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基于自适应投影学习算法的RBF神经网络多用户检测方法 被引量:1

A Multiuser Detection Approach Using RBF Neural Networks based on Adaptive Projective Learning Algorithm
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摘要 自适应投影学习算法是一种简单有效的构造和训练径向基函数神经网络的方法,该方法能迭代地确定径向基函数的个数,中心的位置以及网络的权系数。本文将基于自适应投影学习算法的径向基函数神经网络应用于CDMA系统多用户检测,仿真表明:这种方法对远近问题不敏感,具有良好的误码率性能和抗多址干扰性能。 Based on adaptive projective learning algorithm which is a simple and efficient means for constructing and training radial basis function neural networks, the centers of RBF and the weights of networks can be choosed one by one. In this paper, the RBF neural network based on adaptive projective algorithm has been used to solve the multiuser detection problem in CDMA. Simulation results are provided to show that this method is near-far resistant, and has comparable performance to the optimal detection approach.
出处 《信号处理》 CSCD 2002年第6期518-521.,共4页 Journal of Signal Processing
关键词 自适应投影 学习算法 RBF神经网络 多用户检测方法 移动通信 码分多址 多址干扰 径向基函数 Code-division multiple-access Multiple-access interference Multiuser detection Radial basis function Adaptive projective algorithm Neural network
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参考文献10

  • 1S.Verdu, "Minimum probability of error for asynchronous Gaussian multiple access channels", IEEE Trans. Inform. Theory, vol. IT-32, pp. 85-96, Jan. 1986.
  • 2S.Verdu, "Optimum multiuser asymptotic efficiency", IEEE Trans. Commun. Vol. COM-34, No. 9, Sept. 1989.
  • 3R.Lupas and S.Verdu, "Linear multiuser detector for synchronous code-division multiple access channels", IEEE Trans. Inform. Theory, vol. 35, pp. 123-136, Jan. 1989.
  • 4M.K.Varansi and B.Aazhang, "Multi-stage detection in asynchronous code division multiple access communications", IEEE. Trans. Commun., vol. 38, pp. 509-519, Apr. 1990.
  • 5B.Aazhang, B.-P.Paris, and G.Orsak, "Neural networks for multiuser detection in CDMA communication," IEEE Trans. Commun., vol. 40, no. 7, pp. 1212-1222, July 1992.
  • 6G.Kechriotis and E.S.Manolakos, "Hopfield neural network implementation in the optimal CDMA multiuser detector," IEEE Trans. Neural Networks, vol. 7, No. 1, 1996.
  • 7U.Mitra and H.V.Poor, "Neural Network techniques for adaptive multiuser demodulation", IEEE J.Select.Areas Commun., vol. 12, no. 9, pp. 1460-1470, Dec. 1994.
  • 8J.Park and I.Sandberg, "Universal approximation using radial basis function network ". Neural Computa., 1991. 3(2):246-257.
  • 9S.Mallat and Z.Zhong, "Matching pursuits with time-frequency dictionaries". IEEE Trans. Signal Proc., 1993,41(12): 3397-3415.
  • 10张茁生,刘贵忠,刘峰.基于自适应投影学习算法的径向基函数网络设计及应用[J].电子学报,2000,28(9):120-122. 被引量:6

二级参考文献4

  • 1Hush D,IEEE Signal Processing Magazine,1993年,8页
  • 2Mallat S,IEEE Trans Signal Processing,1993年,41卷,12期,3397页
  • 3Chen S,IEEE Trans Neural Networks,1991年,12卷,2期,302页
  • 4Chen S,Signal Processing,1991年,22卷,1期,77页

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