Machine learning for DCO-OFDM based LiFi.
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0259955 |
| _version_ | 1818735891888209920 |
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| author | Krishna Saha Purnita M Rubaiyat Hossain Mondal |
| author_facet | Krishna Saha Purnita M Rubaiyat Hossain Mondal |
| author_sort | Krishna Saha Purnita |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T00:28:28Z |
| format | Article |
| id | doaj.art-baa7cb46d13948cfabb8faab3ba404dd |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T00:28:28Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-baa7cb46d13948cfabb8faab3ba404dd2022-12-21T21:27:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011611e025995510.1371/journal.pone.0259955Machine learning for DCO-OFDM based LiFi.Krishna Saha PurnitaM Rubaiyat Hossain MondalLight 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.https://doi.org/10.1371/journal.pone.0259955 |
| spellingShingle | Krishna Saha Purnita M Rubaiyat Hossain Mondal Machine learning for DCO-OFDM based LiFi. PLoS ONE |
| title | Machine learning for DCO-OFDM based LiFi. |
| title_full | Machine learning for DCO-OFDM based LiFi. |
| title_fullStr | Machine learning for DCO-OFDM based LiFi. |
| title_full_unstemmed | Machine learning for DCO-OFDM based LiFi. |
| title_short | Machine learning for DCO-OFDM based LiFi. |
| title_sort | machine learning for dco ofdm based lifi |
| url | https://doi.org/10.1371/journal.pone.0259955 |
| work_keys_str_mv | AT krishnasahapurnita machinelearningfordcoofdmbasedlifi AT mrubaiyathossainmondal machinelearningfordcoofdmbasedlifi |