1. Computer Arch. And Embedded Processors Embedded Real-Time Signal Processing Systems Prof. Brian L. Evans The University of Texas at Austin August 26, 2008 http://www.ece.utexas.edu http://signal.ece.utexas.edu http://www.cps.utexas.edu http://www.wncg.org

2. Computer Arch. And Embedded Processors Introduction Embedded systems oImplement dedicated, application-specific tasks oWork behind the scenes Real-time behavior oGuaranteed delivery [ Prof. Yale Patt ] oOften reactive to environment Signal processing oSignals are acquired measurements or models oProcessing transforms input signals to output signals

3. Computer Arch. And Embedded Processors Embedded Systems Computers masquerading as non-computers Casio Camera Watch Nokia 7110 Browser Phone Sony Playstation 2 Philips DVD player Philips TiVo Recorder Slide courtesy of Prof. Stephen A. Edwards of Columbia University

4. Computer Arch. And Embedded Processors Signal Processing Applications 2007 units shipped, consumer products o800M cell phones 100M DSL modems o250M PCs 60M cars/trucks o100M digital still cameras 30M printers Embedded processor cost? CEA Market Research (US)

5. Computer Arch. And Embedded Processors One Family: Digital Signal Processors As low as $2/processor in volume orders Small physical area and volume Predictable input/output rates oDeterministic interrupt service routine latency On-chip direct memory access controllers oStreams input/output separately from CPU oSends interrupt to CPU when block read/written Power consumption oFor battery-powered products: 10-100 mW oFor wall-powered products: 1-10 W

6. Computer Arch. And Embedded Processors Embedded Signal Processing Lab Signal processing for communication systems Image acquisition, analysis, and display Electronic design automation (EDA) Alumni: 16 PhD, 8 MS, 100 BS students Current: 8 PhD, 3 MS, 8 BS students Sys.Subsys.TheoryAlg.ReleaseDesignEmbed.Release ADSLequalizerYYMatlabYHW/SWDSP/C OFDMres. alloc.YYLabVIEWYSWDSP/C XceiverRFI mitig.YYMatlabY DisplayhalftoningYYMatlab/CY EDAfix. pt. con.YMatlabYHW Founded 1996

7. Computer Arch. And Embedded Processors Computer Platform RFI RFI from clocks, clock harmonics, busses oReduces communication performance for embedded wireless data transceivers Objective oImprove data reliability by factor of 10 Approaches oModel RFI using impulsive noise models oFiltering/detection based on RFI models

8. Computer Arch. And Embedded Processors 8 Common Spectral Occupancy Standard Carrier (GHz) Wireless Networking Interfering Clocks and Busses Bluetooth2.4 Personal Area Network Gigabit Ethernet, PCI Express Bus, LCD clock harmonics IEEE 802. 11 b/g/n 2.4 Wireless LAN (Wi-Fi) Gigabit Ethernet, PCI Express Bus, LCD clock harmonics IEEE 802.16e 2.5–2.69 3.3–3.8 5.725–5.85 Mobile Broadband (Wi-Max) PCI Express Bus, LCD clock harmonics IEEE 802.11a 5.2 Wireless LAN (Wi-Fi) PCI Express Bus, LCD clock harmonics

9. Computer Arch. And Embedded Processors Statistical Models Middleton Class ASymmetric Alpha Stable Power Spectral Density with A = 0.15 and  = 0.1 with  = 1.5,  = 0 and  = 10

10. Computer Arch. And Embedded Processors 10 Proposed Contributions Computer Platform Noise Modelling Evaluate fit of measured RFI data to noise models Narrowband Interference: Middleton Class A model Broadband Interference: Symmetric Alpha Stable Parameter EstimationEvaluate estimation accuracy vs complexity tradeoffs Filtering / DetectionEvaluate communication performance vs complexity tradeoffs Middleton Class A: Correlation receiver, Wiener filtering and Bayesian detector Symmetric Alpha Stable: Myriad filtering, hole punching, and Bayesian detector

11. Computer Arch. And Embedded Processors 11 Estimated Parameters Symmetric Alpha Stable Model Localization (δ)0.0043 Distance 0.0514 Characteristic exp. (α)1.2105 Dispersion (γ)0.2413 Middleton Class A Model Overlap Index (A)0.1036 Distance 0.0825 Gaussian Factor (Γ)0.7763 Gaussian Model Mean (µ)0 Distance 0.2217 Variance (σ 2 )1 Distance: Kullback-Leibler divergence Results for Measured RFI Data Set 80,000 samples collected using 20 GSPS scope

12. Computer Arch. And Embedded Processors 12 Pulse shape Raised cosine 10 samples per symbol 10 symbols per pulse Channel A = 0.35  = 0.5 × 10 -3 Memoryless MethodComp.Detection Perform. Correl.Low WienerMediumLow Bayesian Approx. MediumHigh BayesianHigh Detection in Middleton Class A Noise SNR is signal-to-noise ratio, i.e. transmitted signal power over channel noise power

13. Computer Arch. And Embedded Processors 13 MethodComp.Detection Perform. Hole Punching LowMedium Selection Myriad LowMedium Bayesian Approx. MediumHigh Optimal Myriad HighMedium Use dispersion parameter  in place of noise variance to generalize SNR Detection for Symmetric Alpha Stable

14. Computer Arch. And Embedded Processors Conclusion Using impulsive noise models, reduce bit error rates (i.e. increase data reliability) oBy factor of 10-100 for Middleton Class A model oBy factor of 10 for Symmetric Alpha Stable model Tractable parameter estimation algorithms oMiddleton Class A: iterative + polynomial rooting oSymmetric Alpha Stable: non-iterative UT Austin RFI Mitigation Toolbox http://www.ece.utexas.edu/~bevans/projects/rfi Future extensions

15. Computer Arch. And Embedded Processors References 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. 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. 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. 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. 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.

16. Computer Arch. And Embedded Processors References B. Widrow et al., “Principles and Applications”, Proc. of the IEEE, vol. 63, no.12, Sep. 1975. 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. 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. K. Gulati, A. Chopra, R. W. Heath, Jr., B. L. Evans and K. R. Tinsley, and X. E. Lin, "MIMO Receiver Design in the Presence of Radio Frequency Interference", Proc. IEEE Global Communications Conf., Dec. 2008, accepted for publication.