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Design of Interference-Aware Wireless Communication Systems

Brian L. Evans Lead Graduate Students Aditya Chopra, Kapil Gulati , and Marcel Nassar Collaborators from Intel Labs Current: Nageen Himayat , Kirk Skeba , and Srikathyayani Srikanteswara Past: Chaitanya Sreerama , Eddie X. Lin, Alberto A. Ochoa, and Keith R. Tinsley.

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Design of Interference-Aware Wireless Communication Systems

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  1. Wireless Networking and Communications Group Brian L. Evans Lead Graduate Students Aditya Chopra, KapilGulati, and Marcel Nassar Collaborators from Intel Labs Current: NageenHimayat, Kirk Skeba, and SrikathyayaniSrikanteswaraPast: ChaitanyaSreerama, Eddie X. Lin, Alberto A. Ochoa, and Keith R. Tinsley Design of Interference-Aware Wireless Communication Systems

  2. Outline • Introduction • Problem definition • Summary of last talk (in Apr. 2010) at Intel Labs • Recent results • RFI Modeling: Spatial and Temporal dependence • RFI Mitigation: Multi-carrier systems • Conclusions • Future work Radio FrequencyInterference (RFI) Wireless Networking and Communications Group

  3. Introduction (WiMAX Basestation) (Microwave) (Wi-Fi) (Wi-Fi) (WiMAX) antenna (WiMAX Mobile) • Wireless Communication Sources • Closely located sources • Coexisting protocols Non-Communication Sources Electromagnetic radiations baseband processor (Bluetooth) • Computational Platform • Clocks, busses, processors • Co-located transceivers Wireless Networking and Communications Group

  4. Problem Definition • Problem: Co-channel and adjacent channel interference, and platform noise degrade communication performance • Approach: Statistical modeling of RFI • Solution: Receiver design • Listen to the environment • Estimate parameters for RFI statistical models • Use parameters to mitigate RFI • Goal: Improve communication performance • 10-100x reduction in bit error rate • 10-100x improvement in network throughput Wireless Networking and Communications Group

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

  6. Statistical Models (isotropic, zero centered) 6 • 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

  7. Summary of Last Talk: 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

  8. Summary of Last Talk: RFI Modeling • Validated for Laptop radiated RFI • Slides available at:http://users.ece.utexas.edu/~bevans/projects/rfi/talks/April2010RFIMitigationTalk.html • 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

  9. Summary of Last Talk: 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: Extensions • 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

  11. RFI Modeling: Spatial Dependence • System Model • Common and exclusive interferers • Characterizes receiver separation and directional shielding • Joint RFI statistics helpful in choosing spatial transmit and receive techniques 2 1 3 1 1 2 3 1 3 1 2 1 3 2 1 2 3 2 Receiver Interferers impact all receivers Interferers impact receiver 1 Wireless Networking and Communications Group

  12. RFI Modeling: Spatial Dependence • An impulsive event at one antenna increases probability of impulse event at other antennae • Translated environmental parameters to spatial dependence |RFI at antenna 1| |RFI at antenna 1| |RFI at antenna 1| |RFI at antenna 2| |RFI at antenna 2| |RFI at antenna 2| SPATIALLY INDEPENDENT SPATIALLY ISOTROPIC Wireless Networking and Communications Group

  13. RFI Modeling: Temporal Dependence • System Model • Interference is dependent across time slots Wireless Networking and Communications Group

  14. 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

  15. RFI Mitigation: Multi-carrier systems • Single Carrier vs. Multi-Carrier: Intuition Single Carrier Multi Carrier (OFDM) Symbols Symbols High Amplitude Impulse High Amplitude Impulse Impulsive Noise Impulsive Noise • Impulse energy spread across symbols • Noise dependent across subcarriers • Optimal decoding: exponential complexity! • Impulse energy concentrated in one symbol • Symbol Lost Wireless Networking and Communications Group

  16. 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

  17. Summary Temporal Modeling Single Antenna (past work) Multi-Antenna Receivers Physical (PHY) Layer Medium Access Control (MAC) Layer RFI Mitigation: RFI Avoidance and Mitigation: • Detection and Pre-filtering methods • Single- and Two-antenna receivers • Single- and Multi-carrier systems • Microwave Oven Interference • Performance of Ad hoc Networks Impact: 10-100x improvement in communication performance Wireless Networking and Communications Group

  18. Current and Future Work Physical (PHY) Layer Medium Access Control (MAC) Layer RFI Avoidance and Mitigation: RFI Avoidance and Mitigation: • Communication Performance Analysis • MIMO transmit and receive strategies • Improving Communication Performance • Detection and Pre-filtering methods • Error correction coding • Interference Avoidance • Spectrum Sensing • Impact: • Improved communication performance • Network Performance Analysis • Different MAC strategies • Improving Network Performance • Optimizing MAC parameters • MAC algorithms to reduce interference • Interference Avoidance • Resource Allocation (time, frequency) • Impact: • Improved network-wide performance Cognitive Radios Wireless Networking and Communications Group

  19. UT Austin RFI Modeling & Mitigation Toolbox • Freely distributable toolbox in MATLAB • Simulation environment for RFI modeling and mitigation • RFI generation • Measured RFI fitting • Parameter estimation algorithms • Filtering and detection methods • Demos for RFI modeling and mitigation • Latest Toolbox Release Version 1.5, Aug. 16, 2010 Snapshot of a demo http://users.ece.utexas.edu/~bevans/projects/rfi/software/index.html Wireless Networking and Communications Group

  20. Related 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

  21. Related 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

  22. Thanks ! Wireless Networking and Communications Group

  23. 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

  24. 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

  25. 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

  26. 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

  27. Backup Slides • Introduction • Interference avoidance , alignment, and cancellation methods • Femtocell networks • Statistical Modeling of RFI • Computational platform noise • Impact of RFI • Assumptions for RFI Modeling • Transients in digital FIR filters • Poisson field of interferers • Poisson-Poisson cluster field of interferers • Measured RFI Fitting Backup Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group

  28. Backup Slides (cont…) • Gaussian Mixture vs. Alpha Stable • Middleton Class A, B, and C models • Middleton Class A model • Expectation maximization overview • Results: EM for Middleton Class A • Symmetric Alpha Stable • Extreme order statistics based estimator for Alpha Stable • Video over impulsive channels • Demonstration #1 • Demonstration #2 Backup Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group

  29. Backup Slides (cont…) • RFI mitigation in SISO systems • Our contributions • Results: Class A Detection • Results: Alpha Stable Detection • RFI mitigation in MIMO systems • Our contributions • Performance bounds for SISO systems • Performance bounds for MIMO systems • Extensions for multicarrier systems • Turbo codes in impulsive channels Backup Backup Backup Backup Backup Backup Backup Backup Wireless Networking and Communications Group

  30. Backup Slides (cont…) • RFI Toolbox Backup Wireless Networking and Communications Group

  31. Interference Mitigation Techniques • Interference avoidance • CSMA / CA • Interference alignment • Example: [Cadambe & Jafar, 2007] Return Wireless Networking and Communications Group

  32. Interference Mitigation Techniques (cont…) • Interference cancellation Ref: J. G. Andrews, ”Interference Cancellation for Cellular Systems: A Contemporary Overview”, IEEE Wireless Communications Magazine, Vol. 12, No. 2, pp. 19-29, April 2005 Return Wireless Networking and Communications Group

  33. Femtocell Networks Reference: V. Chandrasekhar, J. G. Andrews and A. Gatherer, "Femtocell Networks: a Survey", IEEE Communications Magazine, Vol. 46, No. 9, pp. 59-67, September 2008 Return Wireless Networking and Communications Group

  34. Common Spectral Occupancy Return Wireless Networking and Communications Group

  35. 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

  36. Impact of LCD clock on 802.11g • Pixel clock 65 MHz • LCD Interferers and 802.11g center frequencies Return Wireless Networking and Communications Group

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

  38. Transients in Digital FIR Filters • 25-Tap FIR Filter • Low pass • Stopband freq. 0.22 (normalized) Input Output Return Freq = 0.16 Interference duration = 100 x 1/0.22 Interference duration = 10 * 1/0.22 Transients Transients Significant w.r.t. Steady State Transients Ignorable w.r.t. Steady State Wireless Networking and Communications Group

  39. Poisson Field of Interferers • Interferers distributed over parametric annular space • Log-characteristic function Return Wireless Networking and Communications Group

  40. Poisson Field of Interferers Return Wireless Networking and Communications Group

  41. Poisson Field of Interferers Return Middleton Class A (form of Gaussian Mixture) Symmetric Alpha Stable • Dense Wi-Fi networks • Networks with contention based medium access • Cellular networks • Hotspots (e.g. café) • Sensor networks • Ad hoc networks Wireless Networking and Communications Group

  42. Poisson Field of Interferers Return • Simulation Results (tail probability) Case III: Infinite-area with guard zone Case I: Entire Plane Gaussian and Middleton Class A models are not applicable since mean intensity is infinite Wireless Networking and Communications Group

  43. Poisson Field of Interferers • Simulation Results (tail probability) Return Case II: Finite area annular region Wireless Networking and Communications Group

  44. Poisson-Poisson Cluster Field of Interferers • Cluster centers distributed as spatial Poisson process over • Interferers distributed as spatial Poisson process Return Wireless Networking and Communications Group

  45. Poisson-Poisson Cluster Field of Interferers • Log-Characteristic function Return Wireless Networking and Communications Group

  46. Poisson-Poisson Cluster Field of Interferers Return Gaussian Mixture Model Symmetric Alpha Stable • In-cell and out-of-cell femtocell users in femtocell networks • Out-of-cell femtocell users in femtocell networks • Cluster of hotspots (e.g. marketplace) Wireless Networking and Communications Group

  47. Poisson-Poisson Cluster Field of Interferers Return • Simulation Results (tail probability) Case III: Infinite-area with guard zone Case I: Entire Plane Gaussian and Gaussian mixture models are not applicable since mean intensity is infinite Wireless Networking and Communications Group

  48. Poisson-Poisson Cluster Field of Interferers • Simulation Results (tail probability) Return Case II: Finite area annular region Wireless Networking and Communications Group

  49. Fitting Measured Laptop RFI Data 49 • Statistical-physical models fit data better than Gaussian Return • 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 • Platform RFI sources • May not be Poisson distributed • May not have identical emissions Wireless Networking and Communications Group

  50. Results on Measured RFI Data 50 • For measurement set #23 Return • Tail probability governs communication performance • Bit error rate • Outage probability Wireless Networking and Communications Group

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