2013 National Instruments Week
Smart Grid Communications
Prof. Brian L. Evans
Dept. of Electrical & Computer Engineering
Wireless Networking & Communications Group
The University of Texas at Austin
7 August 2013
In collaboration with
Ms. Jing Lin, Mr. Yousof Mortazavi, Mr. Marcel Nassar & Mr. Karl Nieman at UT
Mr. Mike Dow & Dr. Khurram Waheed at Freescale Semiconductor (Austin)
Dr. Anuj Batra, Dr. Anand Dabak & Dr. Il Han Kim at Texas Instruments (Dallas)
Dr. Doug Kim, Mr. James Kimery, Mr. Mike Trimborn and Dr. Ian Wong (NI)
http://users.ece.utexas.edu/~bevans/projects/plc/index.html
Austin, Texas USA
ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN
Outline
• Smart power grids
• Powerline noise
Types
Modeling
• Receiver design
• Testbeds
• Conclusion
IEEE Signal Processing Magazine
Special Issue on Signal Processing Techniques
for the Smart Grid, September 2012.
1
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Smart Grid
Wind farm
HV-MV Transformer
Central power plant
Grid status monitoring
Utility control center
Smart meters
Integrating distributed
energy resources
Houses
Offices
Device-specific billing
Automated control for
smart appliances
Medium Voltage (MV)
1 kV – 33 kV
three phase
Industrial plant
High Voltage (HV)
33 kV – 765 kV
three phase
2
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Smart Grid Goals
• Improve asset utilization and operating efficiencies
Reduce peak load (generation cost 30x vs. average load)
Reduce excess power generation (12% margin in US)
Accommodate all energy sources (renewable, storage)
Scale grid voltage with energy demand
• Smart meter communications
Communicate grid load snapshots to utility for analysis
Enable reduction of peak demand (e.g. duty cycling AC and scaling billing rate)
Monitor power quality
75M smart meters sold in 2011
Disconnect/reconnect remotely
EU goal of 80% smart meter
Notify outage/restoration event
deployments by 2020
Enable informed customer participation
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
3
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Smart Meter Communications
Communication backhaul
carries traffic between
concentrator and utility
on wired or wireless links
Local utility
Data
concentrator
Low voltage (LV)
under 1 kV
single phase
Smart meters
MV-LV transformer
Smart meter communications
between smart meters and
data concentrator via
powerline or wireless links
Home area data networks
connect appliances, EV charger and smart
meter via powerline or wireless links
4
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Wireless Smart Meter Communications
Category
Band
Bit Rates
Coverage
Enables
Standards
Meter to
customer
2.4
GHz
Up to
250 kbps
100m
Customer
participation
• 802.11b/g
• 802.15.4 (ZigBee)
Meter to
concentrator
900
MHz
Up to
250 kbps
1000m
Smart meter • 802.11ah (draft)
communication • 802.15.4g
Concentrator
to utility
900
MHz
Up to
800 kbps
1000m
Smart meter • 802.11ah (draft)
communication • 802.15.4g
• Use orthogonal frequency division multiplexing (OFDM)
• Communication challenges
Channel distortion
IEEE 802.15.4g will likely initially
use frequency shift keying (FSK)
Non-Gaussian noise/interference in unlicensed bands
5
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Powerline Communications (PLC) for Smart Meters
Category
Band
Bit Rates
Coverage
Enables
Standards
Broadband
1.8-250
MHz
Up to
200 Mbps
<1500 m
Narrowband
3-500
kHz
Up to
800 kbps
• PRIME, G3
MultiSmart meter
• ITU-T G.990x
kilometer communication
• IEEE P1901.2
• HomePlug
Home area
• ITU-T G.996x
data networks
• IEEE P1901
• Use orthogonal frequency division multiplexing (OFDM)
• Communication challenges
Channel distortion
Non-Gaussian noise/interference
6
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Narrowband PLC Transceiver Design
• Periodic bursty transmission of customer load profile
Once every 15 minutes is common today and up to once every minute in future
Uses carrier sense multiple access (CSMA) to see if medium is available
• OFDM transmission
Most of transmission band unusable
Pilot tones, and null tones on band edges and unused tones
• Channel modeling [Nassar12mag]
Transfer functions – include effect of MV-LV transformer for US and Brazil
Additive noise/interference – impulsive noise up to 40 dB higher than thermal
• Global synchronization to AC main frequency (50 or 60 Hz)
• PLC modem should consume less power than small light bulb (30W)
Low power consumption should enable large-scale deployments
Largest power consumption in power amplifier for transmission (e.g. 10V / 1.5A)
7
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Types of Powerline Noise
Cyclostationary
Impulsive Noise
Background Noise
Asynchronous
Impulsive Noise
-50
-100
-150
0
100
200
300
Frequency (kHz)
400
time
500
Spectrally shaped noise
with 1/f spectral decay
Periodic: Synchronous and
asynchronous to half AC cycle
Random impulsive bursts
micro to milliseconds long
Superposition of low
intensity noise sources
Switching power supplies
and rectifiers
Circuit transient noise and
uncoordinated interference
Present in all PLC
Dominant in
Narrowband PLC
Dominant in
Broadband PLC
8
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Periodic Noise from DC-DC Buck Converter
• Spectrum has first peak at twice main AC frequency
• Harmonics at multiples of MOSFET switching frequency (16.9 kHz)
Buck converter
Resulting noise has periodicities in
time domain at 120 Hz &16.9 kHz
9
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Periodic Noise from DC-DC Buck Converter
Time-domain voltage
output ripple varies
periodically at 120 Hz
Impulsive noise
at switching
transients
Note: DC value has
been filtered out
Zoom in to see
16.9 kHz component
10
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise
Medium Voltage Site
Segment: 1 2 3
Low Voltage Site
Segment: 1 2 3
Period is one
half of AC cycle
Field measurements collected jointly with Aclara and
Texas Instruments near St. Louis, Missouri USA
11
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise Modeling
Measurement data
from UT/TI field trial
Cyclostationary
Gaussian Model
Proposed model
uses three filters
[Katayama06]
[Nassar12]
Demux
Period is one half
of an AC cycle
s[k] is zero-mean
Gaussian noise
Adopted by IEEE P1901.2
narrowband PLC standard
12
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Asynchronous Impulsive Noise Modeling
• Additive interference
from multiple sources
Interference
from source i
Assume source emissions are modeled by Poisson distribution
Attenuation g(d) = exp(-a(f) d) where d is distance
Homogeneous network
li = l, mi = m
Ex. Semi-urban
areas, apartment
complexes
Middleton
class A
Ex. Dense urban
and commercial
settings
Gaussian
mixture
model
General (heterogeneous) network
li, mi
Middleton Class A is a special case of Gaussian mixture model
13
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Asynchronous Noise Model Fitting
Homogeneous PLC Network
General PLC Network
Tail probabilities (which direct relate to communication performance)
Models also work for additive uncoordinated wireless interference
Middleton Class A for a Wi-Fi receiver in a Wi-Fi hotspot
Gaussian mixture model for a Wi-Fi receiver in a cluster of Wi-Fi hotspots
14
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
OFDM Systems in Impulsive Noise
• FFT in receiver spreads impulsive energy over all tones
Signal-to-noise ratio (SNR) in each subchannel decreases
• Narrowband PLC systems operate over -5 dB to 5 dB in SNR
Data subchannels carry same number of bits (1-4) in current standards
Each 3 dB increase in SNR on data subchannels could give extra bit
15
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Mitigating Impulsive Noise in OFDM Systems
• A linear system with Gaussian disturbance
v
y = Fe  FHF * x  Fn = Fe  v,
g

v ~ CN (x,  2 I )
Estimate the impulsive noise and remove it from the received signal
yˆ = y  Feˆ  x  g
Then apply standard OFDM decoder as if only AWGN were present
16
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Proposed Non-Parametric Receiver Methods
• Exploit sparsity of impulsive noise in time domain
time
Build statistical model each OFDM symbol using sparse Bayesian learning (SBL)
At receiver, null tones contain only additive noise (Gaussian + impulsive)
• SNR gain vs. conventional OFDM systems at bit error rate 10-4
Complex OFDM, 128-point FFT, QPSK, data tones 33-104, rate ½ conv. code
Test SBL algorithms using additive three-term Gaussian mixture model (GMM)
noise and Middleton Class A (MCA) noise with A = 0.1 and  = 0.01
System
Uncoded
Coded
Noise
SBL w/
null tones
SBL w/
all tones
SBL w/ decision
feedback
GMM
8 dB
10 dB
-
MCA
6 dB
7 dB
-
GMM
2 dB
7 dB
9 dB
MCA
1.75 dB
6.75 dB
8.75 dB
Every SNR gain of 3 dB could mean +1 bit/tone
17
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Time Domain Interleaving
Bursts span consecutive OFDM symbols
Coded performance in cyclostationary noise
Interleave
Bursts spread over many OFDM symbols
PLC standards use frequency-domain interleaving
Complex OFDM, 128-point FFT, QPSK,
data tones 33-104, rate ½ conv. Code
Burst duty cycle of 30%
18
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Testbed #1: Built on Previous DSL Testbed
• Adaptive signal processing algorithms for bit loading and
interference mitigation
Hardware
Software
• NI x86 controllers stream data
• Transceiver algorithms in C on x86
• NI cards generates/receives analog signals • Desktop LabVIEW configures system
• TI front end couples to power line
and visualizes results
1x1 Testbed
19
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Testbed #2: Noise Playback/Analysis
• G3 link using two Freescale G3 PLC modems
• Freescale software tools allow frame-by-frame analysis
• Test setup allows synchronous noise injection into power line
Freescale PLC G3-OFDM Modem
• One modem to sample
Freescale PLC Testbed
powerline noise in field
• Collected 16k 16-bit 400
kS/s at each location
20
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Testbed #2: Cyclic Power Line Noise
10
subcarrier
50
0
-10
40
-20
-30
30
-40
10
20
30
40
OFDM Symbol
50
subcarrier
50
noise power vs avg (dB)
• Analyzed cyclic properties of PLC noise measurements
• Developed cyclic bit loading method for transmitter
D8PSK
DQPSK
DBPSK
40
1. Receiver measures noise
power over half AC cycle
2. Feedback modulation map
to transmitter
3. Allocate more bits in
higher SNR subchannels
ROBO
30
NONE
10
20
30
40
OFDM Symbol
50
2x increase in bit rate
Won ISPLC 2013 Best Paper Award
21
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Testbed #3: FPGA Implementation
• Built NI/LabVIEW testbed with real-time link (G3 PLC settings)
• Redesigned parametric impulsive noise mitigation algorithm
Based on approximate message passing (AMP) framework
Converted matrix operations to distributed calculations on scalars
• Mapped transceiver to fixed-point data/arithmetic using Matlab
• Synthesized NI LabVIEW DSP Diagram onto Xilinx Vertex 5 FPGAs
SNR gain of up to 8 dB
Received QPSK constellation at equalizer output
conventional receiver
with AMP
Utilization
FPGA
Trans.
Rec.
AMP+Eq
1
2
3
total slices
32.6%
64.0%
94.2%
slice reg.
15.8%
39.3%
59.0%
slice LUTs
17.6%
42.4%
71.4%
DSP48s
2.0%
7.3%
27.3%
blockRAMs
7.8%
18.4%
29.1%
22
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Conclusion
• Powerline communication systems are interference limited
• Statistical models powerline interference
Cyclostationary model is synchronous with zero crossings of AC cycle
Gaussian mixture model is for asynchronous impulsive noise
• Interference mitigation algorithms give up to 10 dB of SNR gain
Non-parametric sparse Bayesian learning algorithms do not map well to FPGAs
Parametric distributed approximate message algorithms map well to FPGAs
• Future research for smart meter communications
Use diversity of powerline and wireless links to data concentrator
Maintain minimum quality link under extreme conditions
Reduce power consumption in transmitter front end
Project Web site: http://users.ece.utexas.edu/~bevans/projects/plc/index.html
23
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
References
•
•
•
•
•
•
•
•
•
•
[Caire08] G. Caire, T.Y. Al-Naffouri, and A.K. Narayanan. Impulse noise cancellation in OFDM: an
application of compressed sensing. Proc. IEEE Int. Symp. Information Theory, pages 1293–1297, 2008.
[Cho04] J. H. Cho. Joint transmitter and receiver optimization in additive cyclostationary noise. IEEE
Trans. on Information Theory, vol. 50, no. 12, 2004.
[Garcia07] R. Garcia, L. Diez, J.A. Cortes, and F.J. Canete. Mitigation of cyclic short-time noise in indoor
power-line channels. Proc. IEEE Int. Symp. Power Line Comm. and Its Applications, pp. 396–400, 2007.
[Haring02] J. Haring. Error Tolerant Communication over the Compound Channel. Aachen, 2002.
[Haring03] J. Haring and A. J. H. Vinck. Iterative decoding of codes over complex numbers for impulsive
noise channels. IEEE Trans. on Information Theory, 49(5):1251–1260, 2003.
[Katayama06] M. Katayama, T. Yamazato, and H. Okada. A mathematical model of noise in narrowband
power line communication systems. IEEE J. Sel. Areas in Commun., vol. 24, no 7, pp. 1267-1276, 2006.
[Lampe11] L. Lampe. Bursty impulse noise detection by compressed sensing. Proc. IEEE Int. Symp.
Power Line Commun. and Appl., pages 29–34, 2011
[Liano11] A. Liano, A. Sendin, A. Arzuaga, and S. Santos. Quasi-synchronous noise interference cancellation techniques applied in low voltage PLC. Proc. IEEE Int. Symp. Power Line Comm. and Its
Applications, 2011.
[Lin11] J. Lin, M. Nassar, and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems
Using Sparse Bayesian Learning”, Proc. IEEE Int. Global Comm. Conf., 2011.
[Lin12] J. Lin and B. L. Evans, “Cyclostationary Noise Mitigation in Narrowband Powerline
Communications”, Proc. APSIPA Annual Summit and Conf., 2012.
24
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
References
•
•
•
•
•
•
•
•
•
[Nassar09] M. Nassar, K. Gulati, M. DeYoung, B.L. Evans, and K. Tinsley. Mitigating near-field
interference in laptop embedded wireless transceivers. Journal of Signal Proc. Systems, pp. 1–12, 2009.
[Nassar11] M. Nassar and B.L. Evans. Low Complexity EM-based Decoding for OFDM Systems with
Impulsive Noise. In Proc. Asilomar Conf. on Sig., Systems, and Computers, 2011. .
[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 Sig. Proc., pages 3089–3092, 2012.
[Nassar12mag] M.Nassar, J.Lin, Y. Mortazavi, A.Dabak, I.H.Kim and B.L.Evans, “Local Utility Powerline
Communications in the 3-500 kHz Band: Channel Impairments, Noise, and Standards”, IEEE Signal
Processing Magazine, vol. 29, no. 5, pp. 116-127, Sep. 2012.
[Nieman13] K. Nieman, J. Lin, M. Nassar, K. Waheed and B. L. Evans, “Cyclic Spectral Analysis of
Power Line Noise in the 3-200 kHz Band”, Proc. IEEE Int. Sym. on Power Line Communications and Its
Applications, Mar. 24-27, 2013.
[Pauli06] V. Pauli, L. Lampe, and R. Schober. ”turbo dpsk” using soft multiple-symbol differential sphere
decoding. IEEE Trans. on Information Theory, 52(4):1385–1398, 2006.
[Raphaeli96] D. Raphaeli. Noncoherent coded modulation. IEEE Trans. on Comm., vol. 44, no. 2, pp.
172–183, 1996.
[Tipping01] M.E. Tipping. Sparse Bayesian learning and the relevance vector machine. Journal of
Machine Learning Research, vol. 1, pp. 211–244, 2001.
[Umehara01] D. Umehara, M. Kawai, and Y. Morihiro. Performance analysis of noncoherent coded
modulation for power line communications. Proc. Int. Symp. Power Line Commun. and Its Appl., pp.
291–298, 2001.
25
Backup Slides
26
Research Group
•
•
•
•
Present: 9 PhD, 0 MS, 4 BS
Alumni: 21 PhD, 9 MS, 142 BS
1376 alumni of real-time DSP course
Communication systems
Powerline communications (interference modeling & mitigation)
Cellular, Wimax & Wi-Fi (interference modeling & mitigation)
Mixed-signal IC design (mostly digital ADCs and synthesizers)
• Image processing
• Electronic design automation (EDA) tools/methods
• Part of Wireless Networking & Communications Group
160 grad students, 20 faculty members, 13 affiliate companies
Completed Projects
20 PhD and 9 MS alumni
System
SW release
Prototype
Funding
equalization
Matlab
DSP/C
Freescale, TI
2x2 testbed
LabVIEW
LabVIEW/PXI
Oil&Gas
Wimax/LTE
resource alloc.
LabVIEW
DSP/C
Freescale, TI
Underwater
comm.
space-time comm.
large rec. arrays
Matlab
Lake Travis
testbed
UT Applied
Res. Labs
Camera
image acquisition
Matlab
DSP/C
Intel, Ricoh
Display
image halftoning
Matlab
C
HP, Xerox
video halftoning
Matlab
C
Qualcomm
Matlab
FPGA
Intel, NI
Linux/C++
Navy sonar
Navy, NI
ADSL
Contribution
Elec. design fixed point conv.
automation distributed comp.
DSP: Digital Signal Processor
PXI: PCI Extensions for Inst.
Current Projects
9 PhD students
System
Contributions
Powerline interference reduction
comm.
testbeds
Wi-Fi
interference reduction
SW release
LabVIEW
Matlab
time-based analog-todigital converter
Cellular
(LTE)
Matlab
Handheld reducing rolling shutter
camera
artifacts
Matlab
reliability patterns
Funding
Freescale, TI Freescale,
modems
IBM, TI
NI FPGA
Intel, NI
IBM 45nm
TSMC 180nm
cloud radio access net.
baseband compression
EDA
Prototype
Huawei
Android
TI
NI
Simulated Performance
• Symbol error rate in different noise scenarios
~10dB
~6dB
~6dB
~8dB
~4dB
Gaussian mixture model
Middleton class A model
• MMSE w/ (w/o) CSI: Parametric estimator assuming known (unknown) statistical parameters of noise
• CS+LS: A compressed sensing and least squares based algorithm
30
A Smart Grid
Communication to
isolated area
Power generation
optimization
Integrating
alternative energy
sources
Load balancing
Disturbance
monitoring
Smart metering
Electric car charging &
smart billing
Source: ETSI
31
Power Lines
• Built for unidirectional energy flow
• Bidirectional information flow
throughout smart grid will occur
Low Voltage (LV)
under 1 kV
High Voltage (HV)
33 kV – 765 kV
Medium Voltage (MV)
1 kV – 33 kV
Transformer
Source: ERDF
32
Today’s Power Grids in the United States
• 7 large-scale power grids each managed by a regional utility company
700 GW generation capacity in total for long-haul high-voltage power transmission
Synchronized independently, and exchange power via DC transfer
• 130+ medium-scale power grids each managed by a local utility
Local power distribution to residential, commercial and industrial customers
• Heavy penalties in US for blackouts (2003 legislation)
Utilities generate expected energy demand plus 12%
Energy demand correlated with time of day
Effect of plug-in electric vehicles (EVs) on energy demand uncertain
Generation cost 30x higher during peak times vs. normal load
• Traditional ways to increase capacity to meet peak demand increase
Build generation plant $1B to $10B if new permit is issued
Build transmission line at $0.6M/km which will take 5-10 years to complete
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
33
Smart Power Meters at Customer Site
• Enable local utilities to improve
Operating efficiency
System reliability
Customer participation
• Automatic metering infrastructure functions
Interval reads (every 1/15/30/60 minutes) and on-demand reads and pings
Transmit customer load profiles and system load snapshots
Power quality monitoring
Remote disconnect/reconnect and outage/restoration event notification
• Need low-delay highly-reliable communication link to local utility
• 75M smart meters sold in 2011 (20% increase vs. 2010)
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA
34
Local Utility Powerline Communications (PLC)
• PLC modems (PRIME, etc.) use carrier sensed multiple access
to determine when the medium is available for transmission
• MV router plays similar role as a Wi-Fi access point
35
Sources of Powerline Noise
Uncoordinated
transmission
Power line
disturbance
Electronic devices
Taken from
a local utility
point of view
36
PLC In Different Frequency Bands
Category
Band
Ultra
Narrowband
Narrowband
Broadband
Bit Rate
Applications
Standards
0.3 – 3
kHz
~100 bps
• Automatic meter reading
• Outage detection
• Load control
N/A
3 – 500
kHz
• Smart metering
~500 kbps • Real-time energy
management
1.8 – 250
~200 Mbps • Home area networks
MHz
• PRIME, G3
• ITU-T G.hnem
• IEEE P1901.2
• HomePlug
• ITU-T G.hn
• IEEE P1901
All of the above standards are based on multicarrier communications using orthogonal
frequency division multiplexing (OFDM).
37
Physical Layer Parameters for
OFDM Narrowband PLC Standards
CENELEC A band is from 3 to 95 kHz. FCC band is from 34.375 to 487.5 kHz.
PRIME and G3 use real-valued baseband OFDM. Others are complex-valued.
38
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Comparison Between Wireless and PLC Systems
Wireless Communications
Narrowband PLC (3-500 kHz)
Time-selective fading and
Doppler shift (cellular)
Periodic with period of half AC main freq.
plus lognormal time-selective fading
Power loss vs.
distance d
d –n/2 where n is
propagation constant
e – a(f) d plus additional attenuation when
passing through transformers
Propagation
Dynamically changing
Determinism from fixed grid topology
Synchronization
Varies
AC main power frequency
Additive noise/
interference
Assumed stationary
and Gaussian
Gaussian plus non-Gaussian noise
dominated by cyclostationary component
Time selectivity
Asynchronous
interference
MIMO
Uncoordinated users in
Due to power electronics and
Wi-Fi bands;
uncoordinated users using other standards
Frequency reuse in cellular
Standardized for
Wi-Fi and cellular
Number of wires minus 1;
G.9964 standard for broadband PLC
39
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise
• Linear periodically time-varying system model
H1
vR
H2
N
n  RN
…
HM
Hi - Linear time invariant filter
N - Period in samples
o Period (half of the AC cycle) is partitioned into M segments
o Noise within each segment is stationary
Segment: 1
23
40
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Asynchronous Impulsive Noise Modeling
Wireless
Emissions
Uncoordinated
Meters
(coexistence)
Total interference at receiver:
Interference
from source i
41
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Two Asynchronous Impulsive Noise Models
• Gaussian Mixture Model (isotropic, zero-centered)
Amplitude distribution
• Middleton Class A (without additive Gaussian component)
Special case of the Gaussian Mixture Model
• Also model for additive uncoordinated wireless interference
Middleton Class A for a Wi-Fi receiver in a Wi-Fi hotspot
Gaussian mixture model for a Wi-Fi receiver in a cluster of Wi-Fi hotspots
42
Non-Gaussian Noise: Challenge to PLC
• Performance of conventional communication system degrades in
non-AWGN environment
• Statistical modeling of powerline noise
• Noise mitigation exploiting the noise model or structure
Listen to the environment
Estimate noise model
Use model or structure to mitigate noise
43
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Narrowband PLC Systems
• Problem: Non-Gaussian impulsive noise is primary limitation to
communication performance yet traditional communication system
design assumes additive noise is Gaussian
• Goal: Improve communication performance in impulsive noise
• Approach: Statistical modeling of impulsive noise
• Solution #1: Receiver design (standard compliant)
Parametric Methods
Nonparametric Methods
Listen to environment
No training necessary
Find model parameters
Learn statistical model from
communication signal structure
Use model to mitigate noise
Exploit sparsity to mitigate noise
• Solution #2: Joint transmitter-receiver design
44
Parametric vs. Nonparametric Noise Mitigation
Parametric
Nonparametric
Must build a statistical
model of the noise
Yes
No
Requires training data to
compute model parameters
Yes
No
Degrades in performance
due to model mismatch
Yes
No
Has high complexity when
receiving message data
No
Yes
45
Cyclostationary Noise Modeling
in Narrowband PLC (3-500 kHz)
1. 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.,
Mar. 25-30, 2012, Kyoto, Japan.
2. M. Nassar, J. Lin, Y. Mortazavi, A. Dabak, I. H. Kim and B. L. Evans, “Local
Utility Powerline Communications in the 3-500 kHz Band: Channel
Impairments, Noise, and Standards”, IEEE Signal Processing Magazine,
Special Issue on Signal Processing Techniques for the Smart Grid, Sep.
2012, 14 pages.
46
Impulsive Noise in Broadband PLC:
Modeling and Mitigation
3. M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of
Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc.
IEEE Int. Global Communications Conf., Dec. 5-9, 2011, Houston, TX USA.
4. J. Lin, M. Nassar and B. L. Evans, “Non-Parametric Impulsive Noise
Mitigation in OFDM Systems Using Sparse Bayesian Learning”, Proc. IEEE
Int. Global Communications Conf., Dec. 5-9, 2011, Houston, TX USA.
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Statistical-Physical Modeling
• Interference from a single source
Noise envelope
k pulses in a window of duration T
(k)
(j)
Tk
Pulse emission duration
(1)
(2)
τj
Pulse arrival time
t=0
Emission duration: geometrically distributed with mean μ
Pulse arrivals: homogeneous Poisson point process with rate λ
Assuming channel between interference source and receiver has flat fading
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Parametric Vs. Non-Parametric Methods
• Noise in different PLC networks has different statistical models
• Mitigation algorithms need to be robust in different noise scenarios
Parametric Methods
Non-Parametric Methods
Assume parameterized
noise statistics
Yes
No
Performance degradation
due to model mismatch
Yes
No
Training needed
Yes
No
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Non-Parametric Mitigation Using Null Tones
J : Index set of null tones
FJ : DFT sub-matrix
e: Impulsive noise in time domain
g: AWGN with unknown variance
• A compressed sensing problem
Exploiting the sparse structure of the time-domain impulsive noise
• Sparse Bayesian learning (SBL)
Proposed initially by M. L. Tipping
A Bayesian inference framework with sparsity promoting prior
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Sparse Bayesian Learning
• Bayesian inference
Sparsity promoting prior:
Likelihood:
Posterior probability:
e | g ~ CN (0, ),  = diag (g )
yJ | g , 2 ~ CN (0, FF *   2 I )
e | yJ ; g , 2 ~ CN (m, e )
• Iterative algorithm
Step 1: Maximum likelihood estimation of hyper-parameters (γ, σ2)
Solved by expectation maximization (EM) algorithm (e is latent variable)
Step 2: Estimate e from the mean of the posterior probability, go to Step 1
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Non-Parametric Mitigation Using All Tones
• Joint estimation of data and noise
J : Index set of data tones
z : Received signal in frequency domain
Treat the received signal in data tones as additional hyper-parameters
Estimate of zJ is sent to standard OFDM equalizer and symbol detector
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Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Time-Domain Interleaving
Coded performance in cyclostationary noise
Burst duty cycle 10%
Time-domain interleaving over an AC cycle
Current PLC standards use frequency-domain interleaving (FDI)
Burst duty cycle 30%
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