Low-complexity ML decoding for convolutional tail-biting codes

Hung Ta Pai*, Yungh Siang Han, Ting Yi Wu, Po-Ning Chen, Shin Lin Shieh

*此作品的通信作者

研究成果: Article同行評審

26 引文 斯高帕斯(Scopus)

摘要

Recently, a maximum-likelihood (ML) decoding algorithm with two phases has been proposed for convolutional tailbiting 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 Av algorithm has over 5 times less average decoding complexity in the second phase when Eb/N0≥ 4 dB.

原文English
頁(從 - 到)883-885
頁數3
期刊IEEE Communications Letters
12
發行號12
DOIs
出版狀態Published - 2008

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