Paper Info
Title
Joint Erasure Marking and Viterbi Decoding Algorithm for Unknown Impulsive Noise Channels
Abstract
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.
Year
DOI
Venue
2008
10.1109/TWC.2008.061129
IEEE Transactions on Wireless Communications
Keywords
Field
DocType
optimal performance,viterbi decoding method,euclidean metric based decoder,unknown impulsive noise channel,viterbi decoding algorithm,impulse noise,probability density function,maximum likelihood decoder,viterbi decoding,channel coding,in-corrupted symbol,maximum likelihood detection,channel decoding,significant performance improvement,bernoulli-gaussian impulsive noise model,erasure marking,achievable system performance,convolutionally coded data,viterbi algorithm,impulsive noise,joint erasure marking,unknown impulsive noise channels,in-corrupted signal,gaussian noise,non-gaussian impulsive noise,convolutional codes,impulsive noise statistics,electromagnetic interference,wireless communication,maximum likelihood estimation,decoding,viterbi decoder,communication system,euclidean distance,system performance,statistics,noise,atmospheric modeling
Convolutional code,Soft output Viterbi algorithm,Algorithm,Speech recognition,Viterbi decoder,Decoding methods,Iterative Viterbi decoding,Gaussian noise,Mathematics,Viterbi algorithm,Erasure
Journal
Volume
Issue
ISSN
7
9
1536-1276
Citations 
PageRank 
References 
7
0.81
17
Authors
3
Name
Order
Citations
PageRank
Tao Li1664.38
Wai Ho Mow2116193.21
Manhung Siu346461.40