2011
DOI: 10.1007/978-3-642-17878-8_1
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Analysis of Successive Interference Cancellation in CDMA Systems

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Cited by 3 publications
(3 citation statements)
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“…Several methods have been proposed to deal with interference noises cancellations problem, such as successive interference cancellation (SIC) [5], [6] and parallel interference cancellation (PIC) [7], [8]. Both of them were originally devised to RF receivers to eliminate unmatched signals and spurious interference noises within the received signal+noises.…”
Section: Introductionmentioning
confidence: 99%
“…Several methods have been proposed to deal with interference noises cancellations problem, such as successive interference cancellation (SIC) [5], [6] and parallel interference cancellation (PIC) [7], [8]. Both of them were originally devised to RF receivers to eliminate unmatched signals and spurious interference noises within the received signal+noises.…”
Section: Introductionmentioning
confidence: 99%
“…Because this program must constantly order the new succession sort of multi-user to priority process the maximum power user [4], so any detection error on any detection level would increase MAI and lead detection error diffusion, then severely affect the precision of the subsequent levels [5]. As an optimal estimation algorithm under the linear minimum mean square error (MMSE) criterion [6], the Kalman algorithm can not only make on-line synchronous unbiased estimation of the unknown noise statistics characteristics in DS-CDMA system while conducting state filtering, but also build a state space model for the MUD processing, so as to use optimal filter to adaptive estimate the optimal decision vector [7].…”
Section: Introductionmentioning
confidence: 99%
“…However, both face the problem of a noise enhancement and the requirement of channel estimates at the receiver. The successive interference cancellation [10] technique in the nonlinear category of detectors faces the problem of error propagation, whereas the performance of Parallel interference cancellation (PIC) [11] heavily depends on the initial bit estimates. Recently, there has been increased interest in using evolutionary techniques for the MUD problem, e.g., the Genetic algorithm (GA) [12] , ant colony optimization [13] , Particle swarm optimization (PSO) [14,15] , and Honeybees Mating Optimization [16] .…”
mentioning
confidence: 99%