Design of interference aware wireless communication systems
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Brian L. Evans Lead Graduate Students Aditya Chopra, Kapil Gulati , and Marcel Nassar Collaborators from Intel Labs Current: Nageen Himayat , Kirk Skeba , and Srikathyayani Srikanteswara Past: Chaitanya Sreerama , Eddie X. Lin, Alberto A. Ochoa, and Keith R. Tinsley.

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Design of Interference-Aware Wireless Communication Systems

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Design of interference aware wireless communication systems

Wireless Networking and Communications Group

Brian L. Evans

Lead Graduate Students

Aditya Chopra, KapilGulati, and Marcel Nassar

Collaborators from Intel Labs

Current: NageenHimayat, Kirk Skeba, and SrikathyayaniSrikanteswaraPast: ChaitanyaSreerama, Eddie X. Lin, Alberto A. Ochoa, and Keith R. Tinsley

Design of Interference-Aware Wireless Communication Systems


Outline

Outline

  • Introduction

  • Problem definition

  • Summary of last talk (in Apr. 2010) at Intel Labs

  • Recent results

    • RFI Modeling: Spatial and Temporal dependence

    • RFI Mitigation: Multi-carrier systems

  • Conclusions

  • Future work

Radio FrequencyInterference (RFI)

Wireless Networking and Communications Group


Introduction

Introduction

(WiMAX Basestation)

(Microwave)

(Wi-Fi)

(Wi-Fi)

(WiMAX)

antenna

(WiMAX Mobile)

  • Wireless Communication Sources

  • Closely located sources

  • Coexisting protocols

Non-Communication

Sources

Electromagnetic radiations

baseband processor

(Bluetooth)

  • Computational Platform

  • Clocks, busses, processors

  • Co-located transceivers

Wireless Networking and Communications Group


Problem definition

Problem Definition

  • Problem: Co-channel and adjacent channel interference, and platform noise degrade communication performance

  • Approach: Statistical modeling of RFI

  • Solution: Receiver design

    • Listen to the environment

    • Estimate parameters for RFI statistical models

    • Use parameters to mitigate RFI

  • Goal: Improve communication performance

    • 10-100x reduction in bit error rate

    • 10-100x improvement in network throughput

Wireless Networking and Communications Group


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)

6

  • 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


Summary of last talk rfi modeling

Summary of Last Talk: 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


Summary of last talk rfi modeling1

Summary of Last Talk: RFI Modeling

  • Validated for Laptop radiated RFI

  • Slides available at:http://users.ece.utexas.edu/~bevans/projects/rfi/talks/April2010RFIMitigationTalk.html

  • 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


Summary of last talk rfi mitigation

Summary of Last Talk: 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 extensions

RFI Modeling: Extensions

  • 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 spatial dependence

RFI Modeling: Spatial Dependence

  • System Model

    • Common and exclusive interferers

    • Characterizes receiver separation and directional shielding

  • Joint RFI statistics helpful in choosing spatial transmit and receive techniques

2

1

3

1

1

2

3

1

3

1

2

1

3

2

1

2

3

2

Receiver

Interferers impact all receivers

Interferers impact receiver

1

Wireless Networking and Communications Group


Rfi modeling spatial dependence1

RFI Modeling: Spatial Dependence

  • An impulsive event at one antenna increases probability of impulse event at other antennae

  • Translated environmental parameters to spatial dependence

|RFI at antenna 1|

|RFI at antenna 1|

|RFI at antenna 1|

|RFI at antenna 2|

|RFI at antenna 2|

|RFI at antenna 2|

SPATIALLY INDEPENDENT

SPATIALLY ISOTROPIC

Wireless Networking and Communications Group


Rfi modeling temporal dependence

RFI Modeling: Temporal Dependence

  • System Model

  • Interference is dependent across time slots

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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

  • Single Carrier vs. Multi-Carrier: Intuition

Single Carrier

Multi Carrier (OFDM)

Symbols

Symbols

High Amplitude Impulse

High Amplitude Impulse

Impulsive Noise

Impulsive Noise

  • Impulse energy spread across symbols

  • Noise dependent across subcarriers

  • Optimal decoding: exponential complexity!

  • Impulse energy concentrated in one symbol

  • Symbol Lost

Wireless Networking and Communications Group


Rfi mitigation multi carrier systems1

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


Summary

Summary

Temporal Modeling

Single Antenna (past work)

Multi-Antenna Receivers

Physical (PHY) Layer

Medium Access Control (MAC) Layer

RFI Mitigation:

RFI Avoidance and Mitigation:

  • Detection and Pre-filtering methods

  • Single- and Two-antenna receivers

  • Single- and Multi-carrier systems

  • Microwave Oven Interference

  • Performance of Ad hoc Networks

Impact: 10-100x improvement in communication performance

Wireless Networking and Communications Group


Current and future work

Current and Future Work

Physical (PHY) Layer

Medium Access Control (MAC) Layer

RFI Avoidance and Mitigation:

RFI Avoidance and Mitigation:

  • Communication Performance Analysis

  • MIMO transmit and receive strategies

  • Improving Communication Performance

  • Detection and Pre-filtering methods

  • Error correction coding

  • Interference Avoidance

  • Spectrum Sensing

  • Impact:

  • Improved communication performance

  • Network Performance Analysis

  • Different MAC strategies

  • Improving Network Performance

  • Optimizing MAC parameters

  • MAC algorithms to reduce interference

  • Interference Avoidance

  • Resource Allocation (time, frequency)

  • Impact:

  • Improved network-wide performance

Cognitive Radios

Wireless Networking and Communications Group


Ut austin rfi modeling mitigation toolbox

UT Austin RFI Modeling & Mitigation Toolbox

  • Freely distributable toolbox in MATLAB

  • Simulation environment for RFI modeling and mitigation

    • RFI generation

    • Measured RFI fitting

    • Parameter estimation algorithms

    • Filtering and detection methods

    • Demos for RFI modeling and mitigation

  • Latest Toolbox Release

    Version 1.5, Aug. 16, 2010

Snapshot of a demo

http://users.ece.utexas.edu/~bevans/projects/rfi/software/index.html

Wireless Networking and Communications Group


Related publications

Related 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


Related publications1

Related 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


Design of interference aware wireless communication systems

Thanks !

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


References1

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


References2

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


References3

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

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Backup slides

Backup Slides

  • Introduction

    • Interference avoidance , alignment, and cancellation methods

    • Femtocell networks

  • Statistical Modeling of RFI

    • Computational platform noise

    • Impact of RFI

    • Assumptions for RFI Modeling

    • Transients in digital FIR filters

    • Poisson field of interferers

    • Poisson-Poisson cluster field of interferers

  • Measured RFI Fitting

Backup

Backup

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Wireless Networking and Communications Group


Backup slides cont

Backup Slides (cont…)

  • Gaussian Mixture vs. Alpha Stable

  • Middleton Class A, B, and C models

    • Middleton Class A model

    • Expectation maximization overview

    • Results: EM for Middleton Class A

  • Symmetric Alpha Stable

    • Extreme order statistics based estimator for Alpha Stable

  • Video over impulsive channels

    • Demonstration #1

    • Demonstration #2

Backup

Backup

Backup

Backup

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Backup slides cont1

Backup Slides (cont…)

  • RFI mitigation in SISO systems

    • Our contributions

    • Results: Class A Detection

    • Results: Alpha Stable Detection

  • RFI mitigation in MIMO systems

    • Our contributions

  • Performance bounds for SISO systems

  • Performance bounds for MIMO systems

  • Extensions for multicarrier systems

  • Turbo codes in impulsive channels

Backup

Backup

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Backup slides cont2

Backup Slides (cont…)

  • RFI Toolbox

Backup

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Interference mitigation techniques

Interference Mitigation Techniques

  • Interference avoidance

    • CSMA / CA

  • Interference alignment

    • Example: [Cadambe & Jafar, 2007]

Return

Wireless Networking and Communications Group


Interference mitigation techniques cont

Interference Mitigation Techniques (cont…)

  • Interference cancellation

    Ref: J. G. Andrews, ”Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Vol. 12, No. 2, pp. 19-29, April 2005

Return

Wireless Networking and Communications Group


Femtocell networks

Femtocell Networks

Reference:

V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell Networks: a Survey", IEEE Communications Magazine, Vol. 46, No. 9, pp. 59-67, September 2008

Return

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Common spectral occupancy

Common Spectral Occupancy

Return

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Impact of rfi

Impact of RFI

  • Calculated in terms of desensitization (“desense”)

    • Interference raises noise floor

    • Receiver sensitivity will degrade to maintain SNR

    • Desensitization levels can exceed 10 dB for 802.11a/b/g due to computational platform noise [J. Shi et al., 2006]

      Case Sudy: 802.11b, Channel 2, desense of 11dB

      • More than 50% loss in range

      • Throughput loss up to ~3.5 Mbps for very low receive signal strengths (~ -80 dbm)

Return

Wireless Networking and Communications Group


Impact of lcd clock on 802 11g

Impact of LCD clock on 802.11g

  • Pixel clock 65 MHz

  • LCD Interferers and 802.11g center frequencies

Return

Wireless Networking and Communications Group


Assumptions for rfi modeling

Assumptions for RFI Modeling

  • Key assumptions for Middleton and Alpha Stable models[Middleton, 1977][Furutsu & Ishida, 1961]

    • Infinitely many potential interfering sources with same effective radiation power

    • Power law propagation loss

    • Poisson field of interferers with uniform intensity l

      • Pr(number of interferers = M |area R) ~ Poisson(M; lR)

    • Uniformly distributed emission times

    • Temporally independent (at each sample time)

  • Limitations

    • Alpha Stable models do not include thermal noise

    • Temporal dependence may exist

Return

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Transients in digital fir filters

Transients in Digital FIR Filters

  • 25-Tap FIR Filter

  • Low pass

  • Stopband freq. 0.22 (normalized)

Input

Output

Return

Freq = 0.16

Interference duration = 100 x 1/0.22

Interference duration = 10 * 1/0.22

Transients

Transients Significant w.r.t. Steady State

Transients Ignorable w.r.t. Steady State

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Poisson field of interferers

Poisson Field of Interferers

  • Interferers distributed over parametric annular space

  • Log-characteristic function

Return

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Poisson field of interferers1

Poisson Field of Interferers

Return

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Poisson field of interferers2

Poisson Field of Interferers

Return

Middleton Class A (form of Gaussian Mixture)

Symmetric Alpha Stable

  • Dense Wi-Fi networks

  • Networks with contention based medium access

  • Cellular networks

  • Hotspots (e.g. café)

  • Sensor networks

  • Ad hoc networks

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Poisson field of interferers3

Poisson Field of Interferers

Return

  • Simulation Results (tail probability)

Case III: Infinite-area with guard zone

Case I: Entire Plane

Gaussian and Middleton Class A models are not applicable since mean intensity is infinite

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Poisson field of interferers4

Poisson Field of Interferers

  • Simulation Results (tail probability)

Return

Case II: Finite area annular region

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Poisson poisson cluster field of interferers

Poisson-Poisson Cluster Field of Interferers

  • Cluster centers distributed as spatial Poisson process over

  • Interferers distributed as spatial Poisson process

Return

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Poisson poisson cluster field of interferers1

Poisson-Poisson Cluster Field of Interferers

  • Log-Characteristic function

Return

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Poisson poisson cluster field of interferers2

Poisson-Poisson Cluster Field of Interferers

Return

Gaussian Mixture Model

Symmetric Alpha Stable

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

  • Out-of-cell femtocell users in femtocell networks

  • Cluster of hotspots (e.g. marketplace)

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Poisson poisson cluster field of interferers3

Poisson-Poisson Cluster Field of Interferers

Return

  • Simulation Results (tail probability)

Case III: Infinite-area with guard zone

Case I: Entire Plane

Gaussian and Gaussian mixture models are not applicable since mean intensity is infinite

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Poisson poisson cluster field of interferers4

Poisson-Poisson Cluster Field of Interferers

  • Simulation Results (tail probability)

Return

Case II: Finite area annular region

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Fitting measured laptop rfi data

Fitting Measured Laptop RFI Data

49

  • Statistical-physical models fit data better than Gaussian

Return

  • 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

  • Platform RFI sources

  • May not be Poisson distributed

  • May not have identical emissions

Wireless Networking and Communications Group


Results on measured rfi data

Results on Measured RFI Data

50

  • For measurement set #23

Return

  • Tail probability governs communication performance

  • Bit error rate

  • Outage probability

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Gaussian mixture vs alpha stable

Gaussian Mixture vs. Alpha Stable

  • Gaussian Mixture vs. Symmetric Alpha Stable

Return

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Middleton class a b and c models

[Middleton, 1999]

Middleton Class A, B and C Models

  • Class ANarrowband interference (“coherent” reception)Uniquely represented by 2 parameters

  • Class BBroadband interference (“incoherent” reception)Uniquely represented by six parameters

  • Class CSum of Class A and Class B (approx. Class B)

Return

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Middleton class a model

Parameter

Description

Range

Overlap Index. Product of average number of emissions per second and mean duration of typical emission

A [10-2, 1]

Gaussian Factor. Ratio of second-order moment of Gaussian component to that of non-Gaussian component

Γ  [10-6, 1]

Middleton Class A model

  • Probability Density Function

Return

PDF for A = 0.15,= 0.8

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Expectation maximization overview

Expectation Maximization Overview

54

Return

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Results em estimator for class a

Results: EM Estimator for Class A

Return

55

Normalized Mean-Squared Error in A

Iterations for Parameter A to Converge

K = AG

PDFs with 11 summation terms

50 simulation runs per setting

1000 data samples

Convergence criterion:

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Results em estimator for class a1

Results: EM Estimator for Class A

Return

  • For convergence for A [10-2, 1], worst-case number of iterations for A = 1

  • Estimation accuracy vs. number of iterations tradeoff

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Symmetric alpha stable model

Symmetric Alpha Stable Model

Return

  • Characteristic Function

    • Closed-form PDF expression only forα = 1 (Cauchy), α = 2 (Gaussian),α = 1/2 (Levy), α = 0 (not very useful)

    • Approximate PDF using inverse transform of power series expansion

    • Second-order moments do not exist for α < 2

    • Generally, moments of order > α do not exist

Backup

PDF for  = 1.5,  = 0,  = 10

Backup

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Parameter estimation symmetric alpha stable

Parameter Estimation: Symmetric Alpha Stable

  • Based on extreme order statistics [Tsihrintzis & Nikias, 1996]

  • PDFs of max and min of sequence of i.i.d. data samples

    • PDF of maximum

    • PDF of minimum

  • Extreme order statistics of Symmetric Alpha Stable PDF approach Frechet’s distribution as N goes to infinity

  • Parameter Estimators then based on simple order statistics

    • Advantage:Fast/computationally efficient (non-iterative)

    • Disadvantage:Requires large set of data samples (N~10,000)

Return

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Parameter estimators for alpha stable

Parameter Estimators for Alpha Stable

59

Return

0 < p < α

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Parameter est symmetric alpha stable results

Parameter Est.: Symmetric Alpha Stable Results

Return

  • Data length (N) of 10,000 samples

  • Results averaged over 100 simulation runs

  • Estimate α and “mean” g directly from data

  • Estimate “variance” g from α and δ estimates

Mean squared error in estimate of characteristic exponent α

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Parameter est symmetric alpha stable results1

Parameter Est.: Symmetric Alpha Stable Results

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Mean squared error in estimate of dispersion (“variance”) 

Mean squared error in estimate of localization (“mean”) 

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Extreme order statistics

Extreme Order Statistics

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Video over impulsive channels

Video over Impulsive Channels

  • Video demonstration for MPEG II video stream

    • 10.2 MB compressed stream from camera (142 MB uncompressed)

    • Compressed file sent over additive impulsive noise channel

    • Binary phase shift keyingRaised cosine pulse10 samples/symbol10 symbols/pulse length

    • Composite of transmitted and received MPEG II video streams

      http://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo19dB_correlation.wmv

    • Shows degradation of video quality over impulsive channels with standard receivers (based on Gaussian noise assumption)

Return

Wireless Networking and Communications Group


Video over impulsive channels 2

Video over Impulsive Channels #2

64

  • Video demonstration for MPEG II video stream revisited

    • 5.9 MB compressed stream from camera (124 MB uncompressed)

    • Compressed file sent over additive impulsive noise channel

    • Binary phase shift keyingRaised cosine pulse10 samples/symbol10 symbols/pulse length

    • Composite of transmitted video stream, video stream from a correlation receiver based on Gaussian noise assumption, and video stream for a Bayesian receiver tuned to impulsive noise

      http://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo19dB.wmv

Return

Wireless Networking and Communications Group


Video over impulsive channels 21

Video over Impulsive Channels #2

Structural similarity measure [Wang, Bovik, Sheikh & Simoncelli, 2004]

Score is [0,1] where higher means better video quality

Return

Bit error rates for ~50 million bits sent:

6 x 10-6 for correlation receiver

0 for RFI mitigating receiver (Bayesian)

Frame number


Our contributions

Our Contributions

Return

Mitigation of computational platform noise in single carrier, single antenna systems [Nassar, Gulati, DeYoung, Evans & Tinsley, ICASSP 2008, JSPS 2009]

Wireless Networking and Communications Group


Filtering and detection

Filtering and Detection

  • Assumption

  • Multiple samples of the received signal are available

  • N Path Diversity [Miller, 1972]

  • Oversampling by N[Middleton, 1977]

Impulsive Noise

Pulse

Shaping

Pre-Filtering

Matched Filter

Detection Rule

Return

Middleton Class A noise

Symmetric Alpha Stable noise

Filtering

  • Wiener Filtering (Linear)

    Detection

  • Correlation Receiver (Linear)

  • Bayesian Detector[Spaulding & Middleton, 1977]

  • Small Signal Approximation to Bayesian detector[Spaulding & Middleton, 1977]

Filtering

  • Myriad Filtering

    • Optimal Myriad[Gonzalez & Arce, 2001]

    • Selection Myriad

  • Hole Punching [Ambike et al., 1994]

    Detection

  • Correlation Receiver (Linear)

  • MAP approximation[Kuruoglu, 1998]

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Rfi mitigation in siso systems

RFI Mitigation in SISO systems

Interference + Thermal noise

  • Communication performance

Return

Pulse

Shaping

Pre-filtering

Matched Filter

Detection Rule

Binary Phase Shift Keying

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Results class a detection

Results: Class A Detection

Communication Performance

Return

Binary Phase Shift Keying

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Results alpha stable detection

Results: Alpha Stable Detection

Return

Communication Performance

Same transmitter settings as previous slide

Use dispersion parameter g in place of noise variance to generalize SNR

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Map detection for class a

MAP Detection for Class A

  • Hard decision

  • Bayesian formulation [Spaulding & Middleton, 1977]

  • Equally probable source

Return

Wireless Networking and Communications Group


Map detection for class a small signal approx

Correlation Receiver

MAP Detection for Class A: Small Signal Approx.

72

  • Expand noise PDF pZ(z) by Taylor series about Sj = 0 (j=1,2)‏

  • Approximate MAP detection rule

  • Logarithmic non-linearity + correlation receiver

    • Near-optimal for small amplitude signals

Return

We use 100 terms of the series expansion ford/dxilnpZ(xi) in simulations

Wireless Networking and Communications Group


Incoherent detection

Incoherent Detection

  • Bayesian formulation [Spaulding & Middleton, 1997, pt. II]

  • Small signal approximation

Return

Correlation receiver

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Filtering for alpha stable noise

Filtering for Alpha Stable Noise

  • Myriad filtering

    • Sliding window algorithm outputs myriad of a sample window

    • Myriad of order k for samples x1,x2,…,xN [Gonzalez & Arce, 2001]

      • As k decreases, less impulsive noise passes through the myriad filter

      • As k→0, filter tends to mode filter (output value with highest frequency)

    • Empirical Choice of k [Gonzalez & Arce, 2001]

    • Developed for images corrupted by symmetric alpha stable impulsive noise

Return

Wireless Networking and Communications Group


Filtering for alpha stable noise cont

Filtering for Alpha Stable Noise (Cont..)

75

  • Myriad filter implementation

    • Given a window of samples, x1,…,xN, find β [xmin, xmax]

    • Optimal Myriad algorithm

      • Differentiate objective function polynomial p(β) with respect to β

      • Find roots and retain real roots

      • Evaluate p(β) at real roots and extreme points

      • Output β that gives smallest value of p(β)

    • Selection Myriad (reduced complexity)

      • Use x1, …, xN as the possible values of β

      • Pick value that minimizes objective function p(β)

Return

Wireless Networking and Communications Group


Filtering for alpha stable noise cont1

Filtering for Alpha Stable Noise (Cont..)

  • Hole punching (blanking) filters

    • Set sample to 0 when sample exceeds threshold [Ambike, 1994]

      • Large values are impulses and true values can be recovered

      • Replacing large values with zero will not bias (correlation) receiver for two-level constellation

      • If additive noise were purely Gaussian, then the larger the threshold, the lower the detrimental effect on bit error rate

    • Communication performance degrades as constellation size (i.e., number of bits per symbol) increases beyond two

Return

Wireless Networking and Communications Group


Map detection for alpha stable pdf approx

MAP Detection for Alpha Stable: PDF Approx.

  • SαS random variable Z with parameters a , d, gcan be written Z = X Y½[Kuruoglu, 1998]

    • X is zero-mean Gaussian with variance 2 g

    • Y is positive stable random variable with parameters depending on a

  • PDF of Z can be written as a mixture model of N Gaussians[Kuruoglu, 1998]

    • Mean d can be added back in

    • Obtain fY(.) by taking inverse FFT of characteristic function & normalizing

    • Number of mixtures (N) and values of sampling points (vi) are tunable parameters

Return

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Results alpha stable detection1

Results: Alpha Stable Detection

Return

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Complexity analysis for alpha stable detection

Complexity Analysis for Alpha Stable Detection

Return

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Extensions to mimo systems

Extensions to MIMO systems

Return

Wireless Networking and Communications Group


Our contributions1

Our Contributions

Return

2 x 2 MIMO receiver design in the presence of RFI[Gulati, Chopra, Heath, Evans, Tinsley & Lin, Globecom 2008]

Wireless Networking and Communications Group


Bivariate middleton class a model

Bivariate Middleton Class A Model

  • Joint spatial distribution

Return

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Results on measured rfi data1

Results on Measured RFI Data

Return

  • 50,000 baseband noise samples represent broadband interference

Marginal PDFs of measured data compared with estimated model densities

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System model

System Model

Return

  • 2 x 2 MIMO System

  • Maximum Likelihood (ML) receiver

  • Log-likelihood function

Sub-optimal ML Receivers

approximate

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Sub optimal ml receivers

Sub-Optimal ML Receivers

85

  • Two-piece linear approximation

  • Four-piece linear approximation

Return

Approximation of

chosen to minimize

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Results performance degradation

Results: Performance Degradation

  • Performance degradation in receivers designed assuming additive Gaussian noise in the presence of RFI

Return

  • Simulation Parameters

  • 4-QAM for Spatial Multiplexing (SM) transmission mode

  • 16-QAM for Alamouti transmission strategy

  • Noise Parameters:A = 0.1, 1= 0.01, 2= 0.1, k = 0.4

Severe degradation in communication performance in high-SNR regimes

Wireless Networking and Communications Group


Results rfi mitigation in 2 x 2 mimo

Results: RFI Mitigation in 2 x 2 MIMO

Return

Improvement in communication performance over conventional Gaussian ML receiver at symbol error rate of 10-2

Communication Performance (A = 0.1, 1= 0.01, 2= 0.1, k = 0.4)

Wireless Networking and Communications Group


Results rfi mitigation in 2 x 2 mimo1

Results: RFI Mitigation in 2 x 2 MIMO

Return

88

Complexity Analysis for decoding M-level QAM modulated signal

Complexity Analysis

Communication Performance (A = 0.1, 1= 0.01, 2= 0.1, k = 0.4)

Wireless Networking and Communications Group


Performance bounds single antenna

Performance Bounds (Single Antenna)

  • Channel capacity

Return

System Model

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Performance bounds single antenna1

Performance Bounds (Single Antenna)

  • Channel capacity in presence of RFI

Return

System Model

Capacity

ParametersA = 0.1, Γ = 10-3

Wireless Networking and Communications Group


Performance bounds single antenna2

Performance Bounds (Single Antenna)

  • Probability of error for uncoded transmissions

Return

[Haring & Vinck, 2002]

BPSK uncoded transmission

One sample per symbol

A = 0.1, Γ = 10-3

Wireless Networking and Communications Group


Performance bounds single antenna3

Performance Bounds (Single Antenna)

  • Chernoff factors for coded transmissions

Return

PEP: Pairwise error probability

N: Size of the codeword

Chernoff factor:

Equally likely transmission for symbols

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Performance bounds 2x2 mimo

Performance Bounds (2x2 MIMO)

Return

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Performance bounds 2x2 mimo1

Performance Bounds (2x2 MIMO)

  • Channel capacity

Return

System Model

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Performance bounds 2x2 mimo2

Performance Bounds (2x2 MIMO)

  • Channel capacity in presence of RFI for 2x2 MIMO

Return

System Model

Capacity

Parameters:A = 0.1, G1 = 0.01, G2 = 0.1, k = 0.4

Wireless Networking and Communications Group


Performance bounds 2x2 mimo3

Performance Bounds (2x2 MIMO)

  • Probability of symbol error for uncoded transmissions

Return

Pe: Probability of symbol error

S: Transmitted code vector

D(S): Decision regions for MAP detector

Equally likely transmission for symbols

Parameters:A = 0.1, G1 = 0.01, G2 = 0.1, k = 0.4

Wireless Networking and Communications Group


Performance bounds 2x2 mimo4

Performance Bounds (2x2 MIMO)

  • Chernoff factors for coded transmissions

Return

PEP: Pairwise error probabilityN: Size of the codewordChernoff factor:Equally likely transmission for symbols

Parameters:G1 = 0.01, G2 = 0.1, k = 0.4

Wireless Networking and Communications Group


Performance bounds 2x2 mimo5

Performance Bounds (2x2 MIMO)

  • Cutoff rates for coded transmissions

    • Similar measure as channel capacity

    • Relates transmission rate (R) to Pe for a length T codes

Return

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Performance bounds 2x2 mimo6

Performance Bounds (2x2 MIMO)

  • Cutoff rate

Return

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Extensions to multicarrier systems

Extensions to Multicarrier Systems

  • Impulse noise with impulse event followed by “flat” region

    • Coding may improve communication performance

    • In multicarrier modulation, impulsive event in time domain spreads over all subcarriers, reducing effect of impulse

  • Complex number (CN) codes [Lang, 1963]

    • Unitary transformations

    • Gaussian noise is unaffected (no change in 2-norm Distance)

    • Orthogonal frequency division multiplexing (OFDM) is a special case: Inverse Fourier Transform

    • As number of subcarriers increase, impulsive noise case approaches the Gaussian noise case [Haring 2003]

Return

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


Usage scenario 1

Usage Scenario #1

Return

RFI Toolbox

User System Simulator(e.g. WiMAX simulator)

Wireless Networking and Communications Group


Usage scenario 2

Usage Scenario #2

Return

Measured RFI data

Statistical Modeling DEMO

RFI Toolbox

SISO Communication Performance DEMO

MIMO Communication Performance DEMO

File Transfer DEMO

Wireless Networking and Communications Group


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