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Design of Interference-Aware Communication Systems

Prof. Brian L. Evans Cockrell School of Engineering. Design of Interference-Aware Communication Systems. Presentation to Freescale Semiconductor. Wireless Networking & Comm. Group. Applications. 2. Systems of systems. Networks of networks. Networks of systems. Systems. Networks.

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Design of Interference-Aware Communication Systems

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

  2. Wireless Networking & Comm. Group Applications 2 Systems of systems Networks of networks Networks of systems Systems Networks Middleware Compilers Protocols Operating systems Communication links Communication Processors Computation Circuit design Waveforms Data acq. Antennas Wires Collaboration with UT faculty outside of WNCG 17 faculty120 PhD students Devices

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

  4. Completed Projects – Prof. Evans 4 DSP Digital Signal Processor LTE Long-Term Evolution (cellular) MIMO Multi-Input Multi-Output PXI PCI Extensions for Instrumentation 17 PhD and 8 MS alumni

  5. On-Going Projects – Prof. Evans 5 DSP Digital Signal Processor PXI PCI Extensions for InstrumentationMIMO Multi-Input Multi-Output RFI Radio Frequency Interference 8 PhD and 3 MS students

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

  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) Project began January 2007 Wireless Networking and Communications Group

  8. 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

  9. 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

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

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

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

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

  14. Powerline Noise Modeling & Mitigation 14 • Problem: Impulsive noise is primaryimpairment 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

  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 GUI

  16. Thank you for your attention! 16

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

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

  19. Validating Statistical RFI Modeling • Validated for measurements of radiated RFI from laptop • 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

  20. 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

  21. 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

  22. Extensions to Statistical RFI Modeling • 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

  23. 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

  24. 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

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

  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… 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. • 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

  28. 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

  29. 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

  30. 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

  31. 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 Wireless Networking and Communications Group

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