non-parametric mitigation of periodic impulsive noise in narrowband powerline communications

Non-Parametric Mitigation of Periodic Impulsive Noise in

Narrowband Powerline Communications

Jing Lin and Brian L. EvansDepartment of Electrical and Computer

EngineeringThe University of Texas at Austin

Dec. 11, 2013

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PLC for Local Utility Smart Grid Applications

Local utility

Transformer

Smart meters

Data concentrator

Broadband PLC:• 1.8 – 250 MHz• 200 Mbps• Home area networks

Narrowband (NB) PLC:• 3 – 500 kHz band• ~500 kbps using OFDM• Communication between smart

meters and data concentrators

Communication backhaul

LV (<1kV)

MV (1kV – 72.5kV))

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Periodic Impulsive Noise in NB PLC

• Dominant noise component in 3 – 500 kHz band

Noise bursts arriving periodically – twice

per AC cycle

Noise measurements collected at an outdoor LV site [Nassar12]

Noise power spectral density

raised by 30 – 50 dB during bursts

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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 dB

o Conventional receiver designs assuming AWGN become sub-optimal

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Prior Work

• Transmitter methods

• Receiver methods

Methods Data Rate Reduction

RX-TX Feedback

Performance Improvement

Concatenated coding [G3] Yes No Moderate

Time-domain interleaving [Dweik10] No No Low

Cyclic waterfilling [Nieman13] No Yes High

Methods Training Overhead

RX Complexity

Performance Improvement

MMSE equalizer [Yoo08] High Moderate Moderate

Whitening filter [Lin12] High Low Low

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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

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Time-Domain Block Interleaving

• After the de-interleaver at the receiver

o An OFDM symbol observes a sparse noise vector in time domaino Interleaver size and burst duration determine the sparsityo Typical burst duration: 10% - 30% of a periodo Interleaver size: one or more periods

A noise burst spans multiple OFDM symbols spread into short impulses

Interleave

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Impulsive Noise Estimation

• A compressed sensing problem [Caire08, Lin11]

o Observe noise in null tones of received signal

o Estimate time-domain noise exploiting its sparsity

- Sub-DFT matrix

- Indices of null tones

- Impulsive noise after de-interleaving

- AWGN

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Sparse Bayesian Learning (SBL)

• A Bayesian learning approach for compressed sensing [Tipping01]

o Prior on promotes sparsity

o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters

o MAP estimate of

Shape Scale

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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

o Iteratively estimate impulsive noise and transmitted data

o Disadvantage: sensitive to initial value of

INestimator ++ -

Zero out null tones

-

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Exploiting More Information (cont.)

• Decision feedback estimation

o Use to update hyperparameters

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Simulation Settings

• Baseband complex OFDM system

• Periodic impulsive noise synthesized using a linear periodically time varying model in the IEEE P1901.2 standard [Nassar12]

Parameters ValuesFFT Size 128

Modulation QPSK# of tones 128Data tones # 33 - # 104

Interleaver size ~ 2 periods of noiseForward Error Correction

Code Rate-1/2 Convolutional

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Coded Bit Error Rate (BER) Performance

Burst duration = 10% Burst duration = 30%

7.5 dB 7 dB

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Conclusion

• Non-parametric receiver methods to mitigate periodic impulsive noise in NB PLC

o Do not assume statistical noise models, and do not need trainingo Work in time-domain block interleaving OFDM systemso Exploit the sparsity of the noise in the time domaino Estimate the noise samples from various subcarriers of the received signal and

from decision feedback

• Future work

o Complexity reductiono Joint transmitter and receiver optimization

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Reference

• [Nassar12] 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.

• [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.

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Thank you

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Local Utility Powerline Communications

Category Band Bit Rate(bps) Coverage Applications Standards

Ultra Narrowband

(UNB)0.3-3 kHz ~100 >150 km Last mile comm. • TWACS

Narrowband(NB) 3-500 kHz ~500k

Multi-kilometer Last mile comm.

• PRIME, G3• ITU-T G.hnem• IEEE P1901.2

Broadband(BB)

1.8-250 MHz ~200M <1500 m Home area

networks• HomePlug• ITU-T G.hn• IEEE P1901

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Sparse Bayesian Learning (SBL)

• A Bayesian learning approach for compressed sensing [Tipping01]

o Prior on promotes sparsity

o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters

o MAP estimate of

Degrees of freedom Scale

Shape Scale