扩频系统中最优干扰抑制技术研究——盲自适应算法及并行实现

Research on optimum interference suppression technique for spread spectrum systems:blind adaptive algorithm and parallel plementation

为解决扩频系统在动态环境下的干扰抑制问题,分析了从最小均方误差(MMSE)准则和约束最小均值输出能量(MMOE)准则导出的递推最小二乘(RLS)算法和盲递推最小二乘(BRLS)算法的性能。采用正交三角分解克服两算法数值稳定性差,运算量大,很难并行实现的缺点,讨论了正交三角分解——递推最小二乘(QR-RLS)算法与正交三角分解——盲递推最小二乘(QR-BRLS)算法的配合使用,并给出实现QR-RLS算法和QR-BRLS算法的脉动阵列(systolic array)。理论分析和仿真结果均表明QR-RLS与QR-BRLS算法的合理配合能较好的解决动态环境下的干扰抑制问题。

In order to solve the problem of the dynamic interference suppression for spread spectrum systems, the recursive least squares(RLS)adaptive versions of the minimum mean square error (MMSE) criterion, and the blind recursive least squares (BRLS) adaptive versions of the constrained minimum mean output energy (MMOE) criterion were analyzed. The QR decomposition could overcome three major problems associated with the two adaptive algorithms, that is, numerical instability, computational complexity and difficult for parallel implementation. Then, the matters in the transition from the QR decomposition-based BRLS (QR-BRLS) to the QR decomposition-based RLS (QR-RLS) and vice versa were discussed. The systolic arrays for parallel implementation of the QR-RLS and the QR-BRLS adaptive interference suppression algorithms were also proposed. The theoretic analyses and the emulator results all indicate that the cooperation of the two algorithms can preferably resolve the dynamic interference suppression.