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Reduced RBF Centers Based Multi-user Detection in DS-CDMA Systems

机译:基于DS-CDMA系统中的基于RBF中心的多用户检测

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The major goal of this paper is to develop a practically im-plemental radial basis function neural network based multi-user detector for direct sequence code division in multiple access systems. This work is expected to provide an efficient solution by quickly setting up the proper number of radial basis function centers and their locations required in training. The basic idea in this research is to select all the possible radial basis function centers by using supervised k-means clustering technique, select the only centers which locate near seemingly decision boundary, and reduce them further by grouping some of the centers adjacent to each other. Therefore, it reduces the computational burden for finding the proper number of radial basis function centers and their locations in the existing radial basis function based multi-user detector, and ultimately, make its implementation practical.
机译:本文的主要目的是开发一种实际上的IM-PLIMMENTAL径向基础函数神经网络基于多用户检测器,用于多个接入系统中的直接序列码分。这项工作预计通过快速设置适当数量的径向基函数中心及其在培训所需的位置提供有效的解决方案。本研究中的基本思想是通过使用受监督的K-Means聚类技术选择所有可能的径向基函数中心,选择唯一定位在看似决策边界附近的唯一中心,并通过将彼此相邻的一些中心进行分组进一步减少它们。因此,它降低了在现有的径向基函数的多用户检测器中找到适当数量的径向基函数中心及其位置的计算负担,最终使其实现实用。

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