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Wireless Networking and Communications                                 Group    Design of Interference-Aware      Communic...
Completed Projects – Prof. Evans2      System         Contribution     SW release     Prototype       Companies    ADSL   ...
On-Going Projects – Prof. Evans3      System        Contributions    SW release      Prototype       Companies    Powerlin...
Radio Frequency Interference (RFI)4                                                                         (Wimax Basesta...
RFI Modeling & Mitigation5       Problem: RFI degrades communication performance       Approach: Statistical modeling of...
RFI Modeling6       Ad hoc and    cellular networks    •Single antenna    •Instantaneous    statistics                   •...
RFI Mitigation7                                            Interference + Thermal noise                                   ...
RFI Modeling & Mitigation Software8       Freely distributable toolbox in MATLAB       Simulation of RFI modeling/mitiga...
Voltage Levels in Power Grid                                                                   High-Voltage               ...
Powerline Communications (PLC)10       Concentrator controls medium        to subscriber meters           Plays role of ...
Noise in Powerline Communications11       Superposition of five noise sources [Zimmermann, 2000]            Different ty...
Powerline Noise Modeling & Mitigation12       Problem: Impulsive noise is primary        impairment in powerline communic...
Preliminary Noise Measurement                                             Power Spectral Density Estimate                 ...
Preliminary Noise Measurement                                             Power Spectral Density Estimate                 ...
Preliminary Noise Measurement                                              Power Spectral Density Estimate                ...
Preliminary Noise Measurement                                              Power Spectral Density Estimate                ...
Powerline Communications Testbed17       Integrate ideas from multiple standards (e.g. PRIME)         Quantify communica...
Thank you for your attention!18
Backup
Designing Interference-Aware Receivers20                             Guard zone                                           ...
Statistical Models (isotropic, zero centered)21       Symmetric Alpha Stable [Furutsu           & Ishida, 1961] [Sousa, 1...
Validating Statistical RFI Modeling22                               Validated for measurements of radiated RFI from lapto...
Turbo Codes in Presence of RFI23                                                                                          ...
RFI Mitigation Using Error Correction24                                                                      Return      ...
Extensions to Statistical RFI Modeling25       Extended to include spatial and temporal dependence                       ...
RFI Modeling: Joint Interference Statistics26                                                       Ad hoc networks       ...
RFI Mitigation: Multi-carrier systems27                 Proposed Receiver                                 Iterative Expe...
Smart Grids: The Big Picture28                                Long distance         Real-Time :           communication : ...
Wireless Networking & Comm. Group29                     Applications            Systems of systems                     Net...
Wireless Networking & Comm. Group30                  Communications                                   Networking          ...
Our Publications31    Journal Publications    • K. Gulati, B. L. Evans, J. G. Andrews, and K. R. Tinsley, “Statistics of C...
Our Publications32    Conference Publications (cont…)    • A. Chopra, K. Gulati, B. L. Evans, K. R. Tinsley, and C. Sreera...
References33    RFI Modeling    1. D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: N...
References34    Parameter Estimation    1. S. M. Zabin and H. V. Poor, “Efficient estimation of Class A noise parameters v...
References35    Communication Performance of Wireless Networks (cont…)    5. S. Weber, J. G. Andrews, and N. Jindal, “Indu...
References36    Receiver Design to Mitigate RFI (cont…)    3. S. Ambike, J. Ilow, and D. Hatzinakos, “Detection for binary...
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Evans interferenceawaremar2011

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Evans interferenceawaremar2011

  1. 1. Wireless Networking and Communications Group Design of Interference-Aware Communication Systems Prof. Brian L. Evans Cockrell School of Engineering24 Mar 2011 WNCG “Dallas or Bust” Roadtrip
  2. 2. Completed Projects – Prof. Evans2 System Contribution SW release Prototype Companies ADSL equalization MATLAB DSP/C Freescale, TI MIMO testbed LabVIEW LabVIEW/PXI Oil & Gas Wimax/LTE resource allocation LabVIEW DSP/C Freescale, TI Camera image acquisition MATLAB DSP/C Intel, Ricoh Display image halftoning MATLAB C HP, Xerox video halftoning MATLAB Qualcomm CAD tools fixed point conv. MATLAB FPGA Intel, NI 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
  3. 3. On-Going Projects – Prof. Evans3 System Contributions SW release Prototype Companies Powerline noise reduction; LabVIEW LabVIEW and Freescale, Comm. testbed C/C++ in PXI IBM, SRC, TI Wimax/WiFi RFI mitigation MATLAB LabVIEW/PXI Intel RF Test noise reduction LabVIEW LabVIEW/PXI NI Underwater MIMO testbed; MATLAB Lake Travis Navy Comm. space-time meth. testbed CAD Tools dist. computing. Linux/C++ Navy sonar Navy, NI DSP Digital Signal Processor PXI PCI Extensions for Instrumentation MIMO Multi-Input Multi-Output RFI Radio Frequency Interference 8 PhD and 4 MS students
  4. 4. Radio Frequency Interference (RFI)4 (Wimax Basestation) (Microwave) (Wi-Fi) (Wi-Fi) (Wimax) antenna (Wimax Mobile) Wireless Non-Communication Sources Communication Sources Electromagnetic radiation • Closely located sources • Coexisting protocols baseband processor (Bluetooth) Computational Platform • Clock circuitry • Power amplifiers • Co-located transceivers Wireless Networking and Communications Group
  5. 5. RFI Modeling & Mitigation5  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
  6. 6. RFI Modeling6 Ad hoc and cellular networks •Single antenna •Instantaneous statistics • Sensor networks • Cellular networks • Dense Wi-Fi networks • Ad hoc networks • Hotspots (e.g. café) Femtocell networks •Single antenna •Instantaneous statistics • In-cell and out-of-cell • Cluster of hotspots • Out-of-cell femtocell users (e.g. marketplace) femtocell users Symmetric Alpha Stable Gaussian Mixture Model Wireless Networking and Communications Group
  7. 7. RFI Mitigation7 Interference + Thermal noise Pulse Matched Detection Pre-filtering Shaping Filter Rule  Communication performance 0 10 Correlation Receiver Bayesian Detection Myriad Pre-filtering -1 10 Vector Symbol Error Rate -1 10 Symbol Error Rate 10 – 100x reduction in bit error rate ~ 8 dB -2 ~ 20 dB -2 10 10 Optimal ML Receiver (for Gaussian noise) Optimal ML Receiver (for Middleton Class A) Sub-Optimal ML Receiver (Four-Piece) -3 -3 10 10 Sub-Optimal ML Receiver (Two-Piece) -40 -35 -30 -25 -20 -15 -10 -5 -10 -5 0 5 10 15 20 Signal to Noise Ratio (SNR) [in dB] SNR [in dB] Single carrier, single antenna (SISO) Single carrier, two antenna (2x2 MIMO) Wireless Networking and Communications Group
  8. 8. RFI Modeling & Mitigation Software8  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 Snapshot of a demo  System simulation (e.g. Wimax or powerline communications)  Fit RFI measurements to statistical models Version 1.6 beta Dec. 2010: http://users.ece.utexas.edu/~bevans/projects/rfi/software Wireless Networking and Communications Group
  9. 9. Voltage Levels in Power Grid High-Voltage Source: Électricité Réseau Dist. France (ERDF) Medium-Voltage Low-Voltage Concentrator “Last mile” powerline communications on low/medium voltage line 9
  10. 10. Powerline Communications (PLC)10  Concentrator controls medium to subscriber meters  Plays role of basestation  Applications  Automatic meter reading (right)  Smart energy management  Device-specific billing (plug-in hybrid)  Goal: Improve reliability & rate  Mitigate impulsive noise Source: Powerline Intelligent Metering Evolution (PRIME)  Multichannel transmission Alliance Draft v1.3E
  11. 11. Noise in Powerline Communications11  Superposition of five noise sources [Zimmermann, 2000]  Different types of power spectral densities (PSDs) Colored Background lumped together asAsynchronous to Main: Main: Narrowband Impulsive Noise Noise Synchronous to Can be Noise: Noise: PeriodicPeriodic Impulsive Asynchronous Impulsive Noise: • • • Generalized• Background Noise •impulses by switching transients PSD decreases with frequency modulated amplitudes • Sinusoidal with 50-100Hz, Short duration • 50-200kHz • Superposition of numerous noisesubbands Affects several sources • Caused • PSD decreases with frequency Caused by switching power supplies Arbitrary interarrivals with micro- • with lower intensity • Caused by medium by narrowbands • Approximated and shortwave convertors millisecond durations Caused by power • Time varying (order of minutes and hours) broadcast channels • 50dB above background noise Broadband Powerline Communications: Network Design
  12. 12. Powerline Noise Modeling & Mitigation12  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
  13. 13. Preliminary Noise Measurement Power Spectral Density Estimate -75 -80 Power/frequency (dB/Hz) -85 -90 -95 -100 -105 -110 -115 -120 -125 0 10 20 30 40 50 60 70 80 90 Frequency (kHz)13
  14. 14. Preliminary Noise Measurement Power Spectral Density Estimate -75 -80 Power/frequency (dB/Hz) -85 -90 -95 -100 -105 -110 -115 -120 Colored Background -125 Noise 0 10 20 30 40 50 60 70 80 90 Frequency (kHz)14
  15. 15. Preliminary Noise Measurement Power Spectral Density Estimate -75 -80 Narrowband Noise Power/frequency (dB/Hz) -85 -90 -95 -100 -105 -110 -115 -120 Colored Background -125 Noise 0 10 20 30 40 50 60 70 80 90 Frequency (kHz)15
  16. 16. Preliminary Noise Measurement Power Spectral Density Estimate Periodic and -75 Asynchronous Noise -80 Narrowband Noise Power/frequency (dB/Hz) -85 -90 -95 -100 -105 -110 -115 -120 Colored Background -125 Noise 0 10 20 30 40 50 60 70 80 90 Frequency (kHz)16
  17. 17. Powerline Communications Testbed17  Integrate ideas from multiple standards (e.g. PRIME)  Quantify communication performance vs complexity tradeoffs  Extend our existing real-time DSL testbed (deployed in field) GUI GUI  Adaptive signal processing methods  Channel modeling, impulsive noise filters & equalizers  Medium access control layer scheduling  Effective and adaptive resource allocation
  18. 18. Thank you for your attention!18
  19. 19. Backup
  20. 20. Designing Interference-Aware Receivers20 Guard zone Statistical Modeling of RFI • Derive analytically • Estimate parameters at receiver Physical (PHY) Layer Medium Access Control (MAC) Layer • Receiver pre-filtering • Interference sense and avoid • Receiver detection • Optimize MAC parameters • Forward error correction (e.g. guard zone size, transmit power) RTS / CTS: Request / Clear to send Example: Dense WiFi Networks Wireless Networking and Communications Group
  21. 21. Statistical Models (isotropic, zero centered)21  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
  22. 22. Validating Statistical RFI Modeling22  Validated for measurements of radiated RFI from laptop 0.4 Symmetric Alpha Stable 0.35 Middleton Class A Radiated platform RFI Gaussian Mixture Model • 25 RFI data sets from Intel Kullback-Leibler divergence Gaussian 0.3 • 50,000 samples at 100 MSPS 0.25 • Laptop activity unknown to us 0.2 0.15 Smaller KL divergence 0.1 • Closer match in distribution • Does not imply close match in 0.05 tail probabilities 0 0 5 10 15 20 25 Measurement Set Wireless Networking and Communications Group
  23. 23. Turbo Codes in Presence of RFI23 Return - Parity 1Systematic Data Decoder 1 -  Gaussian channel:  - Middleton Class A channel: Parity 2 Decoder 2 -  1 Extrinsic A-priori Information Information Leads to a 10dB improvement at Independent of Depends on Independent BER of 10-5 [Umehara03] channel channel of channel statistics statistics statistics Wireless Networking and Communications Group
  24. 24. RFI Mitigation Using Error Correction24 Return  Turbo decoder - Parity 1 Decoder 1 Interleaver - Systematic Data Interleaver - Parity 2 Decoder 2 Interleaver -  Decoding depends on the RFI statistics  10 dB improvement at BER 10-5 can be achieved using accurate RFI statistics [Umehara, 2003] Wireless Networking and Communications Group
  25. 25. Extensions to Statistical RFI Modeling25  Extended to include spatial and temporal dependence Statistical Modeling of RFI Single Antenna Spatial Dependence Temporal Dependence Instantaneous statistics • Symbol errors • Multi-antenna receivers • Burst errors • Coded transmissions • Delays in network  Multivariate extensions of  Symmetric Alpha Stable  Gaussian mixture model Wireless Networking and Communications Group
  26. 26. RFI Modeling: Joint Interference Statistics26 Ad hoc networks Cellular networks Multivariate Symmetric Alpha Stable Multivariate Gaussian Mixture Model  Throughput performance of ad hoc networks 10 Network Throughput (normalized) With RFI Mitigation 9 Without RFI Mitigation 8 ~1.6x Network throughput improved [ bps/Hz/area ] 7 by optimizing distribution of 6 ON Time of users (MAC parameter) 5 4 3 2 2 4 6 8 10 12 14 16 Expected ON Time of a User (time slots) Wireless Networking and Communications Group
  27. 27. RFI Mitigation: Multi-carrier systems27  Proposed Receiver  Iterative Expectation Maximization (EM) based on noise model  Communication Performance 0 10 OFDM Receiver Single Carrier Simulation Parameters -1 Proposed EM-based Receiver 10 • BPSK Modulation -2 • Interference Model Bit Error Rate 10 2-term Gaussian Mixture Model -3 ~ 5 dB 10 -4 10 -10 -5 0 5 10 15 20 Signal to Noise Ratio (SNR) [in dB] Wireless Networking and Communications Group
  28. 28. Smart Grids: The Big Picture28 Long distance Real-Time : communication : Customers profiling access to isolated enabling good houses predictions in demand Micro- production = no need to use an : better knowledge additional power plant of energy produced to balance the Demand-side network management : boilers are activatedduring the night whenelectricityisavaila ble Smart building : Anydisturbance due to a significant cost reduction storm : action on energy bill through Security canbetakeninmediatelybas remote monitoring featuresFireisdetect ed on real-time ed : Smart car : charge of information relaycanbeswitched electricalvehicleswhile panels are producing off rapidly Source: ETSI
  29. 29. Wireless Networking & Comm. Group29 Applications Systems of systems Networks of networks Networks of systems Systems Networks Compilers Middleware Operating systems Protocols Processors Communication links Circuit design Waveforms Data Antennas Collaboration acq. Wires 17 faculty with UT faculty outside of WNCG 140 grad students Devices
  30. 30. Wireless Networking & Comm. Group30 Communications Networking Applications B. Evans J. Andrews S. Nettles B. Bard C. Caramanis A. Bovik Embedded DSP Communication Computation Network Design Security Optimization Image/Video A. Gerstlauer R. Heath S. Shakkottai G. de Veciana S. Sanghavi A. Tewfik Embedded Sys Comm/DSP Network Theory Networking Network Science Biomedical T. Rappaport T. Humphreys S. Vishwanath L. Qiu H. Vikalo RF IC Design GPS/Navigation Sensor Networks Network Design Genomic DSP
  31. 31. Our Publications31 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, vol. 58, no. 12, Dec. 2010, pp. 6207-6222. • 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”, Proc. IEEE Int. Conf. on Comm., Jun. 5-9, 2011. • 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 Comm. Conf., Nov. 30-Dec. 4, 2009. Cont… Wireless Networking and Communications Group
  32. 32. Our Publications32 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 1.6 beta, Dec. 16, 2010. Wireless Networking and Communications Group
  33. 33. References33 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. Wireless Networking and Communications Group
  34. 34. References34 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. Wireless Networking and Communications Group
  35. 35. References35 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 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 Wireless Networking and Communications Group
  36. 36. References36 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 Wireless Networking and Communications Group

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