Efficient Soft-Decision Maximum-Likelihood Decoding of BCH Code in the GNSS
Jinhai Sun, Jinhai Li, Haiyang Liu, Feng Wang , Yuepeng Yan
(Institute of microelectronics, Chinese Academy of Sciences, Beijing 100029, China)
Abstract:
Soft-decision decoding of BCH code in the global navigation satellite system (GNSS) is investigated in order to improve the performance of traditional hard-decision decoding. Using the nice structural properties of BCH code, a soft-decision decoding scheme is proposed. It is theoretically shown that the proposed scheme exactly performs maximum-likelihood (ML) decoding, which means the decoding performance is optimal. Moreover, an efficient implementation method of the proposed scheme is designed based on Viterbi algorithm. Simulation results show that the performance of the proposed soft-decision ML decoding scheme is significantly improved compared with the traditional hard-decision decoding method at the expense of moderate complexity increase.
Key words: GNSS BCH codes soft-decision decoding maximum-likelihood (ML) decoding Viterbi algorithm
DOI：10.11916/j.issn.1005-9113.2015.01.008
Clc Number:TP391.7
Fund:
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Efficient Soft-Decision Maximum-Likelihood Decoding of BCH Code in the GNSS
本站小编 哈尔滨工业大学/2019-10-23
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