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Prof. Brian L. Evans Cockrell School of Engineering. Design of Interference-Aware Communication Systems. Presentation to Freescale Semiconductor. Wireless Networking & Comm. Group. Applications. 2. Systems of systems. Networks of networks. Networks of systems. Systems. Networks.

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Design of Interference-Aware Communication Systems


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design of interference aware communication systems
Wireless Networking and Communications Group

Prof. Brian L. Evans

Cockrell School of Engineering

Design of Interference-Aware Communication Systems

Presentation to Freescale Semiconductor

wireless networking comm group
Wireless Networking & Comm. Group

Applications

2

Systems of systems

Networks of networks

Networks of systems

Systems

Networks

Middleware

Compilers

Protocols

Operating systems

Communication links

Communication

Processors

Computation

Circuit design

Waveforms

Data acq.

Antennas

Wires

Collaboration with UT faculty outside of WNCG

17 faculty120 PhD students

Devices

wireless networking comm group3
Wireless Networking & Comm. Group

3

Communications

Networking

Applications

B. Evans

Embedded DSP

J. Andrews

Communication

B. Bard

Security

S. Nettles

Network Design

A. Bovik

Image/Video

C. Caramanis

Optimization

Computation

A. Gerstlauer

Embedded Sys

G. de Veciana

Networking

  • Tewfik
  • Biomedical

R. Heath

Comm/DSP

S. Sanghavi

Network Science

S. Shakkottai

Network Theory

H. Vikalo

Genomic DSP

L. Qiu

Network Design

T. Rappaport

RF IC Design

T. Humphreys

GPS/Navigation

S. Vishwanath

Sensor Networks

completed projects prof evans
Completed Projects – Prof. Evans

4

DSP Digital Signal Processor LTE Long-Term Evolution (cellular) MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation

17 PhD and 8 MS alumni

on going projects prof evans
On-Going Projects – Prof. Evans

5

DSP Digital Signal Processor PXI PCI Extensions for InstrumentationMIMO Multi-Input Multi-Output RFI Radio Frequency Interference

8 PhD and 3 MS students

radio frequency interference rfi
Radio Frequency Interference (RFI)

(Wimax Basestation)

(Wi-Fi)

(Microwave)

(Wi-Fi)

(Wimax)

antenna

(Wimax Mobile)

  • WirelessCommunication Sources
  • Closely located sources
  • Coexisting protocols

Non-Communication Sources

Electromagnetic radiation

baseband processor

(Bluetooth)

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

Wireless Networking and Communications Group

rfi modeling mitigation
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)

Project began January 2007

Wireless Networking and Communications Group

rfi modeling
RFI Modeling

Symmetric Alpha Stable

Gaussian Mixture Model

  • Ad hoc and cellular networks
  • Single antenna
  • Instantaneous statistics
  • Sensor networks
  • Ad hoc networks
  • Dense Wi-Fi networks
  • Cellular networks
  • Hotspots (e.g. café)
  • Femtocell networks
  • Single antenna
  • Instantaneous statistics
  • In-cell and out-of-cell femtocell users
  • Cluster of hotspots (e.g. marketplace)
  • Out-of-cell femtocell users

Wireless Networking and Communications Group

rfi mitigation
RFI Mitigation

Interference + Thermal noise

  • Communication performance

Pulse

Shaping

Pre-filtering

Matched Filter

Detection Rule

10 – 100x reduction in bit error rate

~ 8 dB

~ 20 dB

Single carrier, single antenna (SISO)

Single carrier, two antenna (2x2 MIMO)

Wireless Networking and Communications Group

rfi modeling mitigation software
RFI Modeling & Mitigation Software

10

  • 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

Snapshot of a demo

Version 1.5 Aug. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software

Wireless Networking and Communications Group

smart grids the big picture
Smart Grids: The Big Picture

11

Long distance communication : access to isolated houses

Real-Time : Customers profiling enabling good predictions in demand = no need to use an additional power plant

Micro- production : better knowledge of energy produced to balance the network

Demand-side management : boilers are activatedduring the night whenelectricityisavailable

Smart building : significant cost reduction on energy bill through remote monitoring

Anydisturbance due to a storm : action canbetakeninmediatelybased on real-time information

Security featuresFireisdetected : relaycanbeswitched off rapidly

Smart car : charge of electricalvehicleswhile panels are producing

Source: ETSI

powerline communications plc
Powerline Communications (PLC)

“Last mile” low/mediumvoltage line PLC applications

SRC project began August 2010

Goal: Low-cost, power-efficientand robust communications

Automatic meter reading (right)

Smart energy management

Device-specific billing(plug-in hybrid)

12

Source: Powerline Intelligent Metering Evolution (PRIME) Alliance Draft v1.3E

contacts Marie Burnham, Leo Dehner, Mike Dow, Kevin Kemp, Doug Garrity, John Pigott

noise in powerline communications
Noise in Powerline Communications

13

  • 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

Can be lumped together as Generalized Background Noise

  • 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
  • Periodic Impulsive Noise Asynchronous to Main:
  • 50-200kHz
  • Caused by switching power supplies
  • Approximated by narrowbands

Broadband Powerline Communications: Network Design

powerline noise modeling mitigation
Powerline Noise Modeling & Mitigation

14

  • 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

Wireless Networking and Communications Group

powerline communications testbed
Powerline Communications Testbed

Integrate ideas from multiple standards (e.g. PRIME & G3)

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

15

GUI

GUI

designing interference aware receivers
Designing Interference-Aware Receivers

Guard zone

RTS

CTS

RTS / CTS: Request / Clear to send

Example: Dense WiFi Networks

Wireless Networking and Communications Group

statistical models isotropic zero centered
Statistical Models (isotropic, zero centered)

18

  • 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]

Wireless Networking and Communications Group

validating statistical rfi modeling
Validating Statistical RFI Modeling
  • Validated for measurements of radiated RFI from laptop
  • Radiated platform RFI
  • 25 RFI data sets from Intel
  • 50,000 samples at 100 MSPS
  • Laptop activity unknown to us
  • Smaller KL divergence
  • Closer match in distribution
  • Does not imply close match in tail probabilities

Wireless Networking and Communications Group

turbo codes in presence of rfi
Turbo Codes in Presence of RFI

Return

-

Gaussian channel:

Parity 1

Decoder 1

Systematic Data

-

Middleton Class A channel:

-

Decoder 2

Parity 2

-

Extrinsic Information

A-priori

Information

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

Independent of channel statistics

Depends on channel statistics

Independent of channel statistics

Wireless Networking and Communications Group

rfi mitigation using error correction
RFI Mitigation Using Error Correction
  • 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

-

Decoder 1

Interleaver

Parity 1

-

Systematic Data

Interleaver

-

Decoder 2

Interleaver

Parity 2

-

Wireless Networking and Communications Group

extensions to statistical rfi modeling
Extensions to Statistical RFI Modeling
  • Extended to include spatial and temporal dependence
  • Multivariate extensions of
    • Symmetric Alpha Stable
    • Gaussian mixture model
  • Symbol errors
  • Burst errors
  • Coded transmissions
  • Delays in network
  • Multi-antenna receivers

Wireless Networking and Communications Group

rfi modeling joint interference statistics
RFI Modeling: Joint Interference Statistics
  • Throughput performance of ad hoc networks

Ad hoc networksMultivariate Symmetric Alpha Stable

Cellular networksMultivariate Gaussian Mixture Model

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

~1.6x

Wireless Networking and Communications Group

rfi mitigation multi carrier systems
RFI Mitigation: Multi-carrier systems
  • Proposed Receiver
    • Iterative Expectation Maximization (EM) based on noise model
  • Communication Performance
  • Simulation Parameters
  • BPSK Modulation
  • Interference Model2-term Gaussian Mixture Model

~ 5 dB

Wireless Networking and Communications Group

voltage levels in a power grid
Voltage Levels in a Power Grid

25

High Voltage

Medium Voltage

Low Voltage

Source: ERDF

our publications
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”, IEEE Transactions on Signal Processing, to be published, Dec., 2010.
  • 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 Signal Processing Systems, Mar. 2009, invited paper.
  • Conference Publications
  • M. Nassar, X. E. Lin, and B. L. Evans, “Stochastic Modeling of Microwave Oven Interference in WLANs”, Int. Conf. on Comm., Jan. 5-9, 2011, Kyoto, Japan, submitted.
  • 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. on Acoustics, Speech, and Signal Proc., Mar. 14-19, 2010.
  • K. Gulati, A. Chopra, B. L. Evans, and K. R. Tinsley, “Statistical Modeling of Co-Channel Interference”, Proc.IEEE Int. Global Communications Conf., Nov. 30-Dec. 4, 2009.
  • Cont…

Wireless Networking and Communications Group

our publications27
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, “MIMO Receiver 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.
  • 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 1.5, Aug. 16, 2010.

Wireless Networking and Communications Group

references
References

RFI Modeling

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Wireless Networking and Communications Group

references29
References

Parameter Estimation

  • 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 .
  • 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 Networks

  • 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.
  • 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.
  • 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.
  • 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.

Wireless Networking and Communications Group

references30
References

Communication Performance of Wireless Networks (cont…)

  • 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.
  • 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
  • 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.
  • 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 RFI

  • 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
  • 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

Wireless Networking and Communications Group

references31
References

Receiver Design to Mitigate RFI (cont…)

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

  • 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

Wireless Networking and Communications Group