For a long time, the circularity of the tail-biting trellis is ignored in conventional decoding algorithms of Tail-Biting Convolutional Codes (TBCC). This kind of algorithm starts decoding from the fixed location, and consequently exhibits relatively lower decoding efficiency. For the first time, this paper proves that the decoding result of the tail-biting convolutional codes is independent on the decoding starting location. It means that the Maximum Likelihood (ML) tail-biting path, which starts from any location of the tail-biting trellises, is the global ML tail-biting path. Based on this observation, a new ML decoding algorithm is proposed. The new algorithm ranks the belief-value of each location on the trellis at first, and then selects the location with the highest belief- value as the decoding starting location. Compared with other existing ML decoders, the new decoder exhibits higher convergence speed.
Wang Xiao-tao,Liu Zhen-hua. Belief Ranking Based Low-complexity Maximum Likelihood Decoding Algorithm for Tail-biting Convolutional Codes[J]. JEIT, 2015, 37(7): 1575-1579.
Wang Xiao-tao, Qian Hua, Xiang Wei-dong, et al.. An efficient ML decoder for tail-biting codes based on circular trap detection[J]. IEEE Transactions on Communications, 2013, 61(4): 1212-1221.
[2]
Gluesing-Luerssen H and Forney G D. Local irreducibility of tail-biting trellises[J]. IEEE Transactions on Information Theory, 2013, 59(10): 6597-6610.
Wang Xiao-tao, Qian Hua, Xu Jing, et al.. Trap detection based decoding algorithm for tail-biting convolutional codes [J]. Journal of Electronics & Information Technology, 2011, 33(10): 2300-2305.
Wang X T, Qian H, and Kang K. Viterbi-bidirectional searching based ML decoding algorithm for tail-biting codes [J]. Journal of Electronics & Information Technology, 2013, 35(5): 1017-1022.
[5]
Wu T Y, Chen P N, Pai H T, et al.. Reliability-based decoding for convolutional tail-biting codes[C]. IEEE Vehicular Technology Conference, Taibei, 2010: 1-4.
[6]
3GPP TS. 45.003-3rd generation partnership project; technical specification group GSM/EDGE radio access network; channel coding (release 9)[S]. 2009.
[7]
3GPP TS. 36.212-3rd generation partnership project; technical specification group radio access network; evolved universal terrestrial radio access (E-UTRA); multiplexing and channel coding (release 8)[S]. 2009.
[8]
Williamson A R, Marshall M J, and Wesel R D. Reliability-output decoding of tail-biting convolutional codes [J]. IEEE Transactions on Communications, 2014, 62(6): 1768-1778.
[9]
Bin Khalid F, Masud S, and Uppal M. Design and implementation of an ML decoder for tail-biting convolutional codes[C]. IEEE International Symposium on Circuits and Systems, Beijing, 2013: 285-288.
[10]
Zhu L, Jiang M, and Wu C. An improved decoding of tail-biting convolutional codes for LTE systems[C]. 2013 International Conference on Wireless Communications & Signal Processing, Hangzhou, 2013: 1-4.
[11]
Calderbank A, Forney G Jr, and Vardy A. Minimal tail-biting trellises: the Golay code and more[J]. IEEE Transactions on Information Theory, 1999, 45(5): 1435-1455.
[12]
Wang X T, Qian H, Kang K, et al.. A low-complexity maximum likelihood decoder for tail-biting trellis[J]. EURASIP Journal on Wireless Communications and Networking, 2013, 130(1): 1-11.
[13]
Shao R Y, Lin S, and Fossorier M P C. Two decoding algorithms for tailbiting codes[J]. IEEE Transactions on Communications, 2003, 51(10): 1658-1665.
[14]
Pai H T, Han Y, Wu T, et al.. Low-complexity ML decoding for convolutional tail-biting codes[J]. IEEE Communications Letters, 2008, 12(12): 883-885.
[15]
Bocharova I, Johannesson R, Kudryashov B, et al.. BEAST decoding for block codes[J]. European Transactions on Telecommunications, 2004, 15(1): 297-305.