1. August 4, 2011 Non-Parametric Methods for Mitigating Interference in OFDM Receivers American University of Beirut 1 Prof. Brian L. Evans Department of Electrical and Computer Engineering The University of Texas at Austin In collaboration with PhD students Ms. Jing Lin and Mr. Marcel Nassar Wireless Networking and Communications Group

2. Outline  Motivation  System model  Prior work  Sparse Bayesian learning  Proposed algorithms and results  Conclusion Wireless Networking and Communications Group 2

3. Mobile Internet Data: The Big Picture  Observations  2x increase/year in data traffic: 1000x increase next 10 years  Demand is increasing exponentially but revenues are not  Revenue and traffic suddenly decoupled vs. voice service  Business models remain fuzzy especially for video  Consequences to industry  Restrict data usage (unpopular) OR  Decrease cost per bit exponentially (how?) OR  Lose money and/or watch network collapse (current status) Wireless Networking and Communications Group 3 Source: J. G. Andrews, "Wireless 1000x?", University of Notre Dame Seminar, May 5, 2011.

4. Heterogeneity: Make Cells Smaller/Smarter  Demand handled by different networks  Macrocells guarantee basic coverage and require fast dedicated backhaul  Picocells target traffic “hotspots”  Femtocells must interoperate w/ cellular networks with minimal coordination Wireless Networking and Communications Group 4 BasestationRangePowerBuild CostsOper. CostsDeployed By Macrocell1-10 km40W$100kHighService Provider Picocell100m1-2W$15-40kLowService Provider Femtocell 10m200mW$100Very LowUser at Home Source: J. G. Andrews, "Wireless 1000x?", University of Notre Dame Seminar, May 5, 2011. Tower- mounted macrocell pico femto Basestations

5. Wireless Networking and Communications Group Wireless Interference 5 Guard zone Example: Dense Wi-Fi Networks Duration Channel 11 Channel 9 (a) (b) (c) (d) Interference a) Co-channel b) Adjacent channel c) Out-of-platform d) In-platform

6. Wireless Networking and Communications Group In-Platform Interference 6  May severely degrade communication performance  Impact of LCD noise on throughput for IEEE 802.11g embedded wireless receiver [Shi, Bettner, Chinn, Slattery & Dong, 2006]

7. Low-Voltage Power Line Noise Wireless Networking and Communications Group 7 Measurement on 20 Mar 2011 on low-voltage US apartment power outlet at 5:00 am Powerline comm. standards use either 40-90 kHz or 10-500 kHz Impulsive noise is 45-50 dB above the noise floor [Nassar, Gulati, Mortazavi & Evans, 2011]

8. Heterogeneity: Receiver’s Perspective Wireless Networking and Communications Group 8 Wireless Communication Sources Uncoordinated Transmissions Non-Communication Sources Electromagnetic radiations Computational Platform Clocks, busses, processors Other embedded transceivers Antennas Baseband Processor Network heterogeneity leads to the increase of uncoordinated interference at the receiver

9. Statistical Modeling of Interference Wireless Networking and Communications Group 9 Cellular networks Hotspots (e.g. café) Sensor networks Ad hoc networks Dense Wi-Fi networks Networks with contention based medium access Symmetric Alpha Stable Middleton Class A (form of Gaussian Mixture) [Gulati, Evans, Andrews & Tinsley, 2010]

10. Statistical Modeling of Interference Wireless Networking and Communications Group 10 Cluster of hotspots (e.g. marketplace) In-cell and out-of-cell femtocell users in femtocell networks Out-of-cell femtocell users in femtocell networks Symmetric Alpha Stable Gaussian Mixture Model [Gulati, Evans, Andrews & Tinsley, 2010]

11. Statistical Modeling of Interference  Low-voltage power lines  Multiple noise sources  1% of impulses exceed 1 ms in duration  Amplitude statistics  By derivation, model is Gaussian mixture  Gaussian mixture best fit for tail probabilities Wireless Networking and Communications Group 11 Data captured on power outlet in apartment in Austin, Texas USA, 20 Mar 2011 Fit blocks of 14 ms of data sampled at 1 MSample/s (blocks of 14000 samples) [Nassar, Gulati, Mortazavi & Evans, 2011]

12. Statistical Models of Impulsive Noise  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 12

13. Orthogonal Frequency Division Multiplexing  Divides transmission band into narrow subchannels  Null tones at band edges for reducing spectral leakage  Null tones in low signal-to-noise ratio (SNR) subchannels  Pilot tones for synchronization and channel estimation  Power loading per subcarrier to increase data rates  Subchannel processing combats multipath effects  Better resilience to impulsive noise vs. single carrier  Used in modern data communications standards  Wireless: IEEE802.11a/g/n, cellular LTE  Powerline: PRIME, G3, IEEE1901.2 Wireless Networking and Communications Group 13

14. System Model Wireless Networking and Communications Group 14 [Lin, Nassar & Evans, 2011]

15. OFDM Receivers in Impulsive Noise Wireless Networking and Communications Group 15 [Lin, Nassar & Evans, 2011]

16. Parametric Methods  Use parameterized functional forms of noise statistics  Need to estimate and track noise parameters  Suffer degradation in performance  Due to model mismatch or parameter mismatch  When noise statistics are changing rapidly  Not dependent on null tones  Higher throughput when noise statistics are slowly varying  Complexity in parameter estimation and tracking  OFDM decoders: high complexity for optimality and low-complexity approximations may work well enough Wireless Networking and Communications Group 16 [Lin, Nassar & Evans, 2011]

17. Prior Work Wireless Networking and Communications Group 17 Parametric Methods Semi-nonParametric Methods (Threshold Selection) nonParametric Method [Lin, Nassar & Evans, 2011]

18. Sparse Bayesian Learning Wireless Networking and Communications Group 18 [Lin, Nassar & Evans, 2011]

19. M: # of known tones N: total # of tones Estimation Using Null Tones Wireless Networking and Communications Group 19 [Lin, Nassar & Evans, 2011]

20. Estimation Using All Tones Wireless Networking and Communications Group 20 M: # of known tones N: total # of tones [Lin, Nassar & Evans, 2011]

21. Communication Performance Simulations  In different impulsive noise scenarios Wireless Networking and Communications Group 21 Gaussian mixture modelMiddleton Class A model ~6dB ~8dB ~6dB ~10dB ~4dB Parametric (no null tones =>higher throughput) [Lin, Nassar & Evans, 2011]

22. Communication Performance Simulations  In different impulsive noise scenarios (continued) Wireless Networking and Communications Group 22 Symmetric alpha stable model ~7dB ~4dB [Lin, Nassar & Evans, 2011]

23. Communication Performance Simulations Wireless Networking and Communications Group 23  Performance of first algorithm as number of known tones decreases  SNR is 0 dB  256 tones  Middleton Class A noise  In both algorithms, the EM algorithm converges after a few iterations [Lin, Nassar & Evans, 2011]

24. Comparison  Based on formula for impulsive noise distribution  Needs parameter estimation  Good for slowly varying noise statistics  Suffer from model mismatch in fast varying environments  High complexity for optimal decoders  No assumption of noise statistics  Uses null tones in each OFDM symbol  Robust in fast varying noise environments  Potential reduction in throughput due to null tones (if not already in standard) Parametric Methods Non-Parametric Methods 24 Wireless Networking and Communications Group

25. Conclusions and Future Work  Proposed impulsive noise reduction algorithms  Assume real-valued OFDM symbols (G3, PRIME, ADSL)  Use null + pilot tones to give 4-6 dB SNR gain in simulation  Use all tones to give 8-10 dB SNR gain in simulation  Future work  Extend to complex-valued OFDM symbols (802.11a/b/n, LTE)  Track impulsive noise OFDM symbol to OFDM symbol  Incorporate knowledge of noise statistics  Add channel estimation  Analyze performance with coding and with correlated noise Wireless Networking and Communications Group 25

26. References  G. Caire, T. Al-Naffouri, and A. Narayanan, “Impulse noise cancellation in OFDM: an application of compressed sensing,” Proc. IEEE Int. Sym. on Info. Theory, 2008, pp. 1293–1297.  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.  K. Gulati, B. Evans, J. Andrews, and K. Tinsley, “Statistics of cochannel interference in a field of Poisson and Poisson-Poisson clustered interferers,” IEEE Trans. on Signal Proc., vol. 58, no. 12, pp. 6207–6222, 2010.  J. Haring and A. Vinck, “Iterative decoding of codes over complex numbers for impulsive noise channels,” IEEE Trans. Info. Theory, vol. 49, no. 5, pp. 1251–1260, 2003.  J. Lin, M. Nassar, and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning,” Proc. IEEE Int. Global Communications Conf., Dec. 5-9, 2011.  D. Middleton, “Statistical-Physical Models of Electromagnetic Interference”, IEEE Trans. On Electromagnetic Compatibility, vol. 19, no. 3, Aug. 1977, pp. 106-127.  D. Middleton, “Non-Gaussian noise models in signal processing for telecommunications: new methods an results for Class a and Class b noise models,” IEEE Trans. on Info. Theory, vol. 45, no. 4, pp. 1129–1149, 1999. Wireless Networking and Communications Group 26

27. References  M. Nassar, K. Gulati, M. DeYoung, B. Evans, and K. Tinsley, “Mitigating near-field interference in laptop embedded wireless transceivers,” Journal of Signal Proc. Sys., pp. 1–12, 2009.  M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc. IEEE Int. Global Communications Conf., Dec. 5-9, 2011.  M. Nassar and B. L. Evans, "Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise", Proc. Asilomar Conf. on Signals, Systems and Computers, Nov. 6-9, 2011.  H. W. Sorenson and D. L. Alspach, “Recursive Bayesian estimation using Gaussian sums”, Automatica, vol. 7, no. 4, July 1971, pp. 465-479.  E. S. Sousa, “Performance of a spread spectrum packet radio network link in a Poisson field of interferers,” IEEE Trans. on Info. Theory, vol. 38, no. 6, pp. 1743–1754, Nov. 1992.  D. Wipf and B. Rao, “Sparse Bayesian learning for basis selection,” IEEE Trans. Signal Proc., vol. 52, no. 8, pp. 2153–2164, 2004. Wireless Networking and Communications Group 27

28. BACK UP SLIDES 28 Wireless Networking and Communications Group

29. 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 Wireless Networking and Communications Group 29 Return

30. 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 Wireless Networking and Communications Group 30 Return

31. Wireless Networking and Communications Group Problem Statement 31  Designing wireless transceivers to mitigate residual RFI Guard zone Example: Dense Wi-Fi Networks Duration Channel 11 Channel 9 Transmit signal Pre-Filter Conventional Receiver RFI Thermal noise Distribution of Duration

32. Poisson Field of Interferers  Interferers distributed over parametric annular space  Log-characteristic function Wireless Networking and Communications Group 32 Return

33. Poisson Field of Interferers Wireless Networking and Communications Group 33 Return

34. Poisson-Poisson Cluster Field of Interferers  Cluster centers distributed as spatial Poisson process over  Interferers distributed as spatial Poisson process Wireless Networking and Communications Group 34 Return

35. Poisson-Poisson Cluster Field of Interferers  Log-Characteristic function Wireless Networking and Communications Group 35 Return

36. Gaussian Mixture vs. Alpha Stable  Gaussian Mixture vs. Symmetric Alpha Stable Wireless Networking and Communications Group 36 Gaussian MixtureSymmetric Alpha Stable ModelingInterferers distributed with Guard zone around receiver (actual or virtual due to pathloss function) Interferers distributed over entire plane Pathloss Function With GZ: singular / non-singular Entire plane: non-singular Singular form Thermal Noise Easily extended (sum is Gaussian mixture) Not easily extended (sum is Middleton Class B) OutliersEasily extended to include outliersDifficult to include outliers Return

37. 37 Wireless Networking and Communications Group Middleton Class A model  Probability Density Function PDF for A = 0.15,  = 0.8 ParameterDescriptionRange Overlap Index. Product of average number of emissions per second and mean duration of typical emission A  [10 -2, 1] Gaussian Factor. Ratio of second-order moment of Gaussian component to that of non-Gaussian component Γ  [10 -6, 1] Return

38. Home Power Line Noise Measurement Wireless Networking and Communications Group 38

39. Home Power Line Noise Measurement Wireless Networking and Communications Group 39 Spectrally-Shaped Background Noise

40. Home Power Line Noise Measurement Wireless Networking and Communications Group 40 Spectrally-Shaped Background Noise Narrowband Noise

41. Home Power Line Noise Measurement Wireless Networking and Communications Group 41 Spectrally-Shaped Background Noise Narrowband Noise Periodic and Asynchronous Noise

42. Analytical Models for Powerline Noise Wireless Networking and Communications Group 42

43. Expectation Maximization Overview Wireless Networking and Communications Group 43 Return

44. 44 Video over Impulsive Channels  Video demonstration for MPEG II video stream  10.2 MB compressed stream from camera (142 MB uncompressed)  Compressed file sent over additive impulsive noise channel  Binary phase shift keying Raised cosine pulse 10 samples/symbol 10 symbols/pulse length  Composite of transmitted and received MPEG II video streams http://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo1 9dB_correlation.wmv  Shows degradation of video quality over impulsive channels with standard receivers (based on Gaussian noise assumption) Wireless Networking and Communications Group Additive Class A NoiseValue Overlap index (A)0.35 Gaussian factor (  ) 0.001 SNR19 dB Return

45. Video over Impulsive Channels #2  Video demonstration for MPEG II video stream revisited  5.9 MB compressed stream from camera (124 MB uncompressed)  Compressed file sent over additive impulsive noise channel  Binary phase shift keying Raised cosine pulse 10 samples/symbol 10 symbols/pulse length  Composite of transmitted video stream, video stream from a correlation receiver based on Gaussian noise assumption, and video stream for a Bayesian receiver tuned to impulsive noise http://www.ece.utexas.edu/~bevans/projects/rfi/talks/video_demo1 9dB.wmv Wireless Networking and Communications Group 45 Additive Class A NoiseValue Overlap index (A)0.35 Gaussian factor (  ) 0.001 SNR19 dB Return

46. 46 Video over Impulsive Channels #2  Structural similarity measure [Wang, Bovik, Sheikh & Simoncelli, 2004]  Score is [0,1] where higher means better video quality Frame number Bit error rates for ~50 million bits sent: 6 x 10 -6 for correlation receiver 0 for RFI mitigating receiver (Bayesian) Return