A hybrid deep learning and companding technique for distortion-resilient optical NOMA VLC with 1024-QAM modulation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Visible light communication (VLC) has gained significant attention as a next-generation wireless technology due to its unlicensed bandwidth, high security, and seamless integration with LED illumination. To support high data rates and user scalability, power-domain non-orthogonal multiple access (NOMA) with 1024-QAM modulation enhances spectral efficiency; however, this combination introduces high peak-to-average power ratio (PAPR), resulting in nonlinear signal distortion and degraded bit error rate (BER) performance in intensity-modulated optical systems. To overcome these limitations, this paper proposes a deep learning-based nonlinear companding framework for waveform optimization in Optical NOMA. The proposed technique significantly reduces PAPR, achieving 3.2 dB, 4.4 dB, and 5.3 dB at a CCDF of 10 −3 for 256, 512, and 1024 subcarriers, respectively, outperforming A-Law, μ-Law, and PTS by up to 7.5 dB improvement. BER analysis further confirms its superiority, achieving a BER of 10 −3 at only 8.9 dB SNR, compared to 15.2 dB (A-Law), 13.1 dB (μ-Law), 11.4 dB (PTS), and 17.8 dB for the unprocessed Optical NOMA waveform. These results demonstrate improved power efficiency, reduced nonlinear distortion, and enhanced detection reliability, making the proposed method a strong candidate for high-speed VLC-based NOMA systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it