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Blind Multiuser Detector for Chaos-Based CDMA Using Support Vector Machine

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3 Author(s)
Johnny Wei-Hsun Kao ; Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand ; Stevan Mirko Berber ; Vojislav Kecman

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.

Published in:

IEEE Transactions on Neural Networks  (Volume:21 ,  Issue: 8 )