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.
9310a111-f79a-48b8-98c7-383ca93cbb80
Samingan, A.K.
16b3f372-cc0c-473e-b345-6807442f9fb0
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
May 2001
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
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), .
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.
Text
sqc-aks-lh-nn-may-2001.pdf
- Other
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
Catalogue record
Date deposited: 17 Dec 2003
Last modified: 18 Mar 2024 02:33
Export record
Contributors
Author:
S. Chen
Author:
A.K. Samingan
Author:
L. Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics