基于變量節(jié)點(diǎn)更新的交替方向乘子法 LDPC懲罰譯碼算法

賀文武; 夏巧橋; 鄒煉

doi:10.11999/JEIT170358

基于變量節(jié)點(diǎn)更新的交替方向乘子法 LDPC懲罰譯碼算法

doi: 10.11999/JEIT170358

1.
(武漢大學(xué)電子信息學(xué)院武漢 430072)
2.
(華中師范大學(xué)物理科學(xué)與技術(shù)學(xué)院武漢 430079)

基金項(xiàng)目:

國家自然科學(xué)基金(61501334)，華中師范大學(xué)中央高?；究蒲袠I(yè)務(wù)費(fèi)(CCNU16A05028)

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- 文章訪問數(shù): 1573
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-20
- 修回日期: 2017-10-20
- 刊出日期: 2018-01-19

Alternating Direction Method of Multipliers LDPC Penalized Decoding Algorithm Based on Variable Node Update

1.
(School of Electronic Information, Wuhan University, Wuhan 430072, China)
2.
(College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China)

Funds:

The National Natural Science Foundation of China (61501334), The Fundamental Research Funds for the Central Universities of CCNU (CCNU16A05028)

摘要

摘要: 基于交替方向乘子法(ADMM)的改進(jìn)懲罰函數(shù)LDPC譯碼算法能夠提升譯碼性能，但其所需優(yōu)化參數(shù)過多且性能提升有限。針對(duì)該問題，將該算法與其它帶有懲罰函數(shù)的譯碼算法比較后發(fā)現(xiàn)，兩者的不同之處僅在于譯碼算法中變量節(jié)點(diǎn)的更新規(guī)則不同。因此，該文通過構(gòu)造一種新的變量節(jié)點(diǎn)的更新方法去減少優(yōu)化參數(shù)數(shù)目并提升譯碼性能。實(shí)驗(yàn)仿真表明，相較于原有算法，該文所提算法有效減少了所需優(yōu)化的參數(shù)數(shù)目，此外，所提算法的平均迭代次數(shù)更少且能實(shí)現(xiàn)約0.1 dB的性能提升。
- LDPC /
- 交替方向乘子法 /
- 懲罰函數(shù) /
- 變量節(jié)點(diǎn)更新
Abstract: The LDPC decoding algorithm with improved penalty function can improve the performance of decoding algorithm based on Alternating Direction Method of Multipliers (ADMM), but it has too many parameters to be optimized and the performance improvement is limited. For this problem, by comparing it with other decoding algorithms with penalty function, it is found that the difference between them is only the update rules of variable nodes in the decoding algorithm. Therefore, a new update method for variable nodes is proposed in this paper to reduce the number of parameters and improve the decoding performance. The simulation results show that, compared with the original decoding algorithm, the decoding algorithm in this paper reduces the parameters which need to be optimized, in addition, the average number of iterations of the algorithm is less and the algorithm can achieve about 0.1 dB performance improvement.
- LDPC /
- Alternating Direction Method of Multipliers (ADMM) /
- Penalty function /
- Variable node update

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參考文獻(xiàn)(17)

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