改进Walsh码软扩频盲解扩算法

采用传统密度峰聚类算法实现Walsh码软扩频信号盲解扩时,算法的截断距离根据经验选取,性能不稳定。针对这一问题,提出改进密度峰聚类软扩频信号的盲解扩算法。该算法采用赋权欧氏距离作为数据的相似性度量并根据数据总体分布情况自适应确定截断距离,具有效率高、性能稳定等特点。Matlab环境下仿真结果表明,文中所述的改进算法能够自动确定截断距离,在信噪比为0~10dB范围内准确估计伪码序列规模数和伪码序列,并在该条件下实现Walsh码软扩频信号盲解扩。

Improved Walsh code soft spread spectrum blind despreading algorithm

The cutoff distance of the traditional density peak clustering algorithm which is selected according to experience used to realize blind estimating of Walsh code soft spread spectrum signals results in unstable performance. For this problem, weighted Euclidean distance as data similarity and adaptive determination of cutoff distance according to the data distribution instead of artificial selecting is proposed. The improved algorithm has the characteristics of high efficiency and stable performance. The Matlab simulation results show that the proposed algorithm can automatically determine the cutoff distance and accurately estimate the pseudo-code sequence size and pseudo-code sequence when the signal-to-noise rate is 0-10 dB.