基于可信位置排序的咬尾卷积码译码算法

王晓涛; 刘振华

doi:10.11999/JEIT141459

基于可信位置排序的咬尾卷积码译码算法

doi: 10.11999/JEIT141459

王晓涛¹,
刘振华¹

计量
- 文章访问数: 1283
- HTML全文浏览量: 93
- PDF下载量: 675
- 被引次数: 0
出版历程
- 收稿日期: 2014-11-20
- 修回日期: 2015-02-28
- 刊出日期: 2015-07-19

Belief Ranking Based Low-complexity Maximum Likelihood Decoding Algorithm for Tail-biting Convolutional Codes

Wang Xiao-tao¹,
Liu Zhen-hua¹

摘要

摘要: 咬尾卷积码的传统译码算法没有考虑咬尾格形图的循环性，译码起始位置固定，译码效率相对较低。该文首次证明了咬尾卷积码基于格形图的译码算法与译码起始位置无关，即从任意位置开始译码得到的最优咬尾路径即为全局最优咬尾路径。基于此提出一种基于可信位置排序的咬尾卷积码译码算法。新算法利用咬尾格形图的循环性，根据接收到的信道输出序列估算每个译码起始位置的可靠性，从而选择一个可靠性最高的译码起始位置。和传统译码算法相比，所提算法具有更快的收敛速度。
- 咬尾码 /
- 咬尾格形图 /
- 循环性 /
- 最大似然译码
Abstract: 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.
- Tail-biting codes /
- Tail-biting trellis /
- Circularity /
- Maximum Likelihood (ML) decoding

HTML全文

参考文献(17)

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.

Gluesing-Luerssen H and Forney G D. Local irreducibility of tail-biting trellises[J]. IEEE Transactions on Information Theory, 2013, 59(10): 6597-6610.

王晓涛, 钱骅, 徐景, 等. 基于陷阱检测的咬尾卷积码译码算法[J]. 电子与信息学报, 2011, 33(10): 2300-2305.

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.

王晓涛, 钱骅, 康凯. 基于Viterbi-双向搜索的咬尾码最大似然译码算法[J]. 电子与信息学报, 2013, 35(5): 1017-1022.

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.

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.

3GPP TS. 45.003-3rd generation partnership project; technical specification group GSM/EDGE radio access network; channel coding (release 9)[S]. 2009.

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.

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.

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.

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.

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.

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.

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.

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.

Bocharova I, Johannesson R, Kudryashov B, et al.. BEAST decoding for block codes[J]. European Transactions on Telecommunications, 2004, 15(1): 297-305.

施引文献

资源附件(0)

访问统计