| Author: | Chan, Cheuk-lam |
| Title: | Evaluation of three algorithms used in blind equalization |
| Degree: | M.Sc. |
| Year: | 1998 |
| Subject: | Equalizers (Electronics) Algorithms Hong Kong Polytechnic University -- Dissertations |
| Department: | Multi-disciplinary Studies Department of Electronic Engineering |
| Pages: | x, 105 leaves : ill. ; 30 cm |
| Language: | English |
| Abstract: | Most of the adaptive equalization methods require the initial training sequence. But in some cases, it is not possible to use the training sequence. As blind equalization schemes do not require the training sequence, blind equalizer can be used to achieve this goal. There are several types of blind equalization methods. However, the basic blind equalization method is the Constant Modulus Algorithm (CMA). Based on the Constant Modulus Algorithm, it is possible to develop other algorithms that can achieve better performance by increasing the system complexity. Three blind equalization algorithms - Least Mean Square Constant Modulus Algorithm (LMS-CMA), Fast Quasi-Newton Constant Modulus Algorithm (FQN-CMA), Conjugated Gradient Constant Modulus Algorithm (CG-CMA) - are studied. The relationship between the parameters and the performance are investigated. The FQN-CMA gives the shortest adaptation time and the fastest initial convergence rate. The LMS-CMA gives the longest adaptation time and slowest initial convergence rate while the CG-CMA gives the best compromise amongst these three algorithms. A Modified Conjugated Gradient Constant Modulus Algorithm (MCG-CMA) is the enhancement algorithm of the CG-CMA. The MCG-CMA can achieve similar performance as the CG-CMA with reducing the computational power. The study of optimum value of step size is suggested for further study for the MCG-CMA. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b14418733.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 5.74 MB | Adobe PDF | View/Open |
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