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Analytical performance evaluation of multiuser detection and precoding
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|Title:||Analytical performance evaluation of multiuser detection and precoding|
|Keywords:||Code division multiple access|
|Abstract:||Multiuser detection (MUD) is an advanced technique that can be implemented at the receiver side to remove interference in wireless communication systems. On the other hand, precoding is a complementary technique to MUD in a sense that it can playa role of MUD being implemented at the transmitter side. In this dissertation, we develop analytical methods to evaluate the performance of MUD and precoding with channel estimation error. Specifically, we consider three different types of systems: 1. group-blind MUD algorithm in code division multiple access (CDMA) system with blind channel estimation, 2. zero-forcing beamformer, as one of the linear precoding methods, in multiple transmit antenna CDMA downlink system, 3. Tomlinson-Harashima precoding, as one of the non-linear precoding methods, with training-based maximum likelihood (ML) channel estimation method in time division duplex (TDD) multiple-input-multiple-output (MIMO) systems. The analytical results obtained in the dissertation match very well with the simulation results, and show that blind channel estimation is a very suitable channel estimation method for group-blind MUD from the performance and structure viewpoints. Further, it confirms the importance of accurate channel state information at the transmitter when using precoders. In the last section of the dissertation, we propose a simple tuning process and a new block iterative MUD algorithm based on constant modulus (CM) property of the data symbols, named CM tuning process and blind LSCMA, respectively. CM tuning process is applied after original subspace-based blind/group-blind MUDs, and provides significant performance gain over the original subspace-based blind/group-blind MUDs, with small increase of receiver complexity. Blind LSCMA also outperforms original blind/group-blind MUD algorithms considerably, and approaches the performance of ideal minimum mean square error (MMSE) detector even with a reasonably small number of data symbols.|
|Description:||Thesis (Ph. D.)--University of Hawaii at Manoa, 2005.|
Includes bibliographical references (leaves 105-109).
Also available by subscription via World Wide Web
xii, 109 leaves, bound ill., charts 29 cm
|Rights:||All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.|
|Appears in Collections:||Ph.D. - Electrical Engineering|
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