Walsh码软扩频信号降噪算法

针对低信噪比(signal-to-noise ratio, SNR)条件下, Walsh码软扩频信号盲解扩以及多址信号盲分离难以实现的问题,提出一种Walsh码软扩频信号降噪算法。首先,采用经验模态分解(empirical mode decomposition, EMD)算法将Walsh码软扩频信号分解为有限个本征模态函数(intrinsic mode function, IMF),分界点位置可通过Walsh码软扩频信号和噪声的IMF自相关函数收敛速度的差异进行判断。然后,采用小波软阈值滤波算法处理分界点之前的IMF。最后,利用处理后的低阶IMF和分界点后的IMF重构Walsh码软扩频信号,减少由于降噪造成的信号损失。仿真结果表明,在一定低SNR范围内,降噪算法以较低误码率(bit error rate, BER)实现解调,信号损失较少。

Noise reduction algorithm of Walsh code soft spread spectrum signal

In order to solve the problem of soft spread spectrum signals with low signal-to-noise ratio (SNR) so that it is difficult to realize blind dispreading and blind separation, an improved soft spread spectrum noise reduction algorithm is presented. This algorithm uses the empirical mode decomposition (EMD) algorithm to realize the soft spread spectrum signal denoising, judges the location of the dividing point according to the difference between the soft spread spectrum signal and the noise auto-correlation function and applies wavelet threshold filtering to the intrinsic mode function (IMF) component before the dividing point. Finally, the soft spread spectrum signals are constructed by processed low order IMF components and IMF components after the dividing point. The algorithm makes use of lower IMF components and judges the location of the dividing point according to the auto-correlation characteristics of the IMF component to reduce the signal loss caused by noise reduction. Simulation results show that within a certain SNR range, the denoising algorithm achieves demodulation with low bit error rate (BER) and less signal loss.