基于量子计算的Volterra LMS非线性信道估计
Quantum Calculus-based Volterra LMS for Nonlinear Channel Estimation
摘要：
提出了一种新的自适应滤波方法--$q$ -Volterra最小均方（$q$ -VLMS）。$q$ -VLMS是传统LMS的非线性扩展，并且它基于Jackson导数，也称为$q$ -演算。在Volterra LMS算法中，由于输入信号的方差很大，收敛速度很慢。通过适当的操作，我们成功地提高了Volterra LMS的收敛性能。分析了该算法的步长界，并通过对非线性信道估计问题的计算机仿真验证了分析结果。
英文摘要：
A novel adaptive filtering method called $q$-Volterra least mean square ($q$-VLMS) is presented in this paper. The $q$-VLMS is a nonlinear extension of conventional LMS and it is based on Jackson's derivative also known as $q$-calculus. In Volterra LMS, due to large variance of input signal the convergence speed is very low. With proper manipulation we successfully improved the convergence performance of the Volterra LMS. The proposed algorithm is analyzed for the step-size bounds and results of analysis are verified through computer simulations for nonlinear channel estimation problem.
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