空間耦合LDPC碼的分層譯碼算法
doi: 10.11999/JEIT190626 cstr: 32379.14.JEIT190626
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重慶大學(xué)通信與測控中心 重慶 400044
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重慶大學(xué)微電子與通信工程學(xué)院 重慶 400044
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空天地網(wǎng)絡(luò)互連與信息融合重慶市重點(diǎn)實(shí)驗(yàn)室 重慶 400044
A Layered Decoding Algorithm for Spatially-coupled LDPC Codes
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Centre of Communication and TTC, Chongqing University, Chongqing 400044, China
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College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
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3.
Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion, Chongqing University, Chongqing 400044, China
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摘要:
針對長碼長空間耦合低密度奇偶校驗(yàn)(SC-LDPC)碼譯碼時(shí)延較長的問題，該文提出了分層滑動(dòng)窗譯碼(LSWD)算法。該算法利用SC-LDPC子碼碼塊的準(zhǔn)循環(huán)特性和滑動(dòng)窗內(nèi)校驗(yàn)矩陣的層次結(jié)構(gòu)，通過在滑動(dòng)窗內(nèi)對校驗(yàn)矩陣進(jìn)行分層處理，優(yōu)化層與層之間消息傳遞，從而加快窗內(nèi)譯碼的收斂速度，減少了譯碼迭代次數(shù)。仿真和分析結(jié)果表明：在相同的信噪比(SNR)條件和相同的誤碼性能要求下，LSWD算法所需的迭代次數(shù)少于滑動(dòng)窗譯碼(SWD)算法，特別在高信噪比下，LSWD算法的迭代次數(shù)約為SWD算法的一半，從而有效縮短全局譯碼時(shí)延；在相同譯碼迭代次數(shù)下，LSWD算法的譯碼性能優(yōu)于SWD算法，而其計(jì)算復(fù)雜度增加不大。
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關(guān)鍵詞:
- 空間耦合低密度奇偶校驗(yàn)碼 /
- 分層算法 /
- 譯碼延時(shí) /
- 滑動(dòng)窗
Abstract:In order to solve the problem of the long decoding delay for the Spatially-Coupled Low-Density Parity-Check (SC-LDPC) code with long code length, a Layered Sliding Window Decoding (LSWD) algorithm is proposed. By exploring the quasi-cyclic characteristics of the SC-LDPC sub-codeblock and the hierarchical structure of the check matrix in the sliding window, the part of check matrix in the sliding window is layered to optimize the message transfer between two neighbor layers, with the aim of accelerating the convergence of the iterative procedure and reducing the number of decoding iterations. Simulation and analysis results show that the number of iterations in the proposed LSWD algorithm is less than that in the SWD, under the same Signal-to-Noise Ratio (SNR) and the bit error ratio. In the high SNR region, especially, the number of iterations in the proposed LSWD is about half of that in the SWD, hence the global decoding delay of the former is effectively shorten. In addition, the decoding performance of the LSWD algorithm is better than the SWD algorithm under the same number of decoding iterations, and the overall computational complexity is slightly increased.
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表 1 譯碼算法單次迭代過程的計(jì)算量比較
譯碼算法 加法運(yùn)算 $\phi (x)$運(yùn)算 SWD ${K_g} \times M \times W \times (J + K + 1)$ $2{K_g} \times M \times W \times J$ LSWD ${K_g} \times M \times W \times (2J + K + 1)$ $2{K_g} \times M \times W \times J$ 
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