CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization
The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Spolecnost pro radioelektronicke inzenyrstvi
2016-04-01
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| Series: | Radioengineering |
| Subjects: | |
| Online Access: | http://www.radioeng.cz/fulltexts/2016/16_01_0124_0131.pdf |
| _version_ | 1818971251128926208 |
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| author | E. Tugcu I. Kaya A. Yazgan |
| author_facet | E. Tugcu I. Kaya A. Yazgan |
| author_sort | E. Tugcu |
| collection | DOAJ |
| description | The channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques. |
| first_indexed | 2024-12-20T14:49:24Z |
| format | Article |
| id | doaj.art-fa44c802751c420791d18120d2a169ee |
| institution | Directory Open Access Journal |
| issn | 1210-2512 |
| language | English |
| last_indexed | 2024-12-20T14:49:24Z |
| publishDate | 2016-04-01 |
| publisher | Spolecnost pro radioelektronicke inzenyrstvi |
| record_format | Article |
| series | Radioengineering |
| spelling | doaj.art-fa44c802751c420791d18120d2a169ee2022-12-21T19:37:01ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122016-04-01251124131CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm OptimizationE. TugcuI. KayaA. YazganThe channel matched filter (CMF) is the optimum receiver providing the maximum signal to noise ratio (SNR) for the frequency selective channels. The output intersymbol interference (ISI) profile of the CMF convolved by the channel can be blindly obtained by using the autocorrelation of the received signal. Therefore, the inverse of the autocorrelation function can be used to equalize the channel passed through its own CMF. The only missing part to complete the proposed blind operation is the CMF coefficients. Therefore, in this work, the best training algorithm investigation is subjected for blind estimation of the CMF coefficients. The proposed method allows using more effective training algorithms for blind equalizations. However, the expected high performance training is obtained when the swarm intelligence is used. Unlike the stochastic gradient algorithms, the particle swarm optimization (PSO) is known to have fast convergence because its performance is independent of the characteristics of the systems used. The obtained mean square error (MSE) and bit error rate (BER) performances are promising for high performance real-time systems as an alternative to non-blind equalization techniques.http://www.radioeng.cz/fulltexts/2016/16_01_0124_0131.pdfBlind channel estimationblind channel equalizationparticle swarm optimizationchannel matched filter |
| spellingShingle | E. Tugcu I. Kaya A. Yazgan CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization Radioengineering Blind channel estimation blind channel equalization particle swarm optimization channel matched filter |
| title | CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization |
| title_full | CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization |
| title_fullStr | CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization |
| title_full_unstemmed | CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization |
| title_short | CMF-DFE Based Adaptive Blind Equalization Using Particle Swarm Optimization |
| title_sort | cmf dfe based adaptive blind equalization using particle swarm optimization |
| topic | Blind channel estimation blind channel equalization particle swarm optimization channel matched filter |
| url | http://www.radioeng.cz/fulltexts/2016/16_01_0124_0131.pdf |
| work_keys_str_mv | AT etugcu cmfdfebasedadaptiveblindequalizationusingparticleswarmoptimization AT ikaya cmfdfebasedadaptiveblindequalizationusingparticleswarmoptimization AT ayazgan cmfdfebasedadaptiveblindequalizationusingparticleswarmoptimization |