Kao Johnny Wei-Hsun, Berber Stevan Mirko, Kecman Vojislav
Department of Electrical and Computer Engineering, University of Auckland, Auckland 1142, New Zealand.
IEEE Trans Neural Netw. 2010 Aug;21(8):1221-31. doi: 10.1109/TNN.2010.2048758. Epub 2010 Jun 21.
The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.
本文提出了一种基于混沌码分多址系统的盲多用户检测器算法及其结果，该检测器采用了一种名为支持向量机（SVM）的机器学习技术。仿真结果表明，在加性高斯白噪声（AWGN）和瑞利衰落条件下，使用支持向量机所实现的性能与现有的最小均方误差（MMSE）检测器相当。然而，与MMSE检测器不同的是，支持向量机检测器不需要知道系统中其他用户的扩频码，也不需要估计信道噪声方差。本文考虑了该算法的优化，并将其复杂度与MMSE检测器进行了比较。该检测器比MMSE更适合在前向链路中工作。此外，还推导了支持向量机检测器在AWGN和瑞利衰落条件下的原始理论误码率表达式，以验证仿真结果。
