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Low-Complexity ML Decoding for Convolutional Tail-Biting Codes
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DOI：10.1109/LCOMM.2008.072181.png)
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Abstract
En 中文
Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tail-biting codes [1]. 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 E-b/N-0 >= 4 dB.
Keywords:
Viterbi algorithm
maximum-likelihood
tail-biting codes
algorithm A*
Journal
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4.4
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1.3W
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