Low-complexity ML decoding for convolutional tail-biting codes
| Autor: | Shin-Lin Shieh, Yunghsiang S. Han, Ting-Yi Wu, Po-Ning Chen, Hung-Ta Pai |
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| Rok vydání: | 2008 |
| Předmět: |
Iterative Viterbi decoding
Berlekamp–Welch algorithm List decoding Data_CODINGANDINFORMATIONTHEORY Serial concatenated convolutional codes Sequential decoding Viterbi algorithm Computer Science Applications symbols.namesake Viterbi decoder Convolutional code Modeling and Simulation symbols Electrical and Electronic Engineering Algorithm Computer Science::Information Theory Mathematics |
| Zdroj: | IEEE Communications Letters. 12:883-885 |
| ISSN: | 2373-7891 1089-7798 |
| Popis: | Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting codes. The first phase applies the Viterbi algorithm to obtain the trellis information, and then the second phase employs the algorithm A* to find the ML solution. In this work, we improve the complexity of the algorithm A* by using a new evaluation function. Simulations showed that the improved A* algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0ges 4 dB. |
| Databáze: | OpenAIRE |
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