NASA Technical Reports Server (NTRS) 19990014065: Trellises And Trellis-Based Decoding Algorithms For Linear Block Codes. Part 3; An Iterative Decoding Algorithm For Linear Block Codes Based On A Low-Weight Trellis Search - Info and Reading Options
By NASA Technical Reports Server (NTRS)
"NASA Technical Reports Server (NTRS) 19990014065: Trellises And Trellis-Based Decoding Algorithms For Linear Block Codes. Part 3; An Iterative Decoding Algorithm For Linear Block Codes Based On A Low-Weight Trellis Search" and the language of the book is English.
“NASA Technical Reports Server (NTRS) 19990014065: Trellises And Trellis-Based Decoding Algorithms For Linear Block Codes. Part 3; An Iterative Decoding Algorithm For Linear Block Codes Based On A Low-Weight Trellis Search” Metadata:
- Title: ➤ NASA Technical Reports Server (NTRS) 19990014065: Trellises And Trellis-Based Decoding Algorithms For Linear Block Codes. Part 3; An Iterative Decoding Algorithm For Linear Block Codes Based On A Low-Weight Trellis Search
- Author: ➤ NASA Technical Reports Server (NTRS)
- Language: English
Edition Identifiers:
- Internet Archive ID: NASA_NTRS_Archive_19990014065
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"NASA Technical Reports Server (NTRS) 19990014065: Trellises And Trellis-Based Decoding Algorithms For Linear Block Codes. Part 3; An Iterative Decoding Algorithm For Linear Block Codes Based On A Low-Weight Trellis Search" Description:
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For long linear block codes, maximum likelihood decoding based on full code trellises would be very hard to implement if not impossible. In this case, we may wish to trade error performance for the reduction in decoding complexity. Sub-optimum soft-decision decoding of a linear block code based on a low-weight sub-trellis can be devised to provide an effective trade-off between error performance and decoding complexity. This chapter presents such a suboptimal decoding algorithm for linear block codes. This decoding algorithm is iterative in nature and based on an optimality test. It has the following important features: (1) a simple method to generate a sequence of candidate code-words, one at a time, for test; (2) a sufficient condition for testing a candidate code-word for optimality; and (3) a low-weight sub-trellis search for finding the most likely (ML) code-word.
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