基于可信位置排序的咬尾卷積碼譯碼算法

王曉濤; 劉振華

doi:10.11999/JEIT141459

基于可信位置排序的咬尾卷積碼譯碼算法

doi: 10.11999/JEIT141459

王曉濤¹,
劉振華¹

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- 被引次數(shù): 0
出版歷程
- 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: 咬尾卷積碼的傳統(tǒng)譯碼算法沒有考慮咬尾格形圖的循環(huán)性，譯碼起始位置固定，譯碼效率相對較低。該文首次證明了咬尾卷積碼基于格形圖的譯碼算法與譯碼起始位置無關(guān)，即從任意位置開始譯碼得到的最優(yōu)咬尾路徑即為全局最優(yōu)咬尾路徑?；诖颂岢鲆环N基于可信位置排序的咬尾卷積碼譯碼算法。新算法利用咬尾格形圖的循環(huán)性，根據(jù)接收到的信道輸出序列估算每個譯碼起始位置的可靠性，從而選擇一個可靠性最高的譯碼起始位置。和傳統(tǒng)譯碼算法相比，所提算法具有更快的收斂速度。
- 咬尾碼 /
- 咬尾格形圖 /
- 循環(huán)性 /
- 最大似然譯碼
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

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參考文獻(xiàn)(17)

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