Evans interferenceawaremar2011

Design of Interference-Aware Communication Systems

WNCG “Dallas or Bust” Roadtrip

Wireless Networking and Communications

Group

24 Mar 2011

Prof. Brian L. Evans

Cockrell School of Engineering

Completed Projects – Prof. Evans

System Contribution SW release Prototype Companies

ADSL equalization MATLAB DSP/C Freescale, TI

MIMO testbed LabVIEW LabVIEW/PXI Oil & Gas

Wimax/LTE resource allocation LabVIEW DSP/C Freescale, TI

Camera image acquisition MATLAB DSP/C Intel, Ricoh

Display image halftoning MATLAB C HP, Xerox

video halftoning MATLAB Qualcomm

CAD tools fixed point conv. MATLAB FPGA Intel, NI

DSP Digital Signal Processor LTE Long-Term Evolution (cellular)

MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation

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17 PhD and 8 MS alumni

On-Going Projects – Prof. Evans

System Contributions SW release Prototype Companies

Powerline Comm.

noise reduction; testbed

LabVIEW LabVIEW and C/C++ in PXI

Freescale, IBM, SRC, TI

Wimax/WiFi RFI mitigation MATLAB LabVIEW/PXI Intel

RF Test noise reduction LabVIEW LabVIEW/PXI NI

Underwater Comm.

MIMO testbed;space-time meth.

MATLAB Lake Travis testbed

Navy

CAD Tools dist. computing. Linux/C++ Navy sonar Navy, NI

DSP Digital Signal Processor PXI PCI Extensions for Instrumentation

MIMO Multi-Input Multi-Output RFI Radio Frequency Interference

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8 PhD and 4 MS students

Radio Frequency Interference (RFI)


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WirelessCommunication Sources

• Closely located sources• Coexisting protocols

Non-Communication SourcesElectromagnetic radiation

Computational Platform• Clock circuitry• Power amplifiers• Co-located transceivers

antenna

baseband processor

(Wi-Fi)

(Wimax Basestation)

(Wimax Mobile)

(Bluetooth)

(Microwave) (Wi-Fi) (Wimax)

RFI Modeling & Mitigation

Problem: RFI degrades communication performance

Approach: Statistical modeling of RFI as impulsive noise

Solution: Receiver design

Listen to environment

Build statistical model

Use model to mitigate RFI

Goal: Improve communication

10-100x reduction in bit error rate (done)

10x improvement in network throughput (on-going)


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5

Project began January 2007

RFI Modeling


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6

• Sensor networks• Ad hoc networks

• Dense Wi-Fi networks

• Cluster of hotspots (e.g. marketplace)

• In-cell and out-of-cell femtocell users

• Out-of-cell femtocell users

• Cellular networks• Hotspots (e.g. café)

Symmetric Alpha Stable

Ad hoc and cellular networks

•Single antenna•Instantaneous statistics

Femtocell networks

•Single antenna•Instantaneous statistics

Gaussian Mixture Model

RFI Mitigation

Communication performance


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Pulse Shaping

Pre-filteringMatched

FilterDetection

Rule

Interference + Thermal noise

-10 -5 0 5 10 15 20

10-3

10-2

10-1

SNR [in dB]

Vecto

r S

ym

bol E

rror

Rate

Optimal ML Receiver (for Gaussian noise)

Optimal ML Receiver (for Middleton Class A)

Sub-Optimal ML Receiver (Four-Piece)

Sub-Optimal ML Receiver (Two-Piece)

-40 -35 -30 -25 -20 -15 -10 -5

10-3

10-2

10-1

100

Signal to Noise Ratio (SNR) [in dB]

Sym

bol E

rror

Rate

Correlation Receiver

Bayesian Detection

Myriad Pre-filtering

Single carrier, single antenna (SISO) Single carrier, two antenna (2x2 MIMO)

~ 20 dB~ 8 dB

10 – 100x reduction in bit error rate

RFI Modeling & Mitigation Software

Freely distributable toolbox in MATLAB

Simulation of RFI modeling/mitigation

RFI generation

Measured RFI fitting

Filtering and detection methods

Demos for RFI modeling and mitigation

Example uses

System simulation (e.g. Wimax or powerline communications)

Fit RFI measurements to statistical models


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8

Version 1.6 beta Dec. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software

Snapshot of a demo

Voltage Levels in Power Grid

Medium-Voltage

Low-Voltage

High-Voltage

Source: Électricité

Réseau Dist. France

(ERDF)

9

“Last mile” powerline communications on low/medium voltage line

Concentrator

Powerline Communications (PLC)

Concentrator controls mediumto subscriber meters Plays role of basestation

Applications Automatic meter reading (right)

Smart energy management

Device-specific billing(plug-in hybrid)

Goal: Improve reliability & rate Mitigate impulsive noise

Multichannel transmission

Source: Powerline Intelligent

Metering Evolution (PRIME)

Alliance Draft v1.3E

1

0

Noise in Powerline Communications

Superposition of five noise sources [Zimmermann, 2000]

Different types of power spectral densities (PSDs)

Colored Background Noise:• PSD decreases with frequency

• Superposition of numerous noise sources

with lower intensity

• Time varying (order of minutes and hours)

Narrowband Noise:• Sinusoidal with modulated amplitudes

• Affects several subbands

• Caused by medium and shortwave

broadcast channels

Periodic Impulsive Noise Asynchronous to Main:• 50-200kHz

• Caused by switching power supplies

• Approximated by narrowbands

Periodic Impulsive Noise Synchronous to Main:• 50-100Hz, Short duration impulses

• PSD decreases with frequency

• Caused by power convertors

Asynchronous Impulsive Noise:• Caused by switching transients

• Arbitrary interarrivals with micro-

millisecond durations

• 50dB above background noise

Broadband Powerline Communications: Network Design

Can be lumped together as

Generalized Background Noise

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1

Powerline Noise Modeling & Mitigation

Problem: Impulsive noise is primaryimpairment in powerline communications

Approach: Statistical modeling

Solution: Receiver design

Listen to environment

Build statistical model

Use model to mitigate RFI

Goal: Improve communication

10-100x reduction in bit error rate

10x improvement in network throughput


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2

Preliminary Noise Measurement

13

0 10 20 30 40 50 60 70 80 90

-125

-120

-115

-110

-105

-100

-95

-90

-85

-80

-75

Frequency (kHz)

Pow

er/

frequency (

dB

/Hz)

Power Spectral Density Estimate


14

0 10 20 30 40 50 60 70 80 90

-125

-120

-115

-110

-105

-100

-95

-90

-85

-80

-75

Frequency (kHz)

Pow

er/

frequency (

dB

/Hz)


Colored Background

Noise


15

0 10 20 30 40 50 60 70 80 90

-125

-120

-115

-110

-105

-100

-95

-90

-85

-80

-75

Frequency (kHz)

Pow

er/

frequency (

dB

/Hz)


Colored Background

Noise

Narrowband Noise


16

0 10 20 30 40 50 60 70 80 90

-125

-120

-115

-110

-105

-100

-95

-90

-85

-80

-75

Frequency (kHz)

Pow

er/

frequency (

dB

/Hz)


Colored Background

Noise

Narrowband Noise

Periodic and

Asynchronous Noise

Powerline Communications Testbed

Integrate ideas from multiple standards (e.g. PRIME) Quantify communication performance vs complexity tradeoffs

Extend our existing real-time DSL testbed (deployed in field)

Adaptive signal processing methods Channel modeling, impulsive noise filters & equalizers

Medium access control layer scheduling Effective and adaptive resource allocation

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7

GUIGUI

Thank you for your attention!1

8

Backup

Designing Interference-Aware Receivers


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0

RTS / CTS: Request / Clear to send

Guard zone

Example: Dense WiFi Networks

Medium Access Control (MAC) Layer

• Interference sense and avoid• Optimize MAC parameters

(e.g. guard zone size, transmit power)

Physical (PHY) Layer

• Receiver pre-filtering• Receiver detection• Forward error correction

Statistical Modeling of RFI

• Derive analytically• Estimate parameters at receiver

Statistical Models (isotropic, zero centered)

Symmetric Alpha Stable [Furutsu & Ishida, 1961] [Sousa, 1992]

Characteristic function

Gaussian Mixture Model [Sorenson & Alspach, 1971]

Amplitude distribution

Middleton Class A (w/o Gaussian component) [Middleton, 1977]


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1

Validating Statistical RFI Modeling

Validated for measurements of radiated RFI from laptop


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2

Smaller KL divergence• Closer match in distribution• Does not imply close match in

tail probabilities

Radiated platform RFI• 25 RFI data sets from Intel• 50,000 samples at 100 MSPS• Laptop activity unknown to us

0 5 10 15 20 250

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Measurement Set

Kullb

ack-L

eib

ler

div

erg

ence


Middleton Class A

Gaussian Mixture Model

Gaussian

Turbo Codes in Presence of RFI


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3

Decoder 1Parity 1Systematic Data

Decoder 2

Parity 2

1

-

-

-

-

A-priori Information

Depends on channel statistics

Independent of channel statistics

Gaussian channel:

Middleton Class A channel:

Independentof channel statistics

Extrinsic Information

Leads to a 10dB improvement at BER of 10-5 [Umehara03]

Return

RFI Mitigation Using Error Correction


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2

4

Decoder 1Parity 1

Systematic Data

Decoder 2

Interleaver

Parity 2 Interleaver

-

-

-

-

Interleaver

Turbo decoder

Decoding depends on the RFI statistics

10 dB improvement at BER 10-5 can be achieved using accurate RFI statistics [Umehara, 2003]

Return

Extended to include spatial and temporal dependence

Multivariate extensions of


Gaussian mixture model

Extensions to Statistical RFI Modeling


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5

Statistical Modeling of RFI

Single AntennaInstantaneous statistics

Spatial Dependence Temporal Dependence

• Multi-antenna receivers• Symbol errors • Burst errors• Coded transmissions• Delays in network

RFI Modeling: Joint Interference Statistics

Throughput performance of ad hoc networks


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6

Ad hoc networks

Multivariate Symmetric Alpha StableCellular networks

Multivariate Gaussian Mixture Model

2 4 6 8 10 12 14 162

3

4

5

6

7

8

9

10

Expected ON Time of a User (time slots)

Ne

two

rk T

hro

ug

hp

ut (n

orm

alize

d)

[ b

ps/H

z/a

rea

]

With RFI Mitigation

Without RFI Mitigation

Network throughput improved by optimizing distribution of ON Time of users (MAC parameter)

~1.6x

RFI Mitigation: Multi-carrier systems

Proposed Receiver

Iterative Expectation Maximization (EM) based on noise model

Communication Performance


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2

7

-10 -5 0 5 10 15 20

10-4

10-3

10-2

10-1

100

Signal to Noise Ratio (SNR) [in dB]

Bit E

rro

r R

ate

OFDM Receiver

Single Carrier

Proposed EM-based Receiver

Simulation Parameters

• BPSK Modulation• Interference Model

2-term Gaussian Mixture Model~ 5 dB

Smart Grids: The Big Picture

Smart car : charge of

electricalvehicleswhile

panels are producing

Long distance

communication :

access to isolated

houses

Real-Time :

Customers profiling

enabling good

predictions in demand

= no need to use an

additional power plant

Anydisturbance due to a

storm : action

canbetakeninmediatelybas

ed on real-time

information

Smart building :

significant cost reduction

on energy bill through

remote monitoring

Demand-side

management : boilers

are activatedduring the

night

whenelectricityisavaila

ble

Micro- production

: better knowledge

of energy produced

to balance the

network

Security

featuresFireisdetect

ed :

relaycanbeswitched

off rapidly

Source: ETSI

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8

Networks of networks

Networks

Data acq.

AntennasWires

Communication links

Processors

Systems

Compilers

Circuit design

Protocols

Systems of systems

Middleware

Operating systems

Devices

Waveforms

Networks of systems

Applications2

9

Wireless Networking & Comm. Group

17 faculty140 grad students

Collaboration with UT faculty outside of WNCG

Wireless Networking & Comm. Group

A. Gerstlauer

Embedded Sys

G. de Veciana

Networking

S. Vishwanath

Sensor Networks

S. Nettles

Network Design

S. Shakkottai

Network Theory

J. Andrews

Communication

L. Qiu

Network Design

C. Caramanis

Optimization

H. Vikalo

Genomic DSP

A. Bovik

Image/Video

B. Evans

Embedded DSP

T. Humphreys

GPS/Navigation

T. Rappaport

RF IC Design

R. Heath

Comm/DSP

B. Bard

Security

S. Sanghavi

Network Science

A. Tewfik

Biomedical

Communications Networking Applications

3

0

Com

puta

tion

Our Publications

Journal Publications• K. Gulati, B. L. Evans, J. G. Andrews, and K. R. Tinsley, “Statistics of Co-Channel

Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers”, IEEETransactions on Signal Processing, vol. 58, no. 12, Dec. 2010, pp. 6207-6222.

• M. Nassar, K. Gulati, M. R. DeYoung, B. L. Evans and K. R. Tinsley, “Mitigating Near-Field Interference in Laptop Embedded Wireless Transceivers”, Journal of SignalProcessing Systems, Mar. 2009, invited paper.

Conference Publications• M. Nassar, X. E. Lin, and B. L. Evans, “Stochastic Modeling of Microwave Oven

Interference in WLANs”, Proc. IEEE Int. Conf. on Comm., Jun. 5-9, 2011.• K. Gulati, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel

Interference in a Field of Poisson Distributed Interferers”, Proc. IEEE Int. Conf. onAcoustics, Speech, and Signal Proc., Mar. 14-19, 2010.

• K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-ChannelInterference”, Proc. IEEE Int. Global Comm. Conf., Nov. 30-Dec. 4, 2009.

Cont…

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Our Publications

Conference Publications (cont…)• A. Chopra, K. Gulati, B. L. Evans, K. R. Tinsley, and C. Sreerama, “Performance Bounds

of MIMO Receivers in the Presence of Radio Frequency Interference”, Proc. IEEE Int.Conf. on Acoustics, Speech, and Signal Proc., Apr. 19-24, 2009.

• K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans, K. R. Tinsley, and X. E. Lin, “MIMOReceiver Design in the Presence of Radio Frequency Interference”, Proc. IEEE Int.Global Communications Conf., Nov. 30-Dec. 4th, 2008.

• 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.

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Software Releases• K. Gulati, M. Nassar, A. Chopra, B. Okafor, M. R. DeYoung, N. Aghasadeghi, A. Sujeeth,

and B. L. Evans, "Radio Frequency Interference Modeling and Mitigation Toolbox in MATLAB", version 1.6 beta, Dec. 16, 2010.

References

RFI Modeling1. 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.

2. K. Furutsu and T. Ishida, “On the theory of amplitude distributions of impulsive random noise,” J. Appl. Phys., vol. 32, no. 7, pp. 1206–1221, 1961.

3. J. Ilow and D . Hatzinakos, “Analytic alpha-stable noise modeling in a Poisson field of interferers or scatterers”, IEEE transactions on signal processing, vol. 46, no. 6, pp. 1601-1611, 1998.

4. E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Transactions on Information Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992.

5. X. Yang and A. Petropulu, “Co-channel interference modeling and analysis in a Poisson field of interferers in wireless communications,” IEEE Transactions on Signal Processing, vol. 51, no. 1, pp. 64–76, Jan. 2003.

6. E. Salbaroli and A. Zanella, “Interference analysis in a Poisson field of nodes of finite area,” IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1776–1783, May 2009.

7. M. Z. Win, P. C. Pinto, and L. A. Shepp, “A mathematical theory of network interference and its applications,” Proceedings of the IEEE, vol. 97, no. 2, pp. 205–230, Feb. 2009.

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References

Parameter Estimation1. S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters via the EM

[Expectation-Maximization] algorithms”, IEEE Trans. Info. Theory, vol. 37, no. 1, pp. 60-72, Jan. 1991 .

2. 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.

Communication Performance of Wireless Networks1. R. Ganti and M. Haenggi, “Interference and outage in clustered wireless ad hoc networks,” IEEE

Transactions on Information Theory, vol. 55, no. 9, pp. 4067–4086, Sep. 2009.

2. A. Hasan and J. G. Andrews, “The guard zone in wireless ad hoc networks,” IEEE Transactions on Wireless Communications, vol. 4, no. 3, pp. 897–906, Mar. 2007.

3. X. Yang and G. de Veciana, “Inducing multiscale spatial clustering using multistage MAC contention in spread spectrum ad hoc networks,” IEEE/ACM Transactions on Networking, vol. 15, no. 6, pp. 1387–1400, Dec. 2007.

4. S. Weber, X. Yang, J. G. Andrews, and G. de Veciana, “Transmission capacity of wireless ad hoc networks with outage constraints,” IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4091-4102, Dec. 2005.

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References

Communication Performance of Wireless Networks (cont…)5. S. Weber, J. G. Andrews, and N. Jindal, “Inducing multiscale spatial clustering using multistage MAC

contention in spread spectrum ad hoc networks,” IEEE Transactions on Information Theory, vol. 53, no. 11, pp. 4127-4149, Nov. 2007.

6. J. G. Andrews, S. Weber, M. Kountouris, and M. Haenggi, “Random access transport capacity,” IEEE Transactions On Wireless Communications, Jan. 2010, submitted. [Online]. Available: http://arxiv.org/abs/0909.5119

7. M. Haenggi, “Local delay in static and highly mobile Poisson networks with ALOHA," in Proc. IEEE International Conference on Communications, Cape Town, South Africa, May 2010.

8. F. Baccelli and B. Blaszczyszyn, “A New Phase Transitions for Local Delays in MANETs,” in Proc. of IEEE INFOCOM, San Diego, CA,2010, to appear.

Receiver Design to Mitigate RFI1. 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

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

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http://arxiv.org/abs/0909.5119

References

Receiver Design to Mitigate RFI (cont…)3. S. Ambike, J. Ilow, and D. Hatzinakos, “Detection for binary transmission in a mixture of Gaussian

noise and impulsive noise modelled as an alpha-stable process,” IEEE Signal Processing Letters, vol. 1, pp. 55–57, Mar. 1994.

4. G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, John Wiley & Sons, 2005.

5. Y. Eldar and A. Yeredor, “Finite-memory denoising in impulsive noise using Gaussian mixture models,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 48, no. 11, pp. 1069-1077, Nov. 2001.

6. J. H. Kotecha and P. M. Djuric, “Gaussian sum particle ltering,” IEEE Transactions on Signal Processing, vol. 51, no. 10, pp. 2602-2612, Oct. 2003.

7. J. Haring and A.J. Han Vick, “Iterative Decoding of Codes Over Complex Numbers for Impulsive Noise Channels”, IEEE Trans. On Info. Theory, vol 49, no. 5, May 2003.

8. Ping Gao and C. Tepedelenlioglu. “Space-time coding over mimo channels with impulsive noise”, IEEE Trans. on Wireless Comm., 6(1):220–229, January 2007.

RFI Measurements and Impact1. J. Shi, A. Bettner, G. Chinn, K. Slattery and X. Dong, "A study of platform EMI from LCD panels –

impact on wireless, root causes and mitigation methods,“ IEEE International Symposium onElectromagnetic Compatibility, vol.3, no., pp. 626-631, 14-18 Aug. 2006

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