论文标题
部分更新的变体增强了CLMS算法及其性能分析
Variants of Partial Update Augmented CLMS Algorithm and Their Performance Analysis
论文作者
论文摘要
通过增强的复杂的最小值（ACLMS）算法有效地处理了自然复杂值的信息或在复杂域中呈现的信息。在某些应用程序中，ACLMS算法在计算和内存密集型上可能过于实现。在本文中，提出了一种新算法，称为部分上更高的ACLMS（PU-ACLMS）算法，其中仅选择了系数集的一小部分来在每次迭代时进行更新。这样做，将两种类型的部分更高方案称为顺序和随机部分更高，以减少相应的自适应滤波器中的计算负载和功率消耗。讨论了全更高的PU-ACLM及其部分更高的实现的计算成本。接下来，分析了PU-ACLM的稳态均值和平均值性能，并分析了稳态过量均值误差（EMSE）和均方偏差（MSD）的非圆形复合物信号的闭合形式表达式。然后，采用加权能量保存关系，得出EMSE和MSD学习曲线。通过数值示例对仿真结果进行了验证，并将其与理论预测的结果进行了比较。
Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally- and memory-intensive to implement. In this paper, a new algorithm, termed partial-update ACLMS (PU-ACLMS) algorithm is proposed, where only a fraction of the coefficient set is selected to update at each iteration. Doing so, two types of partial-update schemes are presented referred to as the sequential and stochastic partial-updates, to reduce computational load and power consumption in the corresponding adaptive filter. The computational cost for full-update PU-ACLMS and its partial-update implementations are discussed. Next, the steady-state mean and mean-square performance of PU-ACLMS for non-circular complex signals are analyzed and closed-form expressions of the steady-state excess mean-square error (EMSE) and mean-square deviation (MSD) are given. Then, employing the weighted energy-conservation relation, the EMSE and MSD learning curves are derived. The simulation results are verified and compared with those of theoretical predictions through numerical examples.
