基于构造代价函数求解的自同步扰码盲识别方法

韩树楠; 张旻; 李歆昊

doi:10.11999/JEIT171026

基于构造代价函数求解的自同步扰码盲识别方法

doi: 10.11999/JEIT171026

韩树楠^{*张旻李歆昊},
张旻,
李歆昊

基金项目:

国家自然科学基金(61602491)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-11-02
- 修回日期: 2018-03-23
- 刊出日期: 2018-08-19

A Blind Identification Method of Self-synchronous Scramblers Based on Optimization of Established Cost Function

HAN Shunan^{*张旻李歆昊},
ZHANG Min,
LI Xinhao

Funds:

The National Natural Science Foundation of China (61602491)

摘要

摘要: 由于卷积码序列的0,1 bit的概率几乎均衡，对于卷积码自同步加扰的扰码盲识别，现有的基于输入序列0, 1 bit概率不均衡性的识别方法均已失效，为此该文提出一种新的自同步扰码盲识别方法。首先将卷积码自同步加扰序列进行分块处理，通过加扰数据块与卷积码校验向量相乘产生新的序列；然后以最大化新生成序列间线性约束关系成立概率为准则，利用解调输出的软判决序列建立自同步扰码反馈多项式系数的代价函数；最后根据自同步扰码反馈多项式的项数特点，在求解代价函数时改进了动态搜索烟花算法，增加了对烟花个体元素值的约束操作，由求解出的参量值识别出自同步扰码反馈多项式。仿真实验验证了所提方法的有效性，该方法无需遍历搜索反馈多项式，且具有较好的鲁棒性，所需数据量小，随着数据量的增大和扰码阶数的降低，其识别正确率逐渐提高。
- 自同步扰码 /
- 卷积码 /
- 反馈多项式 /
- 校验向量 /
- 烟花算法
Abstract: Since the probability bias between 0 and 1 bit in a convolutional code sequence is very small, the existing method based on the probability bias in the input sequence is ineffective for the identification of a self-synchronous scrambler placed after a convolutional encoder. To solve this problem, a novel method for the blind identification of a self-synchronous scrambler is proposed. First, the scrambled convolutional code sequence is divided into blocks, and a new bit sequence is generated, in which each bit is the dot product of a scrambled bit block with a parity check vector of the convolutional code. Second, based on the criteria of maximizing the probability that the linear equations in the generated bits hold, the cost function of the feedback polynomial coefficients of the self-synchronous scrambler is established using the soft decision sequence, which is the output of the demodulator. Third, according to the characteristic of the number of terms in the feedback polynomial, the dynamic fireworks algorithm is modified by constraining the values of elements in fireworks, and the cost function is optimized using the modified dynamic fireworks algorithm. Simulation experiments show the effectiveness of the proposed algorithm. There is no need to search for the feedback polynomial exhaustively in the proposed algorithm. It is robust to the noise and the number of data required is small. Moreover, along with the increase of the number of received data or the decrease of the order of the feedback polynomial, the correct identification ratio of the proposed method increases.
- Self-synchronous scrambler /
- Convolutional code /
- Feedback polynomial /
- Parity check vector /
- Fireworks algorithm

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参考文献(24)

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