DocumentCode :
816304
Title :
Multiuser Receiver for DS-CDMA Signals in Multipath Channels: An Enhanced Multisurface Method
Author :
Mahendra, C. ; Puthusserypady, Sadasivan
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore
Volume :
17
Issue :
6
fYear :
2006
Firstpage :
1592
Lastpage :
1605
Abstract :
This paper deals with the problem of multiuser detection in direct-sequence code-division multiple-access (DS-CDMA) systems in multipath environments. The existing multiuser detectors can be divided into two categories: 1) low-complexity poor-performance linear detectors and 2) high-complexity good-performance nonlinear detectors. In particular, in channels where the orthogonality of the code sequences is destroyed by multipath, detectors with linear complexity perform much worse than the nonlinear detectors. In this paper, we propose an enhanced multisurface method (EMSM) for multiuser detection in multipath channels. EMSM is an intermediate piecewise linear detection scheme with a run-time complexity linear in the number of users. Its bit error rate performance is compared with existing linear detectors, a nonlinear radial basis function detector trained by the new support vector learning algorithm, and Verdu´s optimal detector. Simulations in multipath channels, for both synchronous and asynchronous cases, indicate that it always outperforms all other linear detectors, performing nearly as well as nonlinear detectors
Keywords :
code division multiple access; multipath channels; multiuser detection; spread spectrum communication; direct-sequence code-division multiple-access; enhanced multisurface method; intermediate piecewise linear detection scheme; multipath channels; multiuser detection; multiuser receiver; run-time complexity; Bandwidth; Detectors; Multiaccess communication; Multipath channels; Multiuser detection; Neural networks; Recurrent neural networks; Runtime; Support vector machine classification; Support vector machines; Direct-sequence code-division multiple access (DS-CDMA); multipath channels; multisurface method; multiuser detection (MUD); steepest descent algorithm; support vector machines (SVMs); Algorithms; Computer Communication Networks; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2006.881048
Filename :
4012034
Link To Document :
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