computers masquerading as non-computers

Casio Camera Watch

Nokia 7110 Browser Phone

Sony Playstation 2

Philips DVD player

Philips TiVo Recorder

Slide courtesy of Prof. Stephen A. Edwards of Columbia University


Signal Processing Applications

• 2007 units shipped, consumer productso 800M cell phones 100M DSL

modemso 250M PCs 60M cars/truckso 100M digital still cameras 30M printers

• Embedded processor cost?

Product (2004 Calendar Year)

Average Unit Price

Annual Revenue

Wireless phone $136 $11.5 Billion

Digital cameras $271 $ 4.2 Billion

Portable CD players $ 48 $ 0.9 Billion

MP3 players $137 $ 0.7 Billion

Compact audio systems $111 $ 0.5 Billion

CEA

Mark

et

Rese

arc

h

(US)


One Family: Digital Signal Processors

• As low as $2/processor in volume orders• Small physical area and volume• Predictable input/output rates

o Deterministic interrupt service routine latency

• On-chip direct memory access controllerso Streams input/output separately from CPUo Sends interrupt to CPU when block read/written

• Power consumptiono For battery-powered products: 10-100 mWo For wall-powered products: 1-10 W


Embedded Signal Processing Lab

• Signal processing for communication systems• Image acquisition, analysis, and display• Electronic design automation (EDA)• Alumni: 16 PhD, 8 MS, 100 BS students• Current: 8 PhD, 3 MS, 8 BS students

Sys. Subsys. Theory Alg. Release Design Embed. Release

ADSL equalizer Y Y Matlab Y HW/SW DSP/C

OFDM res. alloc. Y Y LabVIEW Y SW DSP/C

Xceiver RFI mitig. Y Y Matlab Y

Display halftoning Y Y Matlab/C Y

EDA fix. pt. con. Y Matlab Y HW

Founded 1996


Computer Platform RFI

• RFI from clocks, clock harmonics, busseso Reduces communication performance for

embedded wireless data transceivers

• Objectiveo Improve data reliability by factor of 10

• Approacheso Model RFI using impulsive

noise modelso Filtering/detection based

on RFI models


8

Common Spectral Occupancy

StandardCarrier (GHz)

Wireless Networking

Interfering Clocks and Busses

Bluetooth 2.4Personal Area

NetworkGigabit Ethernet, PCI Express

Bus, LCD clock harmonics

IEEE 802. 11 b/g/n

2.4Wireless LAN

(Wi-Fi)Gigabit Ethernet, PCI Express

Bus, LCD clock harmonics

IEEE 802.16e

2.5–2.69 3.3–3.8

5.725–5.85

Mobile Broadband

(Wi-Max)

PCI Express Bus,LCD clock harmonics

IEEE 802.11a

5.2Wireless LAN

(Wi-Fi)PCI Express Bus,

LCD clock harmonics


Statistical Models

• Middleton Class A • Symmetric Alpha Stable

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-8

-6

-4

-2

0

2

4

6

8

10

Frequency

Pow

er S

pect

rum

Mag

nitu

de (

dB)

Power Spectal Density of Class A noise, A = 0.15, = 0.1

Power Spectral Density Power Spectral Density

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-10

-8

-6

-4

-2

0

2

4

6

8

10

FrequencyP

ower

Spe

ctru

m M

agni

tude

(dB

)

Power Spectal Density of S S noise, = 1.5, = 10, = 0

with A = 0.15 and = 0.1 with = 1.5, = 0 and = 10


10

Proposed Contributions

Computer Platform Noise Modelling

Evaluate fit of measured RFI data to noise modelsNarrowband Interference: Middleton Class A modelBroadband Interference: Symmetric Alpha Stable

Parameter Estimation Evaluate estimation accuracy vs complexity tradeoffs

Filtering / Detection Evaluate communication performance vs complexity tradeoffs• Middleton Class A: Correlation receiver, Wiener filtering and Bayesian detector• Symmetric Alpha Stable: Myriad filtering, hole punching, and Bayesian detector


-5 -4 -3 -2 -1 0 1 2 3 4 50

0.2

0.4

0.6

0.8

1

Measured Data Fitting

Noise amplitude

Pro

babi

lity

Den

sity

Fun

ctio

n

Measured PDF

Estimated AlphaStable PDFEstimated MiddletonClass A PDF

Estimated Equi-powerGaussian PDF

11

Estimated Parameters

Symmetric Alpha Stable ModelLocalization (δ) 0.0043

Distance 0.0514

Characteristic exp. (α) 1.2105

Dispersion (γ) 0.2413

Middleton Class A ModelOverlap Index (A) 0.1036 Distance

0.0825Gaussian Factor (Γ) 0.7763

Gaussian ModelMean (µ) 0 Distance

0.2217Variance (σ2) 1

Distance: Kullback-Leibler divergence

Results for Measured RFI Data Set

• 80,000 samples collected using 20 GSPS scope


12

Pulse shapeRaised cosine

10 samples per symbol10 symbols per pulse

ChannelA = 0.35

= 0.5 × 10-3

Memoryless

Method Comp. Detection Perform.

Correl. Low Low

Wiener Medium Low

Bayesian Approx.

Medium High

Bayesian High High

Detection in Middleton Class A Noise

SNR is signal-to-noise ratio, i.e. transmitted signal power over channel noise power


13

Method Comp. Detection Perform.

Hole Punching

Low Medium

Selection Myriad

Low Medium

Bayesian Approx.

Medium High

Optimal Myriad

High Medium-10 -5 0 5 10 15 20

10-2

10-1

100

Generalized SNR

BE

R

Communication Performance (=0.9, =0, M=12)

Matched FilterHole PunchingMAPMyriad

Use dispersion parameter in place of noise variance to generalize SNR

Detection for Symmetric Alpha Stable


Conclusion

• Using impulsive noise models, reduce bit error rates (i.e. increase data reliability)o By factor of 10-100 for Middleton Class A modelo By factor of 10 for Symmetric Alpha Stable model

• Tractable parameter estimation algorithmso Middleton Class A: iterative + polynomial rootingo Symmetric Alpha Stable: non-iterative

• UT Austin RFI Mitigation Toolboxhttp://www.ece.utexas.edu/~bevans/projects/rfi

• Future extensions


References• D. Middleton, “Non-Gaussian noise models in signal

processing for telecommunications: New methods and results for Class A and Class B noise models”, IEEE Trans. Info. Theory, vol. 45, no. 4, pp. 1129-1149, May 1999.

• S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM algorithms”, IEEE Trans. Info. Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991.

• G. A. Tsihrintzis and C. L. Nikias, "Fast estimation of the parameters of alpha-stable impulsive interference", IEEE Trans. Signal Proc., vol. 44, Issue 6, pp. 1492-1503, Jun. 1996.

• A. Spaulding and D. Middleton, “Optimum Reception in an Impulsive Interference Environment-Part I: Coherent Detection”, IEEE Trans. Comm., vol. 25, no. 9, Sep. 1977.

• A. Spaulding and D. Middleton, “Optimum Reception in an Impulsive Interference Environment-Part II: Incoherent Detection”, IEEE Trans. Comm., vol. 25, no. 9, Sep. 1977.


References

• B. Widrow et al., “Principles and Applications”, Proc. of the IEEE, vol. 63, no.12, Sep. 1975.

• J. G. Gonzalez and G. R. Arce, “Optimality of the Myriad Filter in Practical Impulsive-Noise Environments”, IEEE Transactions on Signal Processing, vol 49, no. 2, Feb. 2001.

• M. Nassar, K. Gulati, A. K. Sujeeth, N. Aghasadeghi, B. L. Evans and K. R. Tinsley, "Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers", Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 30-Apr. 4, 2008.

• K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans and K. R. Tinsley, and X. E. Lin, "MIMO Receiver Design in the Presence of Radio Frequency Interference", Proc. IEEE Global Communications Conf., Dec. 2008, accepted for publication.

Documents

Computer Arch. And Embedded Processors Embedded Real-Time Signal Processing Systems Prof. Brian L. Evans The University of Texas at Austin August 26, 2008