基于BP神经网络、牛顿法的组合盲均衡技术

Blind Equalization Algorithms Based on BP Neural Network and Newton's Method

随着计算机技术的迅速发展,我们对通信网络的性能和速度的要求也变得更高。由于其结构极其复杂、用户接入的随机性和信元交换的不可预测性,目前宽带网技术的难题在于满足用户的需要以及保证通讯服务质量的同时来如何充分利用网络资源。本文主要采用BP神经网络的梯度和牛顿法相结合的算法,利用其各自的优缺点互补的特性,再结合的盲均衡算法,解决信道间的相互干扰。

With the rapid development of computer technology,we have higher. Requirements about the performance and speed of the communication network .Because of its structure is extremely complex, unpredictability and randomness of user access cell switching, the current problem is that broadband technology to meet the needs of users and to ensure the quality of communication service ,at the same time how to make full use of network resources. This paper uses gradient and Newton Combination with BP neural network algorithm, the use of their respective advantages and disadvantages of complementary features, What is the use in blind equalization algorithm to solve the mutual interference between channels.