An Accurate Method for Calculating Extrinsic Information Transfer Functions of Iterative Processing Unit

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An Accurate Method for Calculating Extrinsic Information Transfer Functions of Iterative Processing Unit

更新时间：2025-07-09

- An Accurate Method for Calculating Extrinsic Information Transfer Functions of Iterative Processing Unit
- Vol. 43, Issue 8, Pages: 68-71(2009)
- 作者机构：
  
  电子科技大学雷达信号处理国家重点实验室,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN911.22
- Online First：10 August 2009，
  
  Published：2009
- 稿件说明：
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An Accurate Method for Calculating Extrinsic Information Transfer Functions of Iterative Processing Unit[J]. 2009, 43(8): 68-71.
DOI：

An Accurate Method for Calculating Extrinsic Information Transfer Functions of Iterative Processing Unit[J]. 2009, 43(8): 68-71. DOI：

摘要

针对现有计算方法得到的迭代处理单元的外部信息转移(EXIT)函数精确度较差的问题

提出了一种EXIT函数的精确计算方法——最优直方图法(OptHIST).首先根据信息比特的取值(+1或-1)将对数似然比数据进行分类

然后采用最优直方图估计对数似然比的概率分布函数

得到在积分最小均方误差意义上最优的概率分布函数

最后通过对对数似然的概率分布函数进行积分得到精确的EXIT函数.OptHIST法比直方图法鲁棒性更强

比直接求均值法适应性更广.实验结果表明

对于采用严格的后验概率算法的处理单元

OptHIST法可比直方图法减小误差约7%～15%.

Abstract

Focusing on the problem that the extrinsic information transfer(EXIT)functions of the iterative processing unit in existing methods are of low accuracy

an accurate method named optimal histogram(OptHIST)is proposed. The OptHIST algorithm is implemented in the following steps. The log-likelihood ratio data are classified according to the value(+1 or-1)of the info bits

and the optimal histogram is then used to estimate the probability distribution function of the log-likelihood ratio. The optimal probability distribution function is obtained in the sense of the integrated mean squared error.Then the exact EXIT function is obtained through integrating the probability distribution function of log-likelihood ratio. Compared with the existing methods

the proposed OptHIST method is more robust than the histogram method and more applicable than the direct average method. Simulation results show that the OptHIST method can reduce the error by about 7% to 15%

compared with the histogram method

for the processing unit which adopts rigid a-post probability algorithm.

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references

BERROU C, GLAVIEUX A, THITIMAJSHIMA P. Near shannon limit error-correcting coding and decoding: turbo-codes [C]∥Proc IEEE ICC. Piscataway, NJ, USA: IEEE, 1993: 1064-1070.

冯昂,殷勤业,张莹. 多输入多输出码分多址系统的Turbo联合检测 [J]. 西安交通大学学报,2007, 41(4): 471-474.

FENG Ang, YIN Qinye, ZHANG Ying. Turbo joint detection algorithm for multiple-input multiple-output code division multiple access system [J]. Journal of Xi'an Jiaotong University, 2007, 41(4): 471-474.

RICHARDSON T, SHOKROLLAHI A, URBANKE R. Design of capacity approaching low-density parity-check codes [J]. IEEE Trans on Information Theory, 2001, 47(2): 599-619.

ALEXEI A, GERHARD K, BRINK S T. Extrinsic information transfer functions: model and erasure channel properties [J]. IEEE Trans on Communication, 2008, 56(10): 2657-2673.

LI Kai, WANG Xiaodong, ASHIKHMIN A. EXIT function of Hadamard component in repeat zigzag Hadamard(RZH)codes with parallel decoding [J]. IEEE Trans on Information Theory, 2008, 54(11): 1773-1785.

KLIEWER J, SOON X N, HANZO L. Efficient computation of EXIT functions for nonbinary iterative decoding [J]. IEEE Trans on Communication, 2006, 54(12): 2133-2136.

COVER T M, THOMAS J A. Elements of information theory [M]. New York, USA: John Wiley, 1991.

WAND M P. Data-based optimal choice of histogram bin width [J]. The American Statistician, 1997, 51(1): 59-64.

TEE R, MAUNDER R G, HANZO L. EXIT-chart aided near-capacity irregular bit-interleaved coded modulation design [J]. IEEE Trans on Wireless Communications, 2009, 8(1):32-37.

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