Robust Transceivers to Combat Impulsive Noise in Powerline Communications. Jing Lin Committee Members. Prof. Brian L. Evans (Supervisor) Prof. Todd E. Humphreys Prof . Alexis Kwasinski Prof. Ahmed H. Tewfik Prof. Haris Vikalo. Outline.
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Robust Transceivers to Combat Impulsive Noise in Powerline Communications
Jing Lin
Committee Members
Prof. Brian L. Evans (Supervisor)
Prof. Todd E. Humphreys
Prof. Alexis Kwasinski
Prof. Ahmed H. Tewfik
Prof. HarisVikalo
Wind farm
HVMV Transformer
Central power plant
Grid status monitoring
Utility control center
Smart metering
Integrating distributed energy resources
Offices
Homes
Devicespecific billing
Building automation
Industrial sites
Communication backhaul
Wireless / Optical
Local utility
Data concentrator
Neighborhood Area Networks (NAN)
Wireless / Powerline
MVLV Transformer
Smart meters
Home Area Networks (HAN)
Wireless / Powerline
PLC systems use Orthogonal Frequency Multiplexing Division (OFDM)
An impulse collected at an indoor power line
Normalized power spectral density of an impulse
Figures from [Zimmermann02, Cortes11]
Noise collected from an outdoor LV power line
Reliability of smart grid communications over power lines can be dramatically improved without sacrificing throughput
by exploiting sparsity and cyclostationarityof the impulsive noise
in both time and frequency domains.
z
z
z
 Mixing probability
samples
samples
samples
 Variance of Gaussian components
 Overlap index
 Mean intensity
1
2
Coherence time of noise statistics varies from millisecsto hours
Parameter Estimator
Parametric
Decoder
Noise
Decodedbits
Received signal
+
Impulsive Noise Estimator
Conventional Decoder
Received signal

Decodedbits
Amplitude
Amplitude
Frequency
Time
Data
Null
Null
 DFT matrix;  Indices of null tones
Prior
MAP Estimation
Expectation Maximization (EM)
Control sparsity
Hyperprior
IG  Inverse Gamma distribution
MAP  Maximum a posteriori
+
+
+

Conventional Decoder
FFT
SBL

Signal Reconstruction

SBL – Sparse Bayesian learning
FFT – Fast Fourier transform
* Measured in GM noise at 104coded BER, compared with conventional OFDM receivers** Assuming GM noise model and perfect knowledge of the model parameters
AWGN
+
Interleaving over half the AC cycle
Conventional Receiver
Π1
FFT
SBL
Channel Equalizer

* Measured in synthesized noise at 104coded BER, compared with conventional OFDM receivers using frequencydomain interleaving
Subchannel SNR is highly frequencyselective and timevarying
Wideband impulses
Narrowband interferences
SNR
X
Symbols
Subchannels
X
Bits
✔
Data rate = 1 bit / channel use
[Schober03]
011
100
011
110
010
010
110
110
010
000
000
000
111
111
111
Optimal length3 code in Rayleigh fading channel
001
101
001
101
001
101
[Hochwald00]
100
011
100
Subcarriers
…
…
…
…
Timedomain noise
OFDM symbols
Estimated subchannel
Diversity Demodulator
Received signal
Loglikelihood ratio (LLR)
Estimated noise power
Low
Med
High
Transmission
Time
Offline
Semionline
Workload at the noise power estimator
Cyclic Prefix
OFDM symbol
Noise
AWGN
NBI

+
Prior [Zhang11]
Row sparsity
Temporal correlation
EM Updates
Diversity Receiver
Hyperprior
Slicing Error Estimation
IG  Inverse Gamma distribution; IW  Inverse Wishart distribution
EM  Expectation maximization
System parameters
TimeFrequency modulation diversity
Subcarriers
Subcarriers
…
…
…
…
…
…
…
…
…
OFDM symbols
OFDM symbols
Length2 code
>100x
Length3 code
>2dB
Thesis Statement
Reliability of smart grid communications over power lines can be dramatically improved without sacrificing throughput
by exploiting sparsity and cyclostationarityof the impulsive noise
in both time and frequency domains.
Journal Articles
Conference Publications