摘要： 对称α稳定(SαS)分布噪声是一种非高斯噪声，相对于高斯噪声具有明显的脉冲特性，因此高斯噪声下的软解映射算法不适用于SαS分布噪声中。为解决该问题，根据高斯噪声下软解映射算法的对数似然比和信号幅度呈线性的特点，提出一种SαS分布噪声下基于欧式距离的软解映射算法，只需在高斯噪声下的软解映射算法和译码算法之间加入预处理算法，限制比特软信息的幅度，并将幅度过高的软信息置零。仿真结果显示，该算法实现简单、运算量低，所需信噪比在α=1.84的SαS分布噪声下比Huber算法低0.3 dB，在α=1.3的SαS分布噪声下低2 dB~5 dB。

关键词: 软解映射, Turbo译码, 对称α稳定分布噪声, Huber惩罚函数, Turbo卷积码

Abstract: Symmetric α Stable(SαS) distribution noise is a kind of non-Gaussian noise, which has obvious pulse characteristics compared with the Gaussian noise. Therefore, the soft de-mapping designed in Gauss noise does not apply to SαS noise. According to the linear characteristic between the logarithmic likelihood ratio of soft de-mapping in Gauss noise and amplitude of the signal, the soft de-mapping algorithm in SαS noise is proposed. The main idea of the proposed algorithm which adds a preprocessing algorithm between soft de- mapping and decoding algorithm in Gauss noise. The bit soft information is limited by the preprocessing algorithm, and the soft information with large amplitude is set to zero. Simulation results show that the Generalized Signal-to-noise Ratio(GSNR) of proposed algorithm is 0.3 dB lower than Huber under the same bit error rate in SαS noise of α=1.84, and 2 dB~5 dB lower than Huber in SαS noise of α=1.3.

Key words: soft de-mapping, Turbo decoding, Symmetric α Stable(SαS) distribution noise, Huber penalty function, Turbo Convolutional Code(TCC)

中图分类号: 

• TN911