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Document Type:Latin Dissertation
Language of Document:English
Record Number:53086
Doc. No:TL23040
Call number:‭3290147‬
Main Entry:Muhammad Moinuddin
Title & Author:Constrained adaptive algorithms for CDMA systemsMuhammad Moinuddin
College:King Fahd University of Petroleum and Minerals (Saudi Arabia)
Date:2006
Degree:Ph.D.
student score:2006
Page No:210
Abstract:Since multiuser Direct Sequence Code Division Multiple Access (DS-CDMA) communications systems are significantly interfered by Multiple Access Interference (MAI), its characterization is then of paramount importance in the performance analysis of these systems. In this work, a detailed analysis of the MAI for synchronous downlink CDMA systems is carried out for BPSK signals with random signature sequences in an AWGN and different fading environments namely the Nakagami-m, the Rayleigh, the One-sided Gaussian, the Nakagami-q, the Rician and the Weibul. Consequently, new explicit closed-form expressions for the probability density function (pdf) of MAI and MAI plus noise are derived for these environments. Moreover, the solution of optimum signal detection problem is presented based on the derived statistics of MAI plus noise and expressions for probability of bit error is obtained for these environments. Furthermore, a Standard Gaussian Approximation (SGA) is also developed for these fading environments to compare the performance of optimum receivers. Our derivations are verified through a number of simulations and found to corroborate the theoretical findings. Also, it is known that the learning speed of an adaptive algorithm can be increased by adding a constraint to it as in the case of the Normalized LMS algorithm. In this work, an MAI plus noise constrained LMS-based algorithm is derived where RobbinsMonro algorithm is used to minimize the conventional mean square error criterion subject to the new combined constraint comprising both the MAI and noise variance. This constrained optimization technique results in an (MAI plus noise) -constrained LMS (MNCLMS) algorithm. Convergence and tracking analysis of the proposed algorithm are carried out in the presence of MAI. Finally, a number of simulations are conducted to compare the performance of the MNCLMS algorithm with other adaptive algorithms.
Subject:Applied sciences; Electrical engineering; 0544:Electrical engineering
Added Entry:King Fahd University of Petroleum and Minerals (Saudi Arabia)