Cyclostationary Noise Mitigation in Narrowband Powerline Communications Jing Lin and Brian L. Evans Department of Electrical and Computer Engineering The.

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