Slides - The University of Texas at Austin

Report
Cyclostationary Noise Mitigation in
Narrowband Powerline Communications
Jing Lin and Brian L. Evans
Department of Electrical and Computer Engineering
The University of Texas at Austin
Dec. 4, 2012
Local Utility Smart Grid Communications
Communication backhauls:
carry traffic between
concentrator and utility
Local utility
Data
concentrator
Last mile communications:
between smart meters
and data concentrators
Transformer
Smart meters
Home area networks:
interconnect smart appliances, line
transducers and smart meters
1
Local Utility Powerline Communications
Category
Band
Bit Rate
(bps)
Coverage
Applications
Ultra
Narrowband
(UNB)
0.3-3 kHz
~100
>150 km
Last mile comm.
Narrowband
(NB)
Broadband
(BB)
3-500 kHz
1.8-250
MHz
~500k
~200M
Multikilometer
<1500 m
Last mile comm.
Home area
networks
Standards
•
TWACS
•
PRIME, G3
•
ITU-T G.hnem
•
IEEE P1901.2
•
HomePlug
•
ITU-T G.hn
•
IEEE P1901
2
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
3
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
4
Statistical Modeling of Cyclostationary Noise
• Linear periodically time varying(LPTV) system model [Nassar12, IEEE P1901.2]
5
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:
…
State sequence
AR parameters
…
Observation
6
Nonparametric Bayesian Learning of Switching AR Model
• Hidden Markov Model (HMM) assumption on the state sequence
o HMM with infinite number of states
o Transition probability matrix
should be sparse vectors (clustering)
Self transition is more likely than inter-state transitions
o Sticky hierarchical Dirichlet Process (HDP) prior on
[Fox11]
7
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]
8
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
,
9
Simulation Settings
• An OFDM system
FFT
Size
# of
Tones
Data Tones
Sampling
Frequency
Modulation
FEC Code
256
128
#23 - #58
400 kHz
QPSK
Rate-1/2
Convolutional
• Cyclostationary noise is synthesized from the LPTV system model
10
Communication Performance
Uncoded
Coded
11
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-11-013405-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.
12
Thank you
13

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