Autoencoder-driven PTS for power optimization of optical-NOMA waveform amplifiers
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P. Radhakrishnan
,
Abdul Hussain Sharief
,
Kanishk Sharma
,
Arun Kuma
und
Aziz Nanthaamornphong
Abstract
This paper proposes an autoencoder-based partial transmit sequence (AE-PTS) framework for peak-to-average power ratio (PAPR) reduction and power efficiency enhancement in optical non-orthogonal multiple access (Optical-NOMA) systems with nonlinear optical amplifiers. The encoder learns compact representations of multi-user waveforms, while a differentiable PTS module generates candidate phase-rotated subblocks. A lightweight neural selector optimally minimizes peak power, and the decoder reconstructs compliant optical waveforms under real-valued intensity and non-negativity constraints. Unlike conventional techniques, AE-PTS jointly optimizes phase selection and waveform representation, effectively reducing PAPR, mitigating nonlinear distortion, and improving BER. Extensive MATLAB simulations for 64, 128, and 256 sub-carrier optical-NOMA confirm its robustness. At a CCDF of 10−3, AE-PTS achieves PAPR values of 1.8 dB, 2.8 dB, and 5.2 dB, corresponding to gains of up to 10 dB over baseline optical-NOMA and 2–5 dB over conventional PTS. Capacity analysis shows AE-PTS reaching ∼200 at 50 dB SNR, compared with 100 for baseline, while training accuracy results demonstrate stable convergence across sub-carrier sizes. These simulation results establish AE-PTS as a scalable and energy-efficient solution for future optical-NOMA networks.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: Not applicable.
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Data availability: Not applicable.
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