Machine learning for DCO-OFDM based LiFi
| Autor: | M. Rubaiyat Hossain Mondal, Krishna Saha Purnita |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Light
Orthogonal frequency-division multiplexing Radio Waves Information Theory Information Storage and Retrieval Optical power computer.software_genre Polynomials Standard deviation Machine Learning Mathematical and Statistical Techniques MATLAB computer.programming_language Mathematics Multidisciplinary Applied Mathematics Simulation and Modeling Physics Electromagnetic Radiation Data Collection Statistics Records Physical Sciences Medicine Regression Analysis Engineering and Technology Wireless Technology Algorithms Research Article Optimization Computer and Information Sciences Visible Light Science Linear Regression Analysis Machine learning Research and Analysis Methods Machine Learning Algorithms Artificial Intelligence Linear regression Humans Statistical Methods Polynomial regression business.industry Background Signal Noise Function (mathematics) Algebra Signal Processing Artificial intelligence business computer DC bias |
| Zdroj: | PLoS ONE PLoS ONE, Vol 16, Iss 11, p e0259955 (2021) |
| ISSN: | 1932-6203 |
| Popis: | Light fidelity (LiFi) uses different forms of orthogonal frequency division multiplexing (OFDM), including DC biased optical OFDM (DCO-OFDM). In DCO-OFDM, the use of a large DC bias causes optical power inefficiency, while a small bias leads to higher clipping noise. Hence, finding an appropriate DC bias level for DCO-OFDM is important. This paper applies machine learning (ML) algorithms to find optimum DC-bias value for DCO-OFDM based LiFi systems. For this, a dataset is generated for DCO-OFDM using MATLAB tool. Next, ML algorithms are applied using Python programming language. ML is used to find the important attributes of DCO-OFDM that influence the optimum DC bias. It is shown here that the optimum DC bias is a function of several factors including, the minimum, the standard deviation, and the maximum value of the bipolar OFDM signal, and the constellation size. Next, linear and polynomial regression algorithms are successfully applied to predict the optimum DC bias value. Results show that polynomial regression of order 2 can predict the optimum DC bias value with a coefficient of determination of 96.77% which confirms the effectiveness of the prediction. |
| Databáze: | OpenAIRE |
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