本文關(guān)鍵詞： 極化碼 信道極化 連續(xù)消除解碼 校正判定　出處：《西北農(nóng)林科技大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：極化碼是Arikan在2009年提出的一種新的編碼方法,它是目前唯一一個(gè)被證明在二進(jìn)制離散無(wú)記憶信道中碼長(zhǎng)足夠長(zhǎng)的情況下能夠達(dá)到香農(nóng)極限的編碼,這種編碼方法是基于信道極化現(xiàn)象,利用信道組合和信道拆分構(gòu)造的一種編碼。極化碼在碼長(zhǎng)無(wú)限長(zhǎng)的條件下才能達(dá)到香農(nóng)極限,但是在實(shí)際通信使用中碼長(zhǎng)無(wú)限長(zhǎng)基本是不可能的。因此,極化碼在碼長(zhǎng)有限的條件下,尋找有效的解碼算法還需要更多的研究。本文通過(guò)對(duì)極化碼連續(xù)消除譯碼算法的研究,提出一種優(yōu)化方案,該方案將解碼中信息位的判定條件改進(jìn)為依據(jù)下個(gè)信息位在當(dāng)前信息位兩種情況下的LR值對(duì)比結(jié)果,并給出改進(jìn)解碼算法的復(fù)雜度。具體研究?jī)?nèi)容和結(jié)論如下:(1)分析了極化碼的基本原理。詳細(xì)討論了極化碼的信道組合、信道拆分以及信道極化現(xiàn)象。并討論了極化碼在二進(jìn)制高斯信道下的極化方式,給出了相應(yīng)的Z參數(shù)計(jì)算方法。(2)對(duì)極化碼的編解碼進(jìn)行了詳細(xì)研究。極化碼的編碼過(guò)程比較直觀(guān),編碼中最主要的部分是獲取生成矩陣和信道挑選。在解碼方面對(duì)極化碼的SC譯碼算法進(jìn)行了詳細(xì)分析,譯碼過(guò)程是采用遞歸的方法,信息位的判定是依據(jù)當(dāng)前計(jì)算的LR值。在高斯信道中對(duì)SC譯碼算法進(jìn)行仿真實(shí)現(xiàn),對(duì)其解碼性能進(jìn)行分析,并發(fā)現(xiàn)SC解碼器在碼長(zhǎng)有限的情況下性能較低的缺陷,提出優(yōu)化的關(guān)鍵點(diǎn)。(3)基于SC解碼算法提出優(yōu)化方案并給出詳細(xì)的解碼設(shè)計(jì)。在解碼過(guò)程中,優(yōu)化的解碼算法對(duì)當(dāng)前信息位的判定需要依據(jù)下個(gè)信息位在當(dāng)前信息位為0和1的條件下的LR值的對(duì)比,這種方法降低了整個(gè)解碼過(guò)程的錯(cuò)誤率。通過(guò)碼樹(shù)分析對(duì)比改進(jìn)的算法與SC解碼算法的計(jì)算流程,給出了優(yōu)化的解碼算法的復(fù)雜度。通過(guò)仿真實(shí)驗(yàn),對(duì)比優(yōu)化的解碼與SC解碼的性能,證明了優(yōu)化的解碼算法的誤碼率低于SC解碼,并對(duì)誤碼率曲線(xiàn)進(jìn)行了分析。
[Abstract]:Polarization code is a new coding method proposed by Arikan in 2009. It is the only code that has been proved to reach Shannon limit in binary discrete memoryless channel when the code length is long enough. This coding method is based on the phenomenon of channel polarization, which is constructed by channel combination and channel splitting. The polarization code can reach Shannon limit under the condition that the code length is infinite. However, it is impossible to use infinite code length in practical communication. Therefore, more research is needed to find effective decoding algorithm under the condition of limited code length. In this paper, we study the continuous elimination decoding algorithm of polarimetric code. An optimization scheme is proposed, in which the decision condition of information bit in decoding is improved as the comparison result of LR value of the next information bit in two cases of current information bit. And the complexity of the improved decoding algorithm is given. The detailed research contents and conclusions are as follows: 1) the basic principle of polarization code is analyzed, and the channel combination of polarization code is discussed in detail. The polarization mode of polarization code in binary Gao Si channel is discussed. The corresponding Z parameter calculation method is given. The encoding and decoding of polarization code is studied in detail. The encoding process of polarization code is more intuitive. The most important part of the coding is to obtain the generation matrix and channel selection. In the aspect of decoding, the SC decoding algorithm of polarization code is analyzed in detail. The decision of the information bit is based on the current calculated LR value. The SC decoding algorithm is simulated in Gao Si channel, and its decoding performance is analyzed. It is found that the SC decoder has a low performance in the case of limited code length. The key point of optimization is put forward. (3) based on SC decoding algorithm, the optimization scheme and detailed decoding design are presented. The decision of the current information bit by the optimized decoding algorithm needs to be compared with the LR value of the next information bit under the condition that the current information bit is 0 and 1. This method reduces the error rate of the whole decoding process. The complexity of the optimized decoding algorithm is given by comparing the improved algorithm with the SC decoding algorithm by code tree analysis. Comparing the performance of optimized decoding and SC decoding, it is proved that the BER of the optimized decoding algorithm is lower than that of SC decoding, and the BER curve is analyzed.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN911.22

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