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Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels

Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels
Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels
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 (SVM), 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.
604-611
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Samingan, A.K.
16b3f372-cc0c-473e-b345-6807442f9fb0
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Samingan, A.K.
16b3f372-cc0c-473e-b345-6807442f9fb0
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, S., Samingan, A.K. and Hanzo, L. (2001) Support Vector Machine Multiuser Receiver for DS-CDMA Signals in Multipath Channels IEEE Transactions on Neural Networks, 12, (3), pp. 604-611.

Record type: Article

Abstract

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 (SVM), 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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More information

Published date: May 2001
Additional Information: submitted in May 2000, revised in Oct. 2000, accepted in Feb. 2001
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 252988
URI: http://eprints.soton.ac.uk/id/eprint/252988
PURE UUID: fbcb558a-5320-4c8a-84a0-eb31176eb726
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 17 Dec 2003
Last modified: 18 Jul 2017 10:00

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Contributors

Author: S. Chen
Author: A.K. Samingan
Author: L. Hanzo ORCID iD

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