Non-Parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications

1. Non-Parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications Jing Lin and Brian L. Evans Department of Electrical and Computer Engineering The University of Texas at Austin Dec. 11, 2013

2. PLC for Local Utility Smart Grid Applications Local utility Communication backhaul Data concentrator MV (1kV – 72.5kV)) • Narrowband (NB) PLC: • 3 – 500 kHz band • ~500 kbps using OFDM • Communication between smart meters and data concentrators Transformer LV (<1kV) Smart meters • Broadband PLC: • 1.8 – 250 MHz • 200 Mbps • Home area networks

3. Periodic Impulsive Noise in NB PLC • Dominant noise component in 3 – 500 kHz band Noise power spectral density raised by 30 – 50 dB during bursts Noise bursts arriving periodically – twice per AC cycle Noise measurements collected at an outdoor LV site [Nassar12]

4. Periodic Impulsive Noise in NB PLC • Noise sources • Switching mode power supplies generate harmonic contents that cannot be perfectly removed by analog filtering • Examples: inverters, DC-DC converters • Causes severe performance degradation • Commercial PLC modems feature low power transmission • Average SNR at receiver is between -5 and 5 dB • Conventional receiver designs assuming AWGN become sub-optimal

5. Prior Work • Transmitter methods • Receiver methods

6. Our Approach • Non-parametric methods to mitigate periodic impulsive noise • No assumption on statistical noise models & No training overhead • Impulsive noise estimation exploiting its sparsity in the time domain • Consider a time-domain block interleaving (TDI) OFDM system

7. Time-Domain Block Interleaving • After the de-interleaver at the receiver • An OFDM symbol observes a sparse noise vector in time domain • Interleaver size and burst duration determine the sparsity • Typical burst duration: 10% - 30% of a period • Interleaver size: one or more periods Interleave spread into short impulses A noise burst spans multiple OFDM symbols

8. Impulsive Noise Estimation • A compressed sensing problem [Caire08, Lin11] • Observe noise in null tones of received signal • Estimate time-domain noise exploiting its sparsity - Sub-DFT matrix - Indices of null tones - Impulsive noise after de-interleaving - AWGN

9. Sparse Bayesian Learning (SBL) • A Bayesian learning approach for compressed sensing [Tipping01] • Prior on promotes sparsity • ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters • MAP estimate of Scale Shape

10. Exploiting More Information • SBL performance is limited by the number of measurements • Null tones occupy 40 – 50% of the transmission band in PLC standards • A heuristic exploiting information on all tones • Iteratively estimate impulsive noise and transmitted data • Disadvantage: sensitive to initial value of - INestimator Zero out null tones + + -

11. Exploiting More Information (cont.) • Decision feedback estimation • Use to update hyperparameters

12. Simulation Settings • Baseband complex OFDM system • Periodic impulsive noise synthesized using a linear periodically time varying model in the IEEE P1901.2 standard [Nassar12]

13. Coded Bit Error Rate (BER) Performance 7 dB 7.5 dB Burst duration = 10% Burst duration = 30%

14. Conclusion • Non-parametric receiver methods to mitigate periodic impulsive noise in NB PLC • Do not assume statistical noise models, and do not need training • Work in time-domain block interleaving OFDM systems • Exploit the sparsity of the noise in the time domain • Estimate the noise samples from various subcarriers of the received signal and from decision feedback • Future work • Complexity reduction • Joint transmitter and receiver optimization

15. Reference • [Nassar12] M. Nassar, A. Dabak, I. H. Kim, T. Pande, and B. L. Evans, “CyclostationaryNoise Modeling In Narrowband Powerline Communication For Smart Grid Applications,” Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc, 2012. • [Dweik10] A. Al-Dweik, A. Hazmi, B. Sharif, and C. Tsimenidis, “Efficient interleaving technique for OFDM system over impulsive noise channels,” in Proc. IEEE Int. Symp. Pers. Indoor and Mobile Radio Comm., 2010. • [Nieman13] K. F. Nieman, J. Lin, M. Nassar, K. Waheed, and B. L. Evans, “Cyclic spectral analysis of power line noise in the 3-200 khz band,” in Proc. IEEE Int. Symp. Power Line Commun. and Appl., 2013. • [Yoo08] Y. Yoo and J. Cho, “Asymptotic analysis of CP-SC-FDE and UW-SC-FDE in additive cyclostationary noise,” Proc. IEEE Int. Conf. Commun., pp. 1410–1414, 2008. • [Lin12] J. Lin and B. Evans, “Cyclostationary noise mitigation in narrowband powerline communications,” Proc. APSIPA Annual Summit Conf., 2012. • [Caire08] G.Caire, T. Al-Naffouri, and A. Narayanan, “Impulse noise cancellation in OFDM: an application of compressed sensing,” in Proc. IEEE Int. Symp. Inf. Theory, 2008, pp. 1293–1297. • [Lin11] J. Lin, M. Nassar, and B. L. Evans, “Non-parametric impulsive noise mitigation in OFDM systems using sparse Bayesian learning,” Proc. IEEE Global Comm. Conf., 2011. • [Tipping01] M. Tipping, “Sparse Bayesian learning and the relevance vector machine,” J. Mach. Learn. Res., vol. 1, pp. 211–244, 2001.

16. Thank you

17. Local Utility Powerline Communications

18. Sparse Bayesian Learning (SBL) • A Bayesian learning approach for compressed sensing [Tipping01] • Prior on promotes sparsity • ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters • MAP estimate of Degrees of freedom Scale Scale Shape