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Error-trellises for convolutional codes-part II: Decoding methods
Meir Ariel
*
,
Jakov Snyders
*
Corresponding author for this work
School of Electrical Engineering
Department of Electrical Engineering - Systems
Research output
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Article
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peer-review
8
Scopus citations
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Keyphrases
Trellis
100%
Convolutional Codes
100%
Decoding Method
100%
Computational Complexity
50%
Low-to-moderate
25%
Search Methods
25%
Noise Level
25%
Codeword
25%
Soft Decision
25%
Channel Noise
25%
Maximum Likelihood Decoding
25%
Maximum Likelihood Decoder
25%
Error Sequence
25%
Viterbi Decoder
25%
Syndrome-based
25%
Weighing Error
25%
Syndrome Decoder
25%
Computational Gain
25%
Engineering
Maximum Likelihood
100%
Convolutional Code
100%
Decoding Method
100%
Noise Level
50%
Computational Complexity
50%
Average Computational Complexity
50%
Likelihood Decoding
50%
Mathematics
Maximum Likelihood
100%
Worst Case
100%
Convolutional Code
100%
Medicine and Dentistry
Maximum Likelihood Method
100%