0. 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

1. Local Utility Smart Grid Communications
Transformer
Smart meters
Data concentrator
Communication backhauls:
carry traffic between concentrator and utility
Last mile communications:
between smart meters and data concentrators
Home area networks:
interconnect smart appliances, line transducers and smart meters

2. 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
PRIME, G3
ITU-T G.hnem
IEEE P1901.2
Broadband (BB)
MHz
~200M
<1500 m
Home area networks
HomePlug
ITU-T G.hn
IEEE P1901

3. Non-Gaussian Noise in NB-PLC
Non-Gaussian noise is the most performance limiting factor in NB-PLC
Performance of conventional system degrades in non-AWGN
Non-Gaussian noise reaches dB/Hz above background noise in PLC
Typical maximum transmit power of a commercial PLC modem is below 40W
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

4. Cyclostationary Noise: Dominant in NB-PLC
Noise statistics vary periodically with half the AC cycle
Caused by switching mode power supplies (e.g. DC-DC converter, light dimmer)
Data collected at an outdoor low-voltage site

5. Statistical Modeling of Cyclostationary Noise
Linear periodically time varying(LPTV) system model [Nassar12, IEEE P1901.2]

6. Model Parameterization
Periodically switching linear autoregressive (AR) process
Introduce a state sequence ,
Parameterize each LTI filter by an order-r AR filter
AR coefficients at time k: …
State sequence
AR parameters
Observation

7. Nonparametric Bayesian Learning of Switching AR Model
Hidden Markov Model (HMM) assumption on the state sequence
HMM with infinite number of states
Transition probability matrix
should be sparse vectors (clustering)
Self transition is more likely than inter-state transitions
Sticky hierarchical Dirichlet Process (HDP) prior on
[Fox11]

8. Nonparametric Bayesian Learning of Switching AR Model
Learning AR coefficients conditioned on the state sequence
Partition into M groups corresponding to states 1 to M
Form M independent linear regression problems
Solve for using Bayesian linear regression … …
[Fox11]

9. Cyclostationary Noise Mitigation Approach
Estimate switching AR model parameters
Receiver can listen to the noise during no-transmission intervals
Estimate the switching AR model parameters
Noise whitening at the receiver ,

10. 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

11. Communication Performance
Uncoded
Coded

12. 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 P Std., June 2011, doc: 2wg 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.

13. Thank you