Channel Estimation For Visible Light Communications Using Neural Networks
| Autor: | Onur Karatalay, Anil Yesilkaya, Arif Selcuk Ogrenci, Erdal Panayirci |
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| Přispěvatelé: | Öǧrenci, Arif Selçuk, Panayirci, Erdal, Yeşilkaya, Anıl, Karatalay, Onur, Öğrenci, Arif Selçuk |
| Rok vydání: | 2016 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Artificial neural network business.industry Computer science Orthogonal frequency-division multiplexing Computer Science - Information Theory Information Theory (cs.IT) Computer Science - Neural and Evolutionary Computing Visible light communication 020206 networking & telecommunications 02 engineering and technology Communications system Nonlinear system 020210 optoelectronics & photonics 0202 electrical engineering electronic engineering information engineering Electronic engineering FOS: Electrical engineering electronic engineering information engineering Wireless Neural and Evolutionary Computing (cs.NE) Electrical Engineering and Systems Science - Signal Processing business Communication channel Computer Science::Information Theory |
| Zdroj: | IJCNN |
| Popis: | Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to rain neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions. COST-TUBITAK Research Grant |
| Databáze: | OpenAIRE |
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