Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.sigpro.2004.07.002
Title: Performance analysis of volterra-based nonlinear adaptive blind multiuser detectors for ds-cdma systems
Authors: Liniin, G.
Puthusserypady, S. 
Keywords: Approximate Newton
DS-CDMA
Exact Newton
MAI
Multiuser detection
Nonlinear
Volterra expansion
Issue Date: Oct-2004
Source: Liniin, G., Puthusserypady, S. (2004-10). Performance analysis of volterra-based nonlinear adaptive blind multiuser detectors for ds-cdma systems. Signal Processing 84 (10) : 1941-1956. ScholarBank@NUS Repository. https://doi.org/10.1016/j.sigpro.2004.07.002
Abstract: The major limitation on the performance and capacity of direct sequence code division multiple access (DS-CDMA) communication systems is the multiple access interference (MAI) due to simultaneous transmission of several users. The linear minimum mean square error (MMSE) detector is a well-known method to suppress MAI adaptively and blindly, however, it is sub-optimal because of the inherent nonlinearity of the system. Therefore, in this paper, two nonlinear blind adaptive interference cancellation algorithms (the exact Newton (EXN) and the approximate Newton (APN)) were proposed and developed based on the 2nd order Volterra expansion. A complete performance analysis of the conventional matched filter (MF) detector, linear adaptive detector (which employs the standard Newton algorithm) and the proposed two nonlinear adaptive (EXN and APN) detectors was carried out in various DS-CDMA systems. Numerical results show that the three Newton type adaptive blind multiuser detectors yield significant bit error ratio (BER) improvement over the conventional MF detector in the presence of strong MAI. Further, the two nonlinear adaptive algorithms always outperformed the linear algorithm. Most attractively, the APN algorithm offers lower computation complexity, higher numerical stability and almost identical BER performance in comparison with the EXN algorithm. © 2004 Elsevier B.V. All rights reserved.
Source Title: Signal Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/57022
ISSN: 01651684
DOI: 10.1016/j.sigpro.2004.07.002
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