Cyclostationary Noise Mitigation in Narrowband Powerline

Communications

Jing Lin and Brian L. EvansDepartment of Electrical and Computer

EngineeringThe University of Texas at Austin

Dec. 4, 2012

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Local Utility Smart Grid Communications

Local utility

Transformer

Smart meters

Data concentrator

Home area networks:interconnect smart appliances, line transducers and smart meters

Last mile communications:between smart meters and data concentrators

Communication backhauls:carry traffic between concentrator and utility

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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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Non-Gaussian Noise in NB-PLC

• Non-Gaussian noise is the most performance limiting factor in NB-PLC

o Performance of conventional system degrades in non-AWGN

o Non-Gaussian noise reaches 30-50 dB/Hz above background noise in PLC

o Typical maximum transmit power of a commercial PLC modem is below 40W

o Significant path loss

Power Lines 100 kHz LV 1.5-3 dB/km

MV (Overhead) 0.5-1 dB/km MV (Underground) 1-2 dB/km

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Cyclostationary Noise: Dominant in NB-PLC

• Noise statistics vary periodically with half the AC cycle

o Caused by switching mode power supplies (e.g. DC-DC converter, light dimmer)

Data collected at an outdoor low-voltage site

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Statistical Modeling of Cyclostationary Noise

• Linear periodically time varying(LPTV) system model [Nassar12, IEEE P1901.2]

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Model Parameterization

• Periodically switching linear autoregressive (AR) process

o Introduce a state sequence ,

o Parameterize each LTI filter by an order-r AR filter

…

…

AR coefficients at time k:

Observation

State sequence

AR parameters

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Nonparametric Bayesian Learning of Switching AR Model

• Hidden Markov Model (HMM) assumption on the state sequenceo HMM with infinite number of stateso Transition probability matrix

should be sparse vectors (clustering)

Self transition is more likely than inter-state transitionso Sticky hierarchical Dirichlet Process (HDP) prior on

[Fox11]

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Nonparametric Bayesian Learning of Switching AR Model

• Learning AR coefficients conditioned on the state sequence

o Partition into M groups corresponding to states 1 to M

o Form M independent linear regression problems

o Solve for using Bayesian linear regression

…

…

[Fox11]

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Cyclostationary Noise Mitigation Approach

• Estimate switching AR model parameters

o Receiver can listen to the noise during no-transmission intervals

o Estimate the switching AR model parameters

• Noise whitening at the receiver

o ,

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Simulation Settings

• An OFDM system

• Cyclostationary noise is synthesized from the LPTV system model

FFT Size

# of Tones Data Tones Sampling

Frequency Modulation FEC Code

256 128 #23 - #58 400 kHz QPSK Rate-1/2 Convolutional

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Communication Performance

Uncoded Coded

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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.

• [IEEE P1901.2] A. Dabak, B. Varadrajan, I. H. Kim, M. Nassar, and G. Gregg, Appendix for noise channel modeling for IEEE P1901.2, IEEE P1901.2 Std., June 2011, doc: 2wg-11-0134-05-PHM5.

• [Fox11] E. B. Fox, E. B. Sudderth, M. I. Jordan, A. S. Willsky, “Bayesian Nonparametric Inference of Switching Dynamic Linear Models,” IEEE Trans. on Signal Proc, vol. 59, pp. 1569–1585, 2011.

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Thank you