一种免疫径向基网络多用户检测方法

An immune RBF neural network MUD method

径向基函数神经网络是一种具有局部逼近能力的前向神经网络模型,它可用作码分多址中的多用户检测器,并具有较好的检测性能;但是存在不足,主要表现在网络的构造和训练算法的优化方面.针对径向基函数网络在码分多址多用户检测应用中存在的网络复杂度与性能之间的矛盾,引入免疫的思想,构造了一种新颖的免疫径向基函数网络多用户检测器.它具有收敛速度快、推广能力好、鲁棒性强等优点,因而有更好的实时性和实用性.

The Radical Basis Function Neural Network(RBFNN) is a feed-forward NN model with the capacity of local approximation, so it can be used as a multiple-user detector (MUD) in the CDMA. Although it proves to be able to present a good performance in the detection, it has some disadvantages which lie in the determination of its structure and the optimization of its training algorithm. In this paper, a novel immune RBFNN MUD is proposed to balance computational complexity and performance of RBFNN. It is characteristic of more rapid convergence, better generalization and greater robustness, so its has better practicability and real-time performance. Simulations also prove its effectiveness.