Convergence Analysis of MultiModulus-Based Blind Equalization Algorithms
碩士 === 輔仁大學 === 電子工程學系 === 93 === Unlike traditional channel equalizers, blind equalizers do not require a training sequence to start up or restart when the communication unexpectedly breaks down. This blind start-up ability is particularly useful in applications such as broadcast and point-to-multi...
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| Format: | Others |
| Language: | en_US |
| Published: |
2004
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| Online Access: | http://ndltd.ncl.edu.tw/handle/85057441434838304919 |
| Summary: | 碩士 === 輔仁大學 === 電子工程學系 === 93 === Unlike traditional channel equalizers, blind equalizers do not require a training sequence to start up or restart when the communication unexpectedly breaks down. This blind start-up ability is particularly useful in applications such as broadcast and point-to-multipoint networks. In this thesis, we propose a new blind equalization algorithm referred to herein as the multimodulus stop-and-go decision-directed algorithm (MSG-DDA) by combining the advantages of both an existing multimodulus algorithm (MMA) and a well-known stop-and-go algorithm (SGA). The dynamic convergence properties of the MSG-DDA, the MMA, and the SGA for blind equalization are mathematically analyzed using a conditional Gaussian approximation. The derived theoretical mean-squared-error (MSE) trajectories for the three algorithms are compared with their corresponding simulation results. The results verify that the MSG-DDA substantially outperforms both the MMA and SGA.
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