均衡技术是克服码间干扰(Inter-Symbol Interference,ISI)的有效措施,由于信道特性的随机性与时变性,实际中消除码间干扰最常用的是自适应均衡器。本文对基于最小均方(Least Mean Squares,LMS)算法和递推最小二乘(Recursive Least Squar...均衡技术是克服码间干扰(Inter-Symbol Interference,ISI)的有效措施,由于信道特性的随机性与时变性,实际中消除码间干扰最常用的是自适应均衡器。本文对基于最小均方(Least Mean Squares,LMS)算法和递推最小二乘(Recursive Least Squares,RLS)算法的自适应均衡器进行仿真研究,分析了信道特性与设计参数对自适应均衡器的收敛速度与稳态性能的影响。展开更多
针对混沌通信系统的非线性信道干扰问题,基于混沌信号重构理论和函数型连接神经网络理论,提出了一种横向滤波器与函数型连接神经网络组合(combination of transversal filter and functional link neural network,CFFLNN)的自适应非线...针对混沌通信系统的非线性信道干扰问题,基于混沌信号重构理论和函数型连接神经网络理论,提出了一种横向滤波器与函数型连接神经网络组合(combination of transversal filter and functional link neural network,CFFLNN)的自适应非线性信道均衡器,并给出基于低复杂度归一化最小均方(NLMS)的自适应算法,并对该均衡器的稳定性以及收敛条件进行了分析.该非线性自适应均衡器充分利用了横向滤波器的快速收敛,以及函数型连接神经网络通过增大输入空间提高非线性逼近能力的特点,进一步提高均衡器的收敛速度和降低稳态误差.仿真研究表明:所提出的非线性自适应均衡器能够有效地消除线性和非线性信道干扰,均衡器输出信号能反映出混沌信号的特性,具有良好的抗干扰性能;且该均衡器的结构简单,收敛稳定性较好,易于工程实现.展开更多
In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum ...In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum mean square error (MSE) and the steady state excess MSE of the WTD adaptive equalizer are obtained. Constant and time varying convergence factor adaptive algorithms are studied respectively. Computational complexities of WTD LMS equalizers are given. The equalizer in WTD shows much better convergence performance than that of the conventional in time domain. 展开更多
文摘均衡技术是克服码间干扰(Inter-Symbol Interference,ISI)的有效措施,由于信道特性的随机性与时变性,实际中消除码间干扰最常用的是自适应均衡器。本文对基于最小均方(Least Mean Squares,LMS)算法和递推最小二乘(Recursive Least Squares,RLS)算法的自适应均衡器进行仿真研究,分析了信道特性与设计参数对自适应均衡器的收敛速度与稳态性能的影响。
文摘针对混沌通信系统的非线性信道干扰问题,基于混沌信号重构理论和函数型连接神经网络理论,提出了一种横向滤波器与函数型连接神经网络组合(combination of transversal filter and functional link neural network,CFFLNN)的自适应非线性信道均衡器,并给出基于低复杂度归一化最小均方(NLMS)的自适应算法,并对该均衡器的稳定性以及收敛条件进行了分析.该非线性自适应均衡器充分利用了横向滤波器的快速收敛,以及函数型连接神经网络通过增大输入空间提高非线性逼近能力的特点,进一步提高均衡器的收敛速度和降低稳态误差.仿真研究表明:所提出的非线性自适应均衡器能够有效地消除线性和非线性信道干扰,均衡器输出信号能反映出混沌信号的特性,具有良好的抗干扰性能;且该均衡器的结构简单,收敛稳定性较好,易于工程实现.
文摘In this paper performances of wavelet transform domain (WTD) adaptive equalizers based on the least mean￣square (LMS) algorithm are analyzed. The optimum Wiener solution, the condition of convergence, the minimum mean square error (MSE) and the steady state excess MSE of the WTD adaptive equalizer are obtained. Constant and time varying convergence factor adaptive algorithms are studied respectively. Computational complexities of WTD LMS equalizers are given. The equalizer in WTD shows much better convergence performance than that of the conventional in time domain.
