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

2

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))

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

-

11

Exploiting More Information (cont.)

• Decision feedback estimation

o 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]

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

13

Coded Bit Error Rate (BER) Performance

Burst duration = 10% Burst duration = 30%

7.5 dB 7 dB

14

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

15

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.

16

Thank you

17

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

18

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