NonParametric Mitigation of Periodic Impulsive Noise in Narrowband

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

PLC for Local Utility Smart Grid Applications Local utility Communication backhaul Data concentrato r MV (1 k. V – 72. 5 k. V)) Transformer LV (<1 k. V) Smart meter s Narrowband (NB) PLC: • 3 – 500 k. Hz band • ~500 kbps using OFDM • Communication between smart meters and data concentrators Broadband PLC: • 1. 8 – 250 MHz • 200 Mbps • Home area networks 1

Periodic Impulsive Noise in NB PLC • Dominant noise component in 3 – 500 k. Hz band Noise power spectral density raised by 30 – 50 d. B during bursts Noise bursts arriving periodically – twice per AC cycle Noise measurements collected at an outdoor LV site [Nassar 12] 2

Periodic Impulsive Noise in NB PLC • Noise sources o Switching mode power supplies generate harmonic contents that cannot be perfectly removed by analog filtering o Examples: inverters, DC-DC converters • Causes severe performance degradation o Commercial PLC modems feature low power transmission o Average SNR at receiver is between -5 and 5 d. B o Conventional receiver designs assuming AWGN become suboptimal 3

Prior Work • Transmitter methods Methods Data Rate Reduction RX-TX Feedback Performan ce Improvem ent Concatenated coding [G 3] Yes No Moderate Time-domain interleaving No No Low No Yes High [Dweik 10] • Receiver methods Cyclic waterfilling [Nieman 13] Performan ce RX Complexity Improveme nt Methods Training Overhead MMSE equalizer [Yoo 08] High Moderate Whitening filter [Lin 12] High Low 4

Our Approach • Non-parametric methods to mitigate periodic impulsive noise o No assumption on statistical noise models & No training overhead o Impulsive noise estimation exploiting its sparsity in the time domain o Consider a time-domain block interleaving (TDI) OFDM system 5

Time-Domain Block Interleaving • After the de-interleaver at the receiver Interleave A noise burst spans multiple OFDM symbols spread into short impulses o o 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 6

Impulsive Noise Estimation • A compressed sensing problem [Caire 08, Lin 11] - Sub-DFT matrix - Indices of null tones - Impulsive noise after de-interlea - AWGN o Observe noise in null tones of received signal o Estimate time-domain noise exploiting its sparsity 7

Sparse Bayesian Learning (SBL) • A Bayesian learning approach for compressed sensing [Tipping 01] o Prior on promotes sparsity Shap e Scal e o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters o MAP estimate of 8

Exploiting More Information • SBL performance is limited by the number of measurements o Null tones occupy 40 – 50% of the transmission band in PLC standards • A heuristic exploiting information on all tones - Zero out IN o Iteratively noise and transmitted data + estimate impulsive + null estimator tones o Disadvantage: sensitive to initial value of 9

Exploiting More Information (cont. ) • Decision feedback estimation o Use to update hyperparameters 10

Simulation Settings • Baseband complex OFDM system Parameters Values FFT Size 128 Modulation QPSK # of tones 128 Data tones # 33 - # 104 Interleaver size ~ 2 periods of noise Forward Error Correction Code Rate-1/2 Convolutional • Periodic impulsive noise synthesized using a linear periodically time varying model in the IEEE P 1901. 2 standard [Nassar 12] 11

Coded Bit Error Rate (BER) Performance 7. 5 d. B Burst duration = 10% 7 d. B Burst duration = 30% 12

Conclusion • Non-parametric receiver methods to mitigate periodic impulsive noise in NB PLC o o 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 o Complexity reduction o Joint transmitter and receiver optimization 13

Reference • [Nassar 12] M. Nassar, A. Dabak, I. H. Kim, T. Pande, and B. L. Evans, “Cyclostationary Noise Modeling In Narrowband Powerline Communication For Smart Grid Applications, ” Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc, 2012. • [Dweik 10] 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. • [Nieman 13] 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. • [Yoo 08] 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. • [Lin 12] J. Lin and B. Evans, “Cyclostationary noise mitigation in narrowband powerline communications, ” Proc. APSIPA Annual Summit Conf. , 2012. • [Caire 08] 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. • [Lin 11] 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. • [Tipping 01] M. Tipping, “Sparse Bayesian learning and the relevance vector machine, ” J. Mach. Learn. Res. , vol. 1, pp. 211– 244, 2001. 14

Thank you 15

Local Utility Powerline Communications Category Band Ultra Narrowban 0. 3 -3 k. Hz d (UNB) Narrowban d (NB) 3 -500 k. Hz Bit Rate Coverag (bps) e ~100 ~500 k >150 km Multikilomete r Applications Last mile comm. Standards • TWACS • PRIME, G 3 • ITU-T G. hnem • IEEE P 1901. 2 Broadband (BB) 1. 8 -250 MHz ~200 M <1500 m Home area networks • Home. Plug • ITU-T G. hn • IEEE 16

Sparse Bayesian Learning (SBL) • A Bayesian learning approach for compressed sensing [Tipping 01] o Prior on promotes sparsity Shap e Degrees of freedom Scal e o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters o MAP estimate of 17