Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Multiuser Detection
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
Introduction to CDMA Wireless Communications
Introduction to CDMA Wireless Communications
Blind multiuser detection based on kernel approximation
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Study of multiuser detection: the support vector machine approach
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Probability of error in MMSE multiuser detection
IEEE Transactions on Information Theory
Multi-user detection for DS-CDMA communications
IEEE Communications Magazine
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
Block PIC technique for synchronous CI/MC-CDMA system using neural network
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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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.