Cyclostationary Noise Mitigation in Narrowband Powerline Communications
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Transcript of Cyclostationary Noise Mitigation in Narrowband Powerline Communications
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