Feed forward ANN approach for PAPR abatement in an ADO-OFDM VLC system
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Nishant Sharan
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Ajit Kumar
Abstract
Orthogonal frequency division multiplexing (OFDM), is one of the most commonly used multi-carrier modulation strategy in visible light communication (VLC) systems. The integration of OFDM enhances the immunity against ISI and supports high-speed data to a significant number of users. However, the summation of identical phase symbols during the IFFT process shoots the peak power leading to a high peak-to-average power ratio (PAPR) issue in an OFDM-based optical system (O-OFDM). High PAPR has been a considerable matter of concern over the past decade as it negatively impacts the bit error rate (BER) of the system. The work intends to implement the feed-forward artificial neural network (ANN) in an asymmetrically clipped direct current biased optical OFDM (ADO-OFDM) system in order to mitigate the PAPR while preserving the BER performance of the system. The application of a feed-forward network optimizes parameters in real time to minimize PAPR levels and maintain optimal signal quality in an ADO-OFDM system. The feed-forward ADO-OFDM shows a PAPR of 10 dB for a reference CCDF of 10−3 achieving a reduction of 2 dB compared to the typical ADO-OFDM system.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for entire content of the manuscript and approve its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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