Mitigating Computer Platform Radio Frequency Interference in Embedded Wireless Transceivers

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Mitigating Computer PlatformRadio Frequency
Interference inEmbedded Wireless Transceivers
Preliminary Results

• Prof. Brian L. Evans
• The University of Texas at Austin
• bevans_at_ece.utexas.edu

October 17, 2007
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Computer Platform RFI

• Clocks, clock harmonics, and busses generate RFI
• Impulsive in nature (non-Gaussian)
• Reduces communication performancefor embedded
  wireless data transceivers
• Goals
• Improve communication performancein presence of
  computer platform RFI
• Develop offline and online algorithms
• Approaches
• Statistical modeling of RFI
• Filtering/detection using estimation
  ofstatistical model parameters

Well be using interference and noise
interchangeably in this talk.
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Statistical Models

• Middleton Class A

• Symmetric Alpha Stable

Power Spectral Density
Power Spectral Density
with a 1.5, d 0 and g 10
with A 0.15 and G 0.1
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Middleton Class A Noise
Communication Performance
Class A ParametersA 0.35G 0.5 10-3
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Symmetric Alpha Stable Noise
Communication Performance
SaS Parametersa 1.5d 0? 1
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Symmetric Alpha Stable Noise

• 80,000 data samples collected using 20 GSPS scope

Parameter Estimation Parameter Estimation
Localization (d) -0.0393
Dispersion (?) 0.5833
Characteristic Exponent (a) 1.5525
fX(x) - PDF
Normalized MSE 0.0055
x noise amplitude
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Conclusion

• Modeling computer platform RFI using impulse
  noise models promising
• Middleton Class A 25 dB gain for BER 10-2
• Symmetric Alpha Stable 5 dB gain for BER 10-1
• Tractable parameter estimation algorithms
• Middleton Class A iterative polynomial
  rooting
• Symmetric Alpha Stable non-iterative
• UT Austin RFI Mitigation Toolbox
• http//www.ece.utexas.edu/bevans/projects/rfi
• Research began January 2007 more to come!

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References

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   signal processing for telecommunications New
   methods and results for Class A and Class B noise
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   4, pp. 1129-1149, May 1999.
2. 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.
3. 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.
4. 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.
5. 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.
6. B. Widrow et al., Principles and Applications,
   Proc. of the IEEE, vol. 63, no.12, Sep. 1975.
7. 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.