基于非线性Volterra信道的复数神经多项式盲均衡算法

Nonlinear Volterra Channel Based Complex-Valued Neural Polynomial Blind Equalization Algorithm

针对传统常模算法收敛速度慢、均方误差大以及传统神经网络参数多、复杂度高的问题,提出了基于非线性Volterra信道的复数神经多项式盲均衡算法(Fuzzy neural network-complex valued neural polynomial-constant modulus algorithm,FNN-CNP-CMA)。该算法包含单层神经网络和非线性处理器的复数神经多项式,模块结构简单、复杂度低。由模糊神经网络(Fuzzy neural network,FNN)设计的模糊规则控制器能有效提高步长的控制精度。仿真实验结果表明,该算法系统结构简单、复杂度低、收敛速度快且稳态误差小,较好地解决了收敛速度与均方误差之间存在的矛盾。

Aiming at the low convergence rate and high mean square error of traditional constant modulus algorithm（CMA） and too many parameters and high complexity of traditional neural network, a complex neural polynomial blind equalization algorithm based on nonlinear Volterra channel is studied. In the studied algorithm, the complex-valued neural polynomial with a single layer neural network and nonlinear processor has very simple structure and low complexity. And the fuzzy rule controller based on fuzzy neu- ral network （FNN） can effectively control the step-size of scale factor. The simulation results show that the proposed algorithm not only has simple structure, low complexity, fast convergence speed and small steady-state error, hut also can solve the contradiction between convergence speed and mean square error.