Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 12, Issue 3, Pages 604-611Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/72.925563
Keywords
direct sequence code division multiple access (DS-CDMA); linear MMSE detector; multiuser detector (MUD); multiuser interference; optimal one-shot detector; support vector machines; unsupervised clustering
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The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVMs), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.
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