DFT/LMS算法在DSSS中的应用及性能分析

本文分析了直接序列扩频(DSSS)系统中最小错误概率(MPE)意义下的最优滤波器,并依据矩阵求逆引理证明最小均方误差(MMSE)意义下的最优滤波——维纳滤波也是MPE意义下的最优滤波。在DSSS中应用自适应滤波,无须先验已知扩频码的码型和干扰的统计特性,就能一并完成解扩以及有效抑制干扰。离散傅立叶变换/最小均方(DFT/LMS)算法的收敛速度远快于LMS算法,而运算量、稳健性与LMS算法基本相同。基于DFT/LMS算法的自适应滤波大大简化DSSS系统接收机的设计,显著增强系统抗干扰能力,具有很强的实用性。

The Application and Performance Analysis of the DFT/LMS algorithm In DSSS

This paper analyzes the optimum filter optimized in the minimum probabil-ty of erro (MPE) sense in direct sequence spread spectrum (DSSS), then proves that, using matrix inversion lemma, the Wiener filter optimized in the minimum mean square error (MMSE) sense is also the optimum filter in the MPE sense. Applying the adaptive filter in DSSS, despreading and suppressing interfrence can be done simultaneously without prior knowledge of the pseudonoise code and the statistical characterization of the interference. The discret fourier transform / least mean square (DFT/LMS) algorithm has significantly improved convergence speed over the least mean square (LMS) algorithm, and meanwhile the complexities and robust performance of the two algorithms are almost identical. Since the adaptive filter using the DFT/LMS algorithm significantly simplifies the design of the receiver, and notably enhances the capability of the anti-interference, it is of good practicability.