Rayleigh信道下的支持向量机多用户检测方法
A support vector machine based detection method on rayleigh channel
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摘要: 在BPSK调制的DS-CDMA中,基于支持向量机(Support Vector Machine,SVM)的多用户检测方法采用支持向量机的分类方法将接受向量分成+1和-1两类,达到检测的目的。与MMSE方法不同的是,支持向量机分类器的目的是找出一个能将训练向量中信号为+1和信号为-1的两类数据分离的最佳分离超平面。从数值仿真结果可以看出,在Rayleigh信道,这种支持向量机的多用户检测方法与MMSE多用户检测器相比,输出能达到较低的误码率。Abstract: In DS-CDMA system with BPSK modulation, Support Vector Machine (SVM) based multiuser detection uses SVM classification method to classify received vectors into two classes. One is the received vectors of desired users symbol +1 and the other is the vectors of desired users symbol -1. So desired users symbol can be detected by this method. Different with MMSE method, SVM classifier finds the optimal separating hyperplane that separates the class of +1 and class of -1. Simulation results show that the performance of the SVM detector is better than that of MMSE detector in Rayleigh channels.
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B. Aazhang, B. Paris, G. Orsak, Neural networks for multiuser detection in CDMA communication, IEEE Trans. on Communications, 1992, 40(7), 1212-1222.[2]C. Burges, A tutorial on support vector machines for pattern recongition, Data Mining and Knowledge Discovery, 1998, 2(2), 121-167.[3]U. Madhow, M. Honing, MMSE interference suppression for direct-sequence spread-spectrum CDMA, IEEE Trans. on Communications, 1994, 42(12), 3178-3188.[4]U. Madhow, MMSE interference suppression for timing acquisition and demodulation in directsequence CDMA systems, IEEE Trans. on Communications, 1998, 46(8), 1065 1075.[5]G. Proakis, Digital Communication, Third Edition, USA: McGraw-Hill, 1995, 758-833.[6]S. Rappaport, Wireless Communications Principles and Practice, USA, Prentice-Hall, 1996, 177181.[7]S. Keerthi, S, Shevade, C. Bhattacharyya, K. Murthy, A fast iterative nearest point algorithn for support vector machine classifier design, IEEE Trans. on Neural Networks, 2000, 11 (1), 124-136. -
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