0. Smart Grid Communications
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)
Austin, Texas USA

1. Outline Smart power grids Powerline noise Receiver design Testbeds
Types
Modeling
Receiver design
Testbeds
Conclusion
ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN
IEEE Signal Processing Magazine Special Issue on Signal Processing Techniques for the Smart Grid, September 2012.

2. Smart Grid Wind farm Central power plant HV-MV Transformer
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Smart Grid
Wind farm
Central power plant
HV-MV Transformer
Grid status monitoring
Utility control center
Smart meters
Integrating distributed energy resources
Houses
Offices
Traditional power grids have been coupled with communication networks today, leading to so-called smart grids. A smart grid enables information flow among various components of the grid, ranging from power plants to distributed energy resources, and from local utilities to residential and commercial customers. The purpose is to better monitor and control power generation and consumption. For example, distributed energy resources, such as solar power, fuel power and other personally-owned energy storage, can be integrated to the grid to provide low-cost or standby energy during peak hours. Transducers deployed over the grids are used to collect measurement data for grid status estimation and outage detection. Smart meters can be used for time-dependent pricing, which motivates customers to scale back their energy usage during peak hours, and also device-specific billing, so that the house owner doesn’t have be pay electric bills for charging his friend’s electric vehicle. In addition, smart buildings and smart homes can be energy efficient with automated lights, air conditioners and other smart appliances.
Device-specific billing
Automated control for smart appliances
Medium Voltage (MV) 1 kV – 33 kV three phase
High Voltage (HV) 33 kV – 765 kV three phase
Industrial plant

3. Smart Grid Goals Improve asset utilization and operating efficiencies
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
Disconnect/reconnect remotely
Notify outage/restoration event
Enable informed customer participation
75M smart meters sold in 2011
EU goal of 80% smart meter deployments by 2020
Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

4. Smart Meter Communications
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
MV-LV transformer
Smart meters
Data concentrator
Smart meter communications
between smart meters and data concentrator via powerline or wireless links
On the distribution side of the grids, the communications between a local utility control center and its customers plays an important role in local utility applications that I just mentioned. The local utility communicates with a number of data concentrators located at the medium-voltage lines in the US via communication backhaul. Each data concentrator is in charge of a few houses and can talk to them via the last mile communications to the smart meters. The smart meter serves as a gateway in the home area sensor networks that interconnect transducers on appliances and indoor power lines.
Distances from Smart Meter to Data Concentrator: m in Europe/China/India and 3-4 km in US 3-4
Low voltage (LV) under 1 kV single phase
Home area data networks
connect appliances, EV charger and smart meter via powerline or wireless links

5. Wireless Smart Meter Communications
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Wireless Smart Meter Communications
Use orthogonal frequency division multiplexing (OFDM)
Communication challenges
Channel distortion
Non-Gaussian noise/interference in unlicensed bands
Category
Band
Bit Rates
Coverage
Enables
Standards
Meter to customer
2.4 GHz
Up to 250 kbps
100m
Customer participation
802.11b/g
(ZigBee)
Meter to concentrator
900 MHz
Up to
250 kbps
1000m
Smart meter communication
802.11ah (draft) g
Concentrator to utility
Up to 800 kbps
IEEE g will likely initially use frequency shift keying (FSK)

6. Powerline Communications (PLC) for Smart Meters
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Powerline Communications (PLC) for Smart Meters
Use orthogonal frequency division multiplexing (OFDM)
Communication challenges
Channel distortion
Non-Gaussian noise/interference
Category
Band
Bit Rates
Coverage
Enables
Standards
Broadband
MHz
Up to 200 Mbps
<1500 m
Home area data networks
HomePlug
ITU-T G.996x
IEEE P1901
Narrowband
3-500 kHz
Up to 800 kbps
Multi-kilometer
Smart meter communication
PRIME, G3
ITU-T G.990x
IEEE P1901.2
The smart grid communications are supported by a heterogeneous set of network technologies, ranging from wireless to wireline solutions. Among the wireline alternatives, powerline communications, or PLC, have been deployed outdoor for last mile communications and indoor for home area networks. Narrowband PLC operating in the kHz band to deliver a few hundred kbps has been used for last mile communications over MV and LV lines. For home area networks, broadband PLC can provide several hundred Mbps in the MHz band. These PLC systems adopt multicarrier communications, or OFDM. (Introduce OFDM here)

7. Narrowband PLC Transceiver Design
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)

8. Types of Powerline Noise
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Types of Powerline Noise
Background Noise
Cyclostationary Impulsive Noise
Asynchronous Impulsive Noise
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
time

9. Periodic Noise from DC-DC Buck Converter
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

10. Periodic Noise from DC-DC Buck Converter
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
Note: DC value has been filtered out
Impulsive noise at switching transients
Zoom in to see 16.9 kHz component

11. Cyclostationary Impulsive Noise
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise
Medium Voltage Site
Low Voltage Site
Period is one half of AC cycle
Segment:
Segment:
Field measurements collected jointly with Aclara and Texas Instruments near St. Louis, Missouri USA

12. Cyclostationary Impulsive Noise Modeling
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise Modeling
Measurement data from UT/TI field trial
Cyclostationary Gaussian Model [Katayama06]
Proposed model uses three filters [Nassar12]
Demux
Period is one half of an AC cycle
s[k] is zero-mean Gaussian noise
Adopted by IEEE P narrowband PLC standard

13. Asynchronous Impulsive Noise Modeling
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Asynchronous Impulsive Noise Modeling
Additive interference from multiple sources
Assume source emissions are modeled by Poisson distribution
Attenuation g(d) = exp(-a(f) d) where d is distance
Interference from source i
Homogeneous network
Ex. Semi-urban areas, apartment complexes
Middleton class A
li = l, mi = m
General (heterogeneous) network
Ex. Dense urban and commercial settings
Gaussian mixture model
li, mi
Middleton Class A is a special case of Gaussian mixture model

14. Asynchronous Noise Model Fitting
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

15. OFDM Systems in Impulsive Noise
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

16. Mitigating Impulsive Noise in OFDM Systems
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Mitigating Impulsive Noise in OFDM Systems
A linear system with Gaussian disturbance
Estimate the impulsive noise and remove it from the received signal
Then apply standard OFDM decoder as if only AWGN were present

17. Proposed Non-Parametric Receiver Methods
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Proposed Non-Parametric Receiver Methods
Exploit sparsity of impulsive noise in time domain
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 , 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
time
System
Noise
SBL w/ null tones
SBL w/ all tones
SBL w/ decision feedback
Uncoded
GMM
8 dB
10 dB -
MCA
6 dB
7 dB
Coded
2 dB
9 dB
1.75 dB
6.75 dB
8.75 dB
Every SNR gain of 3 dB could mean +1 bit/tone

18. Time Domain Interleaving
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Time Domain Interleaving
Bursts span consecutive OFDM symbols
Coded performance in cyclostationary noise
Interleave
Complex OFDM, 128-point FFT, QPSK, data tones , rate ½ conv. Code
Burst duty cycle of 30%
Bursts spread over many OFDM symbols
PLC standards use frequency-domain interleaving

19. Testbed #1: Built on Previous DSL Testbed
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
NI cards generates/receives analog signals
TI front end couples to power line
Transceiver algorithms in C on x86
Desktop LabVIEW configures system and visualizes results
1x1 Testbed

20. Testbed #2: Noise Playback/Analysis
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 powerline noise in field
Collected 16k 16-bit 400 kS/s at each location
Freescale PLC Testbed 20

21. Testbed #2: Cyclic Power Line Noise
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Testbed #2: Cyclic Power Line Noise
Analyzed cyclic properties of PLC noise measurements
Developed cyclic bit loading method for transmitter
Receiver measures noise power over half AC cycle
Feedback modulation map to transmitter
Allocate more bits in higher SNR subchannels
2x increase in bit rate
Won ISPLC 2013 Best Paper Award 21

22. Testbed #3: FPGA Implementation
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
Utilization
Trans.
Rec.
AMP+Eq
FPGA 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%
Received QPSK constellation at equalizer output
conventional receiver
with AMP 22

23. Conclusion Powerline communication systems are interference limited
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:

24. 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 , 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 can- cellation 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.

25. 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,
[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 kHz Band: Channel Impairments, Noise, and Standards”, IEEE Signal Processing Magazine, vol. 29, no. 5, pp , Sep
[Nieman13] K. Nieman, J. Lin, M. Nassar, K. Waheed and B. L. Evans, “Cyclic Spectral Analysis of Power Line Noise in the kHz Band”, Proc. IEEE Int. Sym. on Power Line Communications and Its Applications, Mar , 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.

26. Backup Slides

27. 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

28. Completed Projects System Contribution SW release Prototype Funding
20 PhD and 9 MS alumni
System
Contribution
SW release
Prototype
Funding
ADSL
equalization
Matlab
DSP/C
Freescale, TI
2x2 testbed
LabVIEW
LabVIEW/PXI
Oil&Gas
Wimax/LTE
resource alloc.
Underwater comm.
space-time comm. large rec. arrays
Lake Travis testbed
UT Applied Res. Labs
Camera
image acquisition
Intel, Ricoh
Display
image halftoning C
HP, Xerox
video halftoning
Qualcomm
Elec. design automation
fixed point conv.
FPGA
Intel, NI
distributed comp.
Linux/C++
Navy sonar
Navy, NI
DSP: Digital Signal Processor PXI: PCI Extensions for Inst.

29. Current Projects System Contributions SW release Prototype Funding
9 PhD students
System
Contributions
SW release
Prototype
Funding
Powerline comm.
interference reduction testbeds
LabVIEW
Freescale, TI modems
Freescale, IBM, TI
Wi-Fi
interference reduction
Matlab
NI FPGA
Intel, NI
time-based analog-to-digital converter
IBM 45nm TSMC 180nm
Cellular (LTE)
cloud radio access net. baseband compression
Huawei
Handheld camera
reducing rolling shutter artifacts
Android TI
EDA
reliability patterns NI

30. 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

31. A Smart Grid Source: ETSI Integrating alternative energy sources
Communication to isolated area
Power generation optimization
Load balancing
Disturbance
monitoring
Smart metering
Electric car charging & smart billing
Source: ETSI

32. Power Lines Built for unidirectional energy flow
Bidirectional information flow throughout smart grid will occur
High Voltage (HV) 33 kV – 765 kV
Medium Voltage (MV) 1 kV – 33 kV
Low Voltage (LV) under 1 kV
Transformer
Source: ERDF

33. 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

34. 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

35. 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

36. Sources of Powerline Noise
Uncoordinated transmission
Power line disturbance
Taken from a local utility point of view
Electronic devices

37. PLC In Different Frequency Bands
Category
Band
Bit Rate
Applications
Standards
Ultra Narrowband
0.3 – 3 kHz
~100 bps
Automatic meter reading
Outage detection
Load control
N/A
Narrowband
3 – 500 kHz
~500 kbps
Smart metering
Real-time energy management
PRIME, G3
ITU-T G.hnem
IEEE P1901.2
Broadband
1.8 – 250 MHz
~200 Mbps
Home area networks
HomePlug
ITU-T G.hn
IEEE P1901
All of the above standards are based on multicarrier communications using orthogonal frequency division multiplexing (OFDM).

38. Physical Layer Parameters for OFDM Narrowband PLC Standards
CENELEC A band is from 3 to 95 kHz. FCC band is from to kHz. PRIME and G3 use real-valued baseband OFDM. Others are complex-valued.

39. Comparison Between Wireless and PLC Systems
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Comparison Between Wireless and PLC Systems
Wireless Communications
Narrowband PLC (3-500 kHz)
Time selectivity
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
Asynchronous interference
Uncoordinated users in Wi-Fi bands; Frequency reuse in cellular
Due to power electronics and uncoordinated users using other standards
MIMO
Standardized for Wi-Fi and cellular
Number of wires minus 1; G.9964 standard for broadband PLC

40. Cyclostationary Impulsive Noise
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Cyclostationary Impulsive Noise
Linear periodically time-varying system model
Period (half of the AC cycle) is partitioned into M segments
Noise within each segment is stationary …
Hi - Linear time invariant filter
N - Period in samples
Segment:

41. Asynchronous Impulsive Noise Modeling
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

42. Two Asynchronous Impulsive Noise Models
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

43. 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

44. Narrowband PLC Systems
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)
Solution #2: Joint transmitter-receiver design
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

45. Parametric vs. Nonparametric Noise Mitigation
Must build a statistical model of the noise
Yes No
Requires training data to compute model parameters
Degrades in performance due to model mismatch
Has high complexity when receiving message data
Prior receiver methods to cope with non-Gaussian noise can be categorized as parametric and nonparametric approaches. Parametric methods assumes a statistical noise model, estimates model parameters during a training stage, and use that to mitigate noise during data transmissions. These methods generally allow low-complexity implementations during the transmission stage. However, in time-varying noise statistics, re-training is needed which introduces extra overhead. Otherwise performance degradation can be expected due to model mismatch. Nonparametric methods, on the other hand, require no additional training since they don’t assume any noise models. Instead they denoise the received signal by exploiting certain sparsity structure of the noise. These methods are more robust in various noise environments since it doesn’t rely on statistical models but the sparse structure of the noise. However, the denoising algorithms are generally of high computational complexity.

46. 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 , 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 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.

47. 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.

48. Statistical-Physical Modeling
Interference from a single source
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
k pulses in a window of duration T
(k)
(j)
(2)
(1)
Noise envelope Tk
Pulse emission duration τj
Pulse arrival time
t=0

49. 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
Training needed

50. Non-Parametric Mitigation Using Null Tones
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
J : Index set of null tones
FJ : DFT sub-matrix
e: Impulsive noise in time domain
g: AWGN with unknown variance

51. Sparse Bayesian Learning
Bayesian inference
Sparsity promoting prior:
Likelihood:
Posterior probability:
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

52. Non-Parametric Mitigation Using All Tones
Joint estimation of data and noise
Treat the received signal in data tones as additional hyper-parameters
Estimate of is sent to standard OFDM equalizer and symbol detector
: Index set of data tones
z : Received signal in frequency domain

53. Time-Domain Interleaving
Smart grids – Powerline noise – Receiver design – Testbeds – Conclusion
Time-Domain Interleaving
Coded performance in cyclostationary noise
Burst duty cycle 10%
Burst duty cycle 30%
Time-domain interleaving over an AC cycle
Current PLC standards use frequency-domain interleaving (FDI)