A low-complexity maximum likelihood decoder for tail-biting trellis

Author:

Wang Xiaotao,Qian Hua,Kang Kai,Xiang Weidong

Abstract

Abstract Tail-biting trellises are defined on a circular time axis and can have a smaller number of states than equivalent conventional trellises. Existing circular Viterbi algorithms (CVAs) on the tail-biting trellis are non-convergent and sub-optimal. In this study, we show that the net path metric of each tail-biting path is lower-bounded during the decoding process of the CVA. This property can be applied to remove unnecessary iterations of the CVA and results in a convergent maximum likelihood (ML) decoder. Simulation results show that the proposed algorithm exhibits higher decoding efficiency compared with other existing ML decoders.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Signal Processing

Reference10 articles.

1. Calderbank A, Forney G, Vardy A: Minimal tailbiting trellises: Golay code and more. IEEE Trans. Inf. Theory 1999, 45(5):1435-1455. 10.1109/18.771145

2. Stahl P, Anderson J, Johannesson R: Optimal and near-optimal encoders for short and moderate-length tailbiting trellises. IEEE Trans. Inf. Theory 1999, 45(7):2562-2571. 10.1109/18.796408

3. Gluesing-Luerssen H, Weaver E: Linear tail-biting trellises: characteristic generators and the BCJR-construction. IEEE Trans. Inf. Theory 2011, 57(2):738-751.

4. Wang X, Qian H, Xu J, Yang Y, Wang F: An efficient CVA-based decoding algorithm for tail-biting codes. In IEEE Global Telecommunications Conference. Houston; 5–9 Dec 2011:1-5.

5. Shankar P, Kumar P, Sasidharan K, Rajan B, Madhu A: Efficient convergent maximum likelihood decoding on tail-biting (2007). . Accessed 10 Aug 2007 http://arxiv.org/pdf/cs/0601023v1.pdf

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