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A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes
时间:2019-04-30 22:34    点击:   所属单位:通信工程学院
讲座名称 A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes
讲座时间 2019-05-05 10:30:00
讲座地点 西电北校区新科技楼1012会议室
讲座人 韩永祥
讲座人介绍
韩永祥博士1984 年毕业于台湾清华大学电机工程学系并于1986 年于同系取得硕士学位。1993 年韩博士于纽约州雪城大学获得计算机与信息科学博士。他曾于华梵人文科技学院,暨南国际大学,以及台北大学任教。从2010年8月起,他任教于台湾科技大学电机工程系并于2011年6月起荣任学校讲座教授。目前他也是东莞理工学院杰出人才特聘教授。
韩博士是1994 年雪城大学博士论文奖得主,同时也是IEEE Fellow。2013年他的一个论文赢得了久负盛名的ACM CCS Test of Time 奖。此奖项为ACM 资讯安全领域的年度最有影响力论文奖。

讲座内容
Due to the growing interest in applying tail-biting convolutional coding techniques in real-time communication systems, fast decoding of tail-biting convolutional codes has become an important research direction. In this talk, a new maximum-likelihood (ML) decoder for tail-biting convolutional codes is proposed. It is named Bidirectional Priority-First Search Algorithm (BiPFSA) because Priority-First Search Algorithm has been used both in forward and backward directions during decoding. Simulations involving the antipodal transmission of (2, 1, 6) and (2, 1, 12) tail-biting convolutional codes over additive white Gaussian noise channels show that BiPFSA not only has the least average decoding complexity among state-of the-art decoding algorithms for tail-biting convolutional codes but can also provide a highly stable decoding complexity with respect to growing information length and code constraint length. More strikingly, at high SNR, its average decoding complexity can even approach the ideal benchmark complexity, obtained under a perfect noise-free scenario by any sequential type decoding. This demonstrates the superiority of BiPFSA in terms of decoding efficiency.
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