DC bias optimization in intelligent DCO-OFDM Li-Fi systems using hybrid machine learning with hardware validation

score of 81.37%, consistent with the simulation outcomes and demonstrating its robustness in real-time scenarios. These findings suggest that hybrid polynomial regression with KNN can be an effective technique for predicting the optimal DC bias value in DCO-OFDM-based Li-Fi systems.

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Publication Details

Journal
Scientific Reports
Published
2026-05-08
DOI
https://doi.org/10.1038/s41598-026-49732-4
Primary Topic
Optical Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
0.00

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article

DC bias optimization in intelligent DCO-OFDM Li-Fi systems using hybrid machine learning with hardware validation

Dina A. Ragab, Mohamed Hussien Moharam, Esraa Abdelhakim, Mohamed Abaza
Scientific Reports
Optical Wireless Communication Technologies
article

DC bias optimization in intelligent DCO-OFDM Li-Fi systems using hybrid machine learning with hardware validation

Dina A. Ragab, Mohamed Hussien Moharam, Esraa Abdelhakim, Mohamed Abaza
article en

Abstract

score of 81.37%, consistent with the simulation outcomes and demonstrating its robustness in real-time scenarios. These findings suggest that hybrid polynomial regression with KNN can be an effective technique for predicting the optimal DC bias value in DCO-OFDM-based Li-Fi systems.

Scientific ReportsVol. 16(1)
Misr University for Science and Technology (EG), Arab Academy for Science, Technology, and Maritime Transport (EG)
Science and Technology Development Fund
Openalex Percentile: Top 9%
Optical Wireless Communication Technologies
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