Browsing by Author "Pai, Hung-Ta"

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Priority-First Search Decoding for Convolutional Tail-biting Codes
(2008)
Han, Yunghsiang S.
;
Wu, Ting-Yi
;
Pai, Hung-Ta
;
Chen, Po-Ning
;
Shieh, Shin-Lin
Due to rapid interest on the applications of convolutional tail-biting to communication systems, several suboptimal algorithms have been proposed to achieve near-optimal Word error rate (WER) performances with circular Viterbi decoding approach. Among them, the wrap-around Viterbi algorithm (WAVA) proposed in [1] is the one with least decoding complexity. Very recently, a maximum likelihood (ML) decoding algorithm has been proposed in [2]. The scheme has two phases. The Viterbi algorithm is applied to the trellis of the convolutional tail-biting code and the information obtained in the first phase is used by algorithm A*, which is performed to all subtrellises, in the second phase. In this work, a new two-phase ML decoding algorithm is proposed. From the simulation results for the. (2,1,12) convolutional tail-biting code, the proposed algorithm has 16 times less average decoding complexity in the second phase when compared to the one using algorithm A* and 15123 times less than that of the WAVA., respectively, when SNRb = 4 dB.
Reliability-Based Decoding for Convolutional Tail-biting Codes
(2010)
Wu, Ting-Yi
;
Chen, Po-Ning
;
Pai, Hung-Ta
;
Han, Yunghsiang S.
;
Shieh, Shin-Lin
In this work, we proposed a reliability-based enhancement for the decoding of convolutional tail-biting codes (CTBC) from the observations that the decoding does not have to start from the beginning of the received vector, and that the reliability of the received vector can be used to determine a good starting position of the decoding process. Simulations show that our reliability-based enhancement can be used together with existing decoding algorithms of the CTBC to improve either their error rate or complexity.
The modified wrap-around Viterbi algorithm for convolutional tail-biting codes
(2012-06-01)
Han, Yunghsiang S.
;
Wu, Ting-Yi
;
Pai, Hung-Ta
;
Chen, Po-Ning
;
電機工程學系
Recently, two near-optimal decoding algorithms [Shao, R.Y., Lin, S., and Fossorier, M.P.C., 2003. Two decoding algorithms for tailbiting codes. IEEE transactions on communications, 51(10), 1658-1665; Krishnan, K.M. and Shankar, P., 2006. Approximate linear time ML decoding on tail-biting trellises in two rounds. In IEEE international symposium on information theory, Seattle, WA, USA, pp. 2245-2249] have been proposed for convolutional tail-biting codes. Both algorithms iterate the Viterbi algorithm twice, but use different metrics in the second iteration. Simulations showed that the latter algorithm (Krishnan and Shankar 2006) improved on the earlier one (Shao et al. 2003) in word error rates at the price of additional storage consumption. In this work, we prove that with a proper modification to the earlier one, the two algorithms can be made to have exactly the same survivor path at each state in the trellis, and hence are equivalent in error performance. One can consequently adopt the modified algorithm to alleviate the need for extra storage consumption of the later algorithm and, at the same time, achieve equally good performance.