Joint Channel and Symbol Estimation for OFDM System
碩士 === 國立臺灣海洋大學 === 電機工程學系 === 104 === In real wireless communication environments, intersymbol interference of transmit signals are usually caused by multipath effect, thereby reducing the symbol detection reliability. In order to overcome the channel effect in OFDM systems, we propose a blind chan...
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ndltd-TW-104NTOU54420292017-10-01T04:30:12Z http://ndltd.ncl.edu.tw/handle/47950886396261043816 Joint Channel and Symbol Estimation for OFDM System 正交分頻多工系統之聯合通道與符元估測 Liao, Pei-Jen 廖培任 碩士 國立臺灣海洋大學 電機工程學系 104 In real wireless communication environments, intersymbol interference of transmit signals are usually caused by multipath effect, thereby reducing the symbol detection reliability. In order to overcome the channel effect in OFDM systems, we propose a blind channel equalization method, based on Particle Swarm Optimization (PSO), to jointly estimate channel parameters as well as transmit symbols, without using any reference (training) symbols, to improve detection probability. The PSO-based blind channel equalization method utilizes the distance function, derived from the maximum likelihood principle, as the fitness function to assess the locations of the locally best particles and the globally best particle at each iteration, so that every particle’s position and velocity can be adjusted accordingly at the next iteration. Through each iteration, estimation accuracy is gradually increased until the iteration number achieves its preset value, and the particle of the minimum fitness value is chosen and its position is regarded as the best estimated values of the parameters. Simulation results show that the proposed method is feasible and its performance can be improved by adjusting the iteration number and the particle number. Hung, Hsien-Sen 洪賢昇 2016 學位論文 ; thesis 32 zh-TW |
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碩士 === 國立臺灣海洋大學 === 電機工程學系 === 104 === In real wireless communication environments, intersymbol interference of transmit signals are usually caused by multipath effect, thereby reducing the symbol detection reliability. In order to overcome the channel effect in OFDM systems, we propose a blind channel equalization method, based on Particle Swarm Optimization (PSO), to jointly estimate channel parameters as well as transmit symbols, without using any reference (training) symbols, to improve detection probability.
The PSO-based blind channel equalization method utilizes the distance function, derived from the maximum likelihood principle, as the fitness function to assess the locations of the locally best particles and the globally best particle at each iteration, so that every particle’s position and velocity can be adjusted accordingly at the next iteration. Through each iteration, estimation accuracy is gradually increased until the iteration number achieves its preset value, and the particle of the minimum fitness value is chosen and its position is regarded as the best estimated values of the parameters. Simulation results show that the proposed method is feasible and its performance can be improved by adjusting the iteration number and the particle number.
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author2 |
Hung, Hsien-Sen |
author_facet |
Hung, Hsien-Sen Liao, Pei-Jen 廖培任 |
author |
Liao, Pei-Jen 廖培任 |
spellingShingle |
Liao, Pei-Jen 廖培任 Joint Channel and Symbol Estimation for OFDM System |
author_sort |
Liao, Pei-Jen |
title |
Joint Channel and Symbol Estimation for OFDM System |
title_short |
Joint Channel and Symbol Estimation for OFDM System |
title_full |
Joint Channel and Symbol Estimation for OFDM System |
title_fullStr |
Joint Channel and Symbol Estimation for OFDM System |
title_full_unstemmed |
Joint Channel and Symbol Estimation for OFDM System |
title_sort |
joint channel and symbol estimation for ofdm system |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/47950886396261043816 |
work_keys_str_mv |
AT liaopeijen jointchannelandsymbolestimationforofdmsystem AT liàopéirèn jointchannelandsymbolestimationforofdmsystem AT liaopeijen zhèngjiāofēnpínduōgōngxìtǒngzhīliánhétōngdàoyǔfúyuángūcè AT liàopéirèn zhèngjiāofēnpínduōgōngxìtǒngzhīliánhétōngdàoyǔfúyuángūcè |
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1718542109361831936 |