Joint Erasure Marking and Viterbi Decoding Algorithm for Unknown Impulsive Noise Channels

In many real-world communication systems, the extent of non-Gaussian impulsive noise (IN) rather than Gaussian noise poses practical limits on the achievable system performance. The decoding of IN-corrupted signals is complicated by the fact that accurate IN statistics are typically unavailable at t...

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Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 7; no. 9; pp. 3407 - 3416
Main Authors: Tao Li, Tao Li, Wai Ho Mow, Wai Ho Mow, Manhung Siu, Manhung Siu
Format: Journal Article
Language:English
Published: Piscataway, NJ IEEE 01.09.2008
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Atmospheric modeling
channel decoding
Channels
Coding, codes
Communication systems
Decoders
Electromagnetic interference
erasure marking
Euclidean distance
Exact sciences and technology
Gaussian noise
Impulsive noise
Information, signal and communications theory
Mathematical models
Maximum likelihood decoding
Maximum likelihood estimation
Noise
Signal and communications theory
Statistics
Studies
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
Viterbi algorithm
Viterbi decoding
Wireless communication
Working environment noise
Performance evaluation
Convolutional code
Bernoulli scheme
Non gaussian noise
Data transmission
Impulsive noise
channel decoding
Implementation
Maximum likelihood decoding
Statistical method
Viterbi code
Pulse noise
erasure marking
Gaussian process
Viterbi algorithm
Gaussian noise
Telecommunication system
Probability density function
Metric
Viterbi decoding
ISSN:1536-1276, 1558-2248
Online Access:Get full text
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Summary:In many real-world communication systems, the extent of non-Gaussian impulsive noise (IN) rather than Gaussian noise poses practical limits on the achievable system performance. The decoding of IN-corrupted signals is complicated by the fact that accurate IN statistics are typically unavailable at the receiver. Without exploiting the IN statistics, the conventional method is to try to mark the IN-corrupted symbols as erasures preceding a Euclidean metric based decoder. In this work, a novel joint erasure marking and Viterbi algorithm (JEVA) is proposed to decode the convolutionally coded data transmitted over an unknown impulsive noise channel. Based on the Bernoulli-Gaussian IN model, it is empirically demonstrated that JEVA not only can offer significant performance improvement over the conventional separate erasure marking and Viterbi decoding method, but also can almost achieve the optimal performance of the maximum likelihood decoder that fully exploits the perfect knowledge of the IN probability density function. Various implementations of JEVA are proposed to provide different performance-complexity trade-offs.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2008.061129