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