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Improving Wireless Data Transmission Speed and Reliability to Mobile Computing Platforms

Improving Wireless Data Transmission Speed and Reliability to Mobile Computing Platforms. Prof. Brian L. Evans Lead Graduate Students Aditya Chopra, Kapil Gulati and Marcel Nassar In collaboration with Keith R. Tinsley and Chaitanya Sreerama at Intel Labs.

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Improving Wireless Data Transmission Speed and Reliability to Mobile Computing Platforms

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  1. Wireless Networking and Communications Group Improving Wireless Data Transmission Speed and Reliability to Mobile Computing Platforms Prof. Brian L. Evans Lead Graduate Students Aditya Chopra, KapilGulati and Marcel Nassar In collaboration with Keith R. Tinsley and ChaitanyaSreerama at Intel Labs Texas Wireless Summit, Austin, Texas

  2. Problem Definition • Objectives • Develop offline methods to improve communication performance in presence of computer platform RFI • Develop adaptive online algorithms for these methods • Approach • Statistical modeling of RFI • Filtering/detection based on estimated model parameters Within computing platforms, wireless transceivers experience radio frequency interference (RFI) from clocks and busses We will use noise and interference interchangeably Wireless Networking and Communications Group

  3. Common Spectral Occupancy Wireless Networking and Communications Group

  4. Impact of RFI • Impact of LCD noise on throughput performance for a 802.11g embedded wireless receiver[Shi et al., 2006] Backup Wireless Networking and Communications Group

  5. Our Contributions Mitigation of computational platform noise in single carrier,single antenna systems [Nassar et al., ICASSP 2008] Wireless Networking and Communications Group

  6. Power Spectral Densities • Middleton Class A • Symmetric Alpha Stable Parameter values:a = 1.5, d = 0and g = 10 Parameter values:A = 0.15 and G = 0.1

  7. Fitting Measured RFI Data • Broadband RFI data • 80,000 samples collected using 20GSPS scope Backup Distance: Kullback-Leiblerdivergence Wireless Networking and Communications Group

  8. Fitting Measured RFI Data Best fit for 25 data sets under different conditions Return

  9. Filtering and Detection Methods Middleton Class A noise Symmetric Alpha Stable noise Filtering • Wiener Filtering (Linear) Detection • Correlation Receiver (Linear) • MAP (Maximum a posteriori probability) detector[Spaulding & Middleton, 1977] • Small Signal Approximation to MAP detector[Spaulding & Middleton, 1977] Filtering • Myriad Filtering[Gonzalez & Arce, 2001] • Hole Punching Detection • Correlation Receiver (Linear) • MAP approximation Backup Backup Backup Backup Backup Wireless Networking and Communications Group

  10. Results: Class A Detection Wireless Networking and Communications Group

  11. Results: Alpha Stable Detection Backup Backup Use dispersion parameter g in place of noise variance to generalize SNR Wireless Networking and Communications Group

  12. Results: Class A for 2 2 MIMO Improvement in communication performance over conventional Gaussian ML receiver at symbol error rate of 10-2 Complexity Analysis Communication Performance (A = 0.1, 1= 0.01, 2= 0.1, k = 0.4) Wireless Networking and Communications Group

  13. Conclusions • Radio frequency interference from computing platform • Affects wireless data communication transceivers • Fit Middleton Class A and symmetric alpha stable models • RFI mitigation can reduce bit error rate by a factor of • 100 for Middleton Class A model, single carrier system • 10 for Middleton Class A model, 2 x 2 MIMO system • 10 for Symmetric Alpha Stable model, single carrier system • Other applications of impulsive noise models • Co-channel interference • Adjacent channel interference Wireless Networking and Communications Group

  14. Contributions • Publications 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, Las Vegas, NV USA. 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, New Orleans, LA USA, accepted for publication. 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, Taipei, Taiwan, submitted. • Software Releases RFI Mitigation Toolbox Version 1.1 Beta (Released November 21st, 2007)Version 1.0 (Released September 22nd, 2007) • Project Websitehttp://users.ece.utexas.edu/~bevans/projects/rfi/index.html Wireless Networking and Communications Group

  15. Thank You, Questions ? Wireless Networking and Communications Group

  16. References RFI Modeling [1] 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.F. McDonald and R.S. Blum. “A physically-based impulsive noise model for array observations”, Proc. IEEE Asilomar Conference on Signals, Systems& Computers, vol 1, 2-5 Nov. 1997. [3] 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. [4] 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. Parameter Estimation [5] 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 [6] 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 RFI Measurements and Impact [7] 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

  17. References (cont…) Filtering and Detection [8] 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 [9] 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 [10] 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 [11] 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. [12] J. G. Gonzalez and G. R. Arce, “Optimality of the myriad filter in practical impulsive-noise environments,” IEEE Trans. on Signal Proc, vol. 49, no. 2, pp. 438–441, Feb 2001. [13] E. Kuruoglu, “Signal Processing In Alpha Stable Environments: A Least Lp Approach,” Ph.D. dissertation, University of Cambridge, 1998. [14] 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 [15] 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. Wireless Networking and Communications Group

  18. Backup Slides • Most backup slides are linked to the main slides • Miscellaneous topics not covered in main slides • Performance bounds for single carrier single antenna system in presence of RFI Backup Wireless Networking and Communications Group

  19. Outline • Problem definition • Single carrier single antenna systems • Radio frequency interference modeling • Estimation of interference model parameters • Filtering/detection • Multi-input multi-output (MIMO) single carrier systems • Conclusions Wireless Networking and Communications Group

  20. 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

  21. Statistical Modeling of RFI • Radio Frequency Interference (RFI) • Sum of independent radiation events • Predominantly non-Gaussian impulsive statistics • Key Statistical-Physical Models • Middleton Class A, B, C models • Independent of physical conditions (Canonical) • Sum of independent Gaussian and Poisson interference • Model non-linear phenomenon governing RFI • Symmetric Alpha Stable models • Approximation of Middleton Class B model Backup Backup Wireless Networking and Communications Group

  22. Assumptions for RFI Modeling • Key Assumptions [Middleton, 1977][Furutsu & Ishida, 1961] • Infinitely many potential interfering sources with same effective radiation power • Power law propagation loss • Poisson field of interferers • Pr(number of interferers = M |area R) ~ Poisson • Poisson distributed emission times • Temporally independent (at each sample time) • Limitations • [Alpha Stable]: Does not include thermal noise • Temporal dependence may exist Wireless Networking and Communications Group

  23. [Middleton, 1999] Middleton Class A, B and C Models • Class A Narrowband interference (“coherent” reception)Uniquely represented by 2 parameters • Class B Broadband interference (“incoherent” reception)Uniquely represented by six parameters • Class C Sum of Class A and Class B (approx. Class B) Return Backup Wireless Networking and Communications Group

  24. 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 PDF for A = 0.15,= 0.8 Wireless Networking and Communications Group

  25. Middleton Class B Model • Envelope Statistics • Envelope exceedence probability density (APD), which is 1 – cumulative distribution function (CDF) Return Wireless Networking and Communications Group

  26. Middleton Class B Model (cont…) • Middleton Class B Envelope Statistics Return Wireless Networking and Communications Group

  27. Parameters Description Typical Range Impulsive Index AB [10-2, 1] Ratio of Gaussian to non-Gaussian intensity ΓB  [10-6, 1] Scaling Factor NI  [10-1, 102] Spatial density parameter α  [0, 4] Effective impulsive index dependent on α A α [10-2, 1] Inflection point (empirically determined)‏ εB > 0 Middleton Class B Model (cont…) • Parameters for Middleton Class B Model Return Wireless Networking and Communications Group

  28. Accuracy of Middleton Noise Models Return Magnetic Field Strength, H (dB relative to microamp per meter rms)‏ ε0 (dB > εrms)‏ Percentage of Time Ordinate is Exceeded P(ε > ε0)‏ Soviet high power over-the-horizon radar interference [Middleton, 1999] Fluorescent lights in mine shop office interference [Middleton, 1999] Wireless Networking and Communications Group

  29. Symmetric Alpha Stable Model • 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 and  = 10 Backup Wireless Networking and Communications Group

  30. Symmetric Alpha Stable PDF • Closed form expression does not exist in general • Power series expansions can be derived in some cases • Standard symmetric alpha stable model for localization parameter  = 0 Return Wireless Networking and Communications Group

  31. Symmetric Alpha Stable Model • Heavy tailed distribution Return Density functions for symmetric alpha stable distributions for different values of characteristic exponent alpha: a) overall density and b) the tails of densities Wireless Networking and Communications Group

  32. Estimation of Noise Model Parameters • Middleton Class A model • Expectation Maximization (EM) [Zabin & Poor, 1991] • Find roots of second and fourth order polynomials at each iteration • Advantage: Small sample size is required (~1000 samples) • Disadvantage: Iterative algorithm, computationally intensive • Symmetric Alpha Stable Model • Based on Extreme Order Statistics [Tsihrintzis & Nikias, 1996] • Parameter estimators require computations similar to mean and standard deviation computations • Advantage: Fast / computationally efficient (non-iterative) • Disadvantage: Requires large set of data samples (~10000 samples) Backup Backup Wireless Networking and Communications Group

  33. Parameter Estimation: Middleton Class A • Expectation Maximization (EM) • E Step: Calculate log-likelihood function \w current parameter values • M Step: Find parameter set that maximizes log-likelihood function • EM Estimator for Class A parameters [Zabin & Poor, 1991] • Express envelope statistics as sum of weighted PDFs • Maximization step is iterative • Given A, maximize K (= AG). Root 2nd order polynomial. • Given K, maximize A. Root 4th order polynomial Return Backup Results Backup Wireless Networking and Communications Group

  34. Expectation Maximization Overview Return Wireless Networking and Communications Group

  35. Results: EM Estimator for Class A Return 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: Wireless Networking and Communications Group

  36. 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 Wireless Networking and Communications Group

  37. 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 Results Backup Wireless Networking and Communications Group

  38. Results: Symmetric Alpha Stable Parameter Estimator 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 Wireless Networking and Communications Group

  39. Results: Symmetric Alpha Stable Parameter Estimator (Cont…) Return Mean squared error in estimate of dispersion (“variance”)  Mean squared error in estimate of localization (“mean”)  Wireless Networking and Communications Group

  40. Extreme Order Statistics Return Wireless Networking and Communications Group

  41. Parameter Estimators for Alpha Stable Return 0 < p < α Wireless Networking and Communications Group

  42. Filtering and Detection • System Model • Assumptions: • Multiple samples of the received signal are available • N Path Diversity [Miller, 1972] • Oversampling by N[Middleton, 1977] • Multiple samples increase gains vs. Gaussian case • Impulses are isolated events over symbol period Impulsive Noise Pulse Shaping Pre-Filtering Matched Filter Detection Rule N samples per symbol Wireless Networking and Communications Group

  43. z(n)‏ ^ d(n)‏ x(n)‏ d(n)‏ w(n)‏ d(n)‏ ^ x(n)‏ d(n)‏ e(n)‏ w(n)‏ Wiener Filtering • Optimal in mean squared error sense in presence of Gaussian noise Return Model ^ d(n): desired signald(n): filtered signale(n): error w(n): Wiener filter x(n): corrupted signalz(n): noise Design Minimize Mean-Squared Error E { |e(n)|2 } Wireless Networking and Communications Group

  44. Wiener Filter Design • Infinite Impulse Response (IIR) • Finite Impulse Response (FIR) • Wiener-Hopf equations for order p-1 Return desired signal: d(n)power spectrum:(e j )correlation of d and x:rdx(n)autocorrelation of x: rx(n)Wiener FIR Filter:w(n) corrupted signal:x(n)noise: z(n)‏ Wireless Networking and Communications Group

  45. Raised Cosine Pulse Shape n Transmitted waveform corrupted by Class A interference n Received waveform filtered by Wiener filter n Results: Wiener Filtering • 100-tap FIR Filter Return Pulse shape10 samples per symbol10 symbols per pulse ChannelA = 0.35 = 0.5 × 10-3SNR = -10 dBMemoryless Wireless Networking and Communications Group

  46. 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 Wireless Networking and Communications Group

  47. Filtering for Alpha Stable Noise (Cont..) • 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) Wireless Networking and Communications Group

  48. MAP Detection for Class A • Hard decision • Bayesian formulation [Spaulding & Middleton, 1977] • Equally probable source Return Wireless Networking and Communications Group

  49. Correlation Receiver MAP Detection for Class A: Small Signal Approx. • 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

  50. Incoherent Detection • Baye’s formulation [Spaulding & Middleton, 1997, pt. II] • Small Signal Approximation Return Correlation receiver Wireless Networking and Communications Group

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