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Combined adaptive multiuser detection for DS-CDMA system


Combined adaptive multiuser detection for DS-CDMA system

Guo, Yishu (2003) Combined adaptive multiuser detection for DS-CDMA system. Masters thesis, Concordia University.

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The inadequacy of the conventional Code Division Multiple Access (CDMA) receivers in a multiple access interference-limited mobile radio environment has spurred research on advanced receiver technologies. This research investigates the use of adaptive receivers for multi-user detection to overcome some of the deficiencies of a conventional receiver and, hence, suppress the multiple access interference (MAI) and narrow band interference (NBI) in DS/CDMA wireless systems. The MAI is a major factor influencing the communication quality and the capacity in CDMA wireless systems. Hence, suppression of MAI and NBI are essential for an efficient performance of a CDMA wireless system and to enhance the system capacity. Analysis of the conventional detector and minimum mean-squared error (MMSE) detector is carried out to provide a better understanding of the effect of the channel parameters on the performance of the detectors and to explain the near-far resilience of the receiver. The performance of these detectors are compared and analyzed. The derivation of the relationship between the minimum mean-squared error detector and minimum output energy (MOE) detector is developed in order to provide an adaptive implementation of the later. The limitation of the existing RLS blind algorithms for MMSE detector in AWGN channels is analyzed. In order to improve the performance of the existing schemes and to eliminate the requirement of training sequences, a scheme combining the blind adaptation and MAI cancellation is proposed. The performance of this algorithm is analyzed and compared with the existing schemes. Extensive simulations have been carried out to demonstrate that the proposed scheme is more reliable and it provides an effective resilience to the environment changes.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Guo, Yishu
Pagination:xiii, 122 leaves : ill. ; 29 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Dept. of Electrical and Computer Engineering
Thesis Supervisor(s):Ahmad, M. O
Identification Number:TK 5103.452 G86 2003
ID Code:2423
Deposited By: Concordia University Library
Deposited On:27 Aug 2009 17:28
Last Modified:13 Jul 2020 19:52
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