1. Design of Interference-Aware Communication Systems Presentation to Freescale Semiconductor Wireless Networking and Communications Group 16 Dec 2010 Prof. Brian L. Evans Cockrell School of Engineering

2. Computation Communication Networks of networks Networks Data acq. Antennas Wires Communication links Processors Systems Compilers Circuit design Protocols Systems of systems Middleware Operating systems Devices Waveforms Networks of systems Applications 2 Wireless Networking & Comm. Group 17 faculty 120 PhD students Collaboration with UT faculty outside of WNCG

3. Wireless Networking & Comm. Group A. Gerstlauer Embedded Sys G. de Veciana Networking S. Vishwanath Sensor Networks S. Nettles Network Design S. Shakkottai Network Theory J. Andrews Communication L. Qiu Network Design C. Caramanis Optimization H. Vikalo Genomic DSP A. Bovik Image/Video B. Evans Embedded DSP T. Humphreys GPS/Navigation T. Rappaport RF IC Design R. Heath Comm/DSP B. Bard Security S. Sanghavi Network Science A.Tewfik Biomedical Communications NetworkingApplications 3 Computation

4. Completed Projects – Prof. Evans SystemContributionSW releasePrototypeCompanies ADSLequalizationMATLABDSP/CFreescale, TI MIMO testbedLabVIEWLabVIEW/PXIOil & Gas Wimax/LTEresource allocationLabVIEWDSP/CFreescale, TI Cameraimage acquisitionMATLABDSP/CIntel, Ricoh Displayimage halftoningMATLABCHP, Xerox video halftoningMATLABQualcomm CAD toolsfixed point conv.MATLABFPGAIntel, NI DSP Digital Signal Processor LTE Long-Term Evolution (cellular) MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 4 17 PhD and 8 MS alumni

5. On-Going Projects – Prof. Evans SystemContributionsSW releasePrototypeCompanies Powerline Comm. noise reduction; testbed LabVIEWLabVIEW and C/C++ in PXI Freescale, IBM, SRC, TI Wimax/WiFiRFI mitigationMATLABLabVIEW/PXIIntel RF Testnoise reductionLabVIEWLabVIEW/PXINI Underwater Comm. MIMO testbed; space-time meth. MATLABLake Travis testbed Navy CAD Toolsdist. computing.Linux/C++Navy sonarNavy, NI DSP Digital Signal Processor PXI PCI Extensions for Instrumentation MIMO Multi-Input Multi-Output RFI Radio Frequency Interference 5 8 PhD and 3 MS students

6. Radio Frequency Interference (RFI) Wireless Networking and Communications Group 6 Wireless Communication Sources Closely located sources Coexisting protocols Non-Communication Sources Electromagnetic radiation Computational Platform Clock circuitry Power amplifiers Co-located transceivers antenna baseband processor (Wi-Fi) (Wimax Basestation) (Wimax Mobile) (Bluetooth) (Microwave) (Wi-Fi)(Wimax)

7. 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) Wireless Networking and Communications Group 7 Project began January 2007

8. RFI Modeling Wireless Networking and Communications Group 8 Sensor networks Ad hoc networks Dense Wi-Fi networks Cluster of hotspots (e.g. marketplace) In-cell and out-of-cell femtocell users Out-of-cell femtocell users Cellular networks Hotspots (e.g. café) Symmetric Alpha Stable Ad hoc and cellular networks Single antenna Instantaneous statistics Femtocell networks Single antenna Instantaneous statistics Gaussian Mixture Model

9. RFI Mitigation  Communication performance Wireless Networking and Communications Group 9 Pulse Shaping Pre-filtering Matched Filter Detection Rule Interference + Thermal noise Single carrier, single antenna (SISO)Single carrier, two antenna (2x2 MIMO) ~ 20 dB ~ 8 dB 10 – 100x reduction in bit error rate

10. RFI Modeling & Mitigation Software  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 Wireless Networking and Communications Group 10 Version 1.5 Aug. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software Snapshot of a demo

11. Smart Grids: The Big Picture Smart car : charge of electricalvehicleswhile panels are producing Long distance communication : access to isolated houses Real-Time : Customers profiling enabling good predictions in demand = no need to use an additional power plant Anydisturbance due to a storm : action canbetakeninmediatelybased on real-time information Smart building : significant cost reduction on energy bill through remote monitoring Demand-side management : boilers are activatedduring the night whenelectricityisavailable Micro- production : better knowledge of energy produced to balance the network Security featuresFireisdetecte d : relaycanbeswitched off rapidly Source: ETSI 11

12. Powerline Communications (PLC)  “Last mile” low/medium voltage line PLC applications  SRC project began August 2010  Goal: Low-cost, power-efficient and robust communications  Automatic meter reading (right)  Smart energy management  Device-specific billing (plug-in hybrid) Source: Powerline Intelligent Metering Evolution (PRIME) Alliance Draft v1.3E 12 contacts Marie Burnham, Leo Dehner, Mike Dow, Kevin Kemp, Doug Garrity, John Pigott

13. Noise in Powerline Communications  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 Periodic Impulsive Noise Asynchronous to Main: 50-200kHz Caused by switching power supplies Approximated by narrowbands 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 Broadband Powerline Communications: Network Design Can be lumped together as Generalized Background Noise 13

14. Powerline Noise Modeling & Mitigation  Problem: Impulsive noise is primary impairment 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 14

15. 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

16. Thank you for your attention! 16

17. Designing Interference-Aware Receivers Wireless Networking and Communications Group 17 RTS CTS RTS / CTS: Request / Clear to send Guard zone Example: Dense WiFi Networks

18. Statistical Models (isotropic, zero centered)  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 18

19. Validating Statistical RFI Modeling  Validated for measurements of radiated RFI from laptop Wireless Networking and Communications Group 19 Smaller KL divergence Closer match in distribution Does not imply close match in tail probabilities Radiated platform RFI 25 RFI data sets from Intel 50,000 samples at 100 MSPS Laptop activity unknown to us

20. Turbo Codes in Presence of RFI Wireless Networking and Communications Group 20 Decoder 1 Parity 1 Systematic Data Decoder 2 Parity 2 - - - - A-priori Information Depends on channel statistics Independent of channel statistics Gaussian channel: Middleton Class A channel: Independent of channel statistics Extrinsic Information Leads to a 10dB improvement at BER of 10 -5 [Umehara03] Return

21. RFI Mitigation Using Error Correction Wireless Networking and Communications Group 21 Decoder 1 Parity 1 Systematic Data Decoder 2 Interleaver Parity 2 Interleaver - - - -  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

22.  Extended to include spatial and temporal dependence  Multivariate extensions of  Symmetric Alpha Stable  Gaussian mixture model Extensions to Statistical RFI Modeling Wireless Networking and Communications Group 22 Multi-antenna receivers Symbol errors Burst errors Coded transmissions Delays in network

23. RFI Modeling: Joint Interference Statistics  Throughput performance of ad hoc networks Wireless Networking and Communications Group 23 Ad hoc networks Multivariate Symmetric Alpha Stable Cellular networks Multivariate Gaussian Mixture Model Network throughput improved by optimizing distribution of ON Time of users (MAC parameter) ~1.6x

24. RFI Mitigation: Multi-carrier systems  Proposed Receiver  Iterative Expectation Maximization (EM) based on noise model  Communication Performance Wireless Networking and Communications Group 24 Simulation Parameters BPSK Modulation Interference Model 2-term Gaussian Mixture Model ~ 5 dB

25. Voltage Levels in a Power Grid Medium Voltage Low Voltage High Voltage Source: ERDF 25

26. 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… 26 Wireless Networking and Communications Group

27. 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. 27 Wireless Networking and Communications Group 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.

28. 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. 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. 3.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. 4.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. 5.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. 6.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. 7.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. 28 Wireless Networking and Communications Group

29. References Parameter Estimation 1.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. 2.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 1.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. 2.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. 3.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. 4.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. 29 Wireless Networking and Communications Group

30. References Communication Performance of Wireless Networks (cont…) 5.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. 6.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 http://arxiv.org/abs/0909.5119 7.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. 8.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 1.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 2.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 30 Wireless Networking and Communications Group

31. References Receiver Design to Mitigate RFI (cont…) 3.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. 4.G. R. Arce, Nonlinear Signal Processing: A Statistical Approach, John Wiley & Sons, 2005. 5.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. 6.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. 7.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. 8.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 1.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 on Electromagnetic Compatibility, vol.3, no., pp. 626-631, 14-18 Aug. 2006 31 Wireless Networking and Communications Group