Non-Parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications
description
Transcript of Non-Parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications
Non-Parametric Mitigation of Periodic Impulsive Noise in
Narrowband Powerline Communications
Jing Lin and Brian L. EvansDepartment of Electrical and Computer
EngineeringThe University of Texas at Austin
Dec. 11, 2013
2
PLC for Local Utility Smart Grid Applications
Local utility
Transformer
Smart meters
Data concentrator
Broadband PLC:• 1.8 – 250 MHz• 200 Mbps• Home area networks
Narrowband (NB) PLC:• 3 – 500 kHz band• ~500 kbps using OFDM• Communication between smart
meters and data concentrators
Communication backhaul
LV (<1kV)
MV (1kV – 72.5kV))
3
Periodic Impulsive Noise in NB PLC
• Dominant noise component in 3 – 500 kHz band
Noise bursts arriving periodically – twice
per AC cycle
Noise measurements collected at an outdoor LV site [Nassar12]
Noise power spectral density
raised by 30 – 50 dB during bursts
4
Periodic Impulsive Noise in NB PLC
• Noise sources
o Switching mode power supplies generate harmonic contents that cannot be
perfectly removed by analog filtering
o Examples: inverters, DC-DC converters
• Causes severe performance degradation
o Commercial PLC modems feature low power transmission
o Average SNR at receiver is between -5 and 5 dB
o Conventional receiver designs assuming AWGN become sub-optimal
5
Prior Work
• Transmitter methods
• Receiver methods
Methods Data Rate Reduction
RX-TX Feedback
Performance Improvement
Concatenated coding [G3] Yes No Moderate
Time-domain interleaving [Dweik10] No No Low
Cyclic waterfilling [Nieman13] No Yes High
Methods Training Overhead
RX Complexity
Performance Improvement
MMSE equalizer [Yoo08] High Moderate Moderate
Whitening filter [Lin12] High Low Low
6
Our Approach
• Non-parametric methods to mitigate periodic impulsive noise
o No assumption on statistical noise models & No training overhead
o Impulsive noise estimation exploiting its sparsity in the time domain
o Consider a time-domain block interleaving (TDI) OFDM system
7
Time-Domain Block Interleaving
• After the de-interleaver at the receiver
o An OFDM symbol observes a sparse noise vector in time domaino Interleaver size and burst duration determine the sparsityo Typical burst duration: 10% - 30% of a periodo Interleaver size: one or more periods
A noise burst spans multiple OFDM symbols spread into short impulses
Interleave
8
Impulsive Noise Estimation
• A compressed sensing problem [Caire08, Lin11]
o Observe noise in null tones of received signal
o Estimate time-domain noise exploiting its sparsity
- Sub-DFT matrix
- Indices of null tones
- Impulsive noise after de-interleaving
- AWGN
9
Sparse Bayesian Learning (SBL)
• A Bayesian learning approach for compressed sensing [Tipping01]
o Prior on promotes sparsity
o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters
o MAP estimate of
Shape Scale
10
Exploiting More Information
• SBL performance is limited by the number of measurements
o Null tones occupy 40 – 50% of the transmission band in PLC standards
• A heuristic exploiting information on all tones
o Iteratively estimate impulsive noise and transmitted data
o Disadvantage: sensitive to initial value of
INestimator ++ -
Zero out null tones
-
11
Exploiting More Information (cont.)
• Decision feedback estimation
o Use to update hyperparameters
12
Simulation Settings
• Baseband complex OFDM system
• Periodic impulsive noise synthesized using a linear periodically time varying model in the IEEE P1901.2 standard [Nassar12]
Parameters ValuesFFT Size 128
Modulation QPSK# of tones 128Data tones # 33 - # 104
Interleaver size ~ 2 periods of noiseForward Error Correction
Code Rate-1/2 Convolutional
13
Coded Bit Error Rate (BER) Performance
Burst duration = 10% Burst duration = 30%
7.5 dB 7 dB
14
Conclusion
• Non-parametric receiver methods to mitigate periodic impulsive noise in NB PLC
o Do not assume statistical noise models, and do not need trainingo Work in time-domain block interleaving OFDM systemso Exploit the sparsity of the noise in the time domaino Estimate the noise samples from various subcarriers of the received signal and
from decision feedback
• Future work
o Complexity reductiono Joint transmitter and receiver optimization
15
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.
• [Dweik10] A. Al-Dweik, A. Hazmi, B. Sharif, and C. Tsimenidis, “Efficient interleaving technique for OFDM system over impulsive noise channels,” in Proc. IEEE Int. Symp. Pers. Indoor and Mobile Radio Comm., 2010.
• [Nieman13] K. F. Nieman, J. Lin, M. Nassar, K. Waheed, and B. L. Evans, “Cyclic spectral analysis of power line noise in the 3-200 khz band,” in Proc. IEEE Int. Symp. Power Line Commun. and Appl., 2013.
• [Yoo08] Y. Yoo and J. Cho, “Asymptotic analysis of CP-SC-FDE and UW-SC-FDE in additive cyclostationary noise,” Proc. IEEE Int. Conf. Commun., pp. 1410–1414, 2008.
• [Lin12] J. Lin and B. Evans, “Cyclostationary noise mitigation in narrowband powerline communications,” Proc. APSIPA Annual Summit Conf., 2012.
• [Caire08] G.Caire, T. Al-Naffouri, and A. Narayanan, “Impulse noise cancellation in OFDM: an application of compressed sensing,” in Proc. IEEE Int. Symp. Inf. Theory, 2008, pp. 1293–1297.
• [Lin11] J. Lin, M. Nassar, and B. L. Evans, “Non-parametric impulsive noise mitigation in OFDM systems using sparse Bayesian learning,” Proc. IEEE Global Comm. Conf., 2011.
• [Tipping01] M. Tipping, “Sparse Bayesian learning and the relevance vector machine,” J. Mach. Learn. Res., vol. 1, pp. 211–244, 2001.
16
Thank you
17
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 Last mile comm.
• PRIME, G3• ITU-T G.hnem• IEEE P1901.2
Broadband(BB)
1.8-250 MHz ~200M <1500 m Home area
networks• HomePlug• ITU-T G.hn• IEEE P1901
18
Sparse Bayesian Learning (SBL)
• A Bayesian learning approach for compressed sensing [Tipping01]
o Prior on promotes sparsity
o ML estimation by expectation maximization (EM) - Latent variables - Hyper-parameters
o MAP estimate of
Degrees of freedom Scale
Shape Scale