November 9th, 2010 Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise Asilomar Conference on Signals, Systems, and Computers 2011 1 Marcel Nassar and Brian L. Evans Wireless Networking and Communications Group The University of Texas at Austin
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Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise
Low Complexity EM-based Decoding for OFDM Systems with Impulsive Noise. Marcel Nassar and Brian L. Evans Wireless Networking and Communications Group The University of Texas at Austin. Asilomar Conference on Signals, Systems, and Computers 2011. Wireless Transceivers. antennas. - PowerPoint PPT Presentation
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November 9th, 2010
Low Complexity EM-based Decoding for OFDM Systems with Impulsive
Noise
Asilomar Conference on Signals, Systems, and Computers 2011
Modeling the first order statistics of noise Gaussian Mixture Model Middleton Class A Symmetric Alpha Stable
Some Fitted Parameters for GM
Wireless Networking and Communications Group
4
0 1 2 3 4 5 6 7 8 910
-20
10-15
10-10
10-5
100
Threshold Amplitude (a)
Tail
Pro
babi
litie
s [P
(X >
a)]
EmpiricalMiddleton Class ASymmteric Alpha StableGaussianGaussian Mixture Model
0.75
0.25
13.7
0.89
0.11
198
0.87
0.13
140
Platform Noise
Powerline Noise
can be estimated during quiet time
Consider an OFDM communication system
Noise Model: a K-term Gaussian Mixture
Assumptions: Channel is fixed during an OFDM symbol Channel state information (CSI) at the receiver Noise is stationary Noise parameters at the receiver
System Model
Wireless Networking and Communications Group
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impulsive noisenormalized SNRDFT matrix
OFDM symbolreceived symbol circulant channel
Has a product formSymbol Decodable
Exponential in N(N in hundreds)
Problem Statement OFDM detection problem
Transformed detection problem (DFT operation)
Wireless Networking and Communications Group
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• no efficient code representation for• not symbol decodable
• for Gaussian noise, statistics are preserved
• for impulsive noise, dependency is introduced No Product FormExponential in N
Single Carrier (SC) vs. OFDM
-10 -5 0 5 10 15 2010
-6
10-5
10-4
10-3
10-2
10-1
100
SNR
SER
OFDM SystemSingle Carrier System
Wireless Networking and Communications Group
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𝜋 1=0.9 ,𝜋 2=0.1 ,𝜎21
𝜎12=100
Low SNR: SC better
High SNR: OFDM better
OFDM provides time diversity through the FFT operation
Lot of other reasons to choose OFDM
Gaussian Mixture with
SC outperforms OFDM OFDM outperforms SC
SC vs. OFDM: Intuition
Single Carrier OFDM
8
Wireless Networking and Communications Group
N modulated symbols
Impulsive Noise
Time-domain OFDM Symbol
Impulsive Noise
High Amplitude Impulse
High Amplitude Impulse
• Impulse energy concentrated in one symbol• Symbol lost
• Impulse energy spread across symbols• Loss depends on impulse amplitude and SNR