PAPR Reduction and Nonlinearity Mitigation of Optical Digital Subcarrier Multiplexing Systems With a Silicon Photonics Transmitter
Bibliographic record
Abstract
We propose two low-complexity digital signal processing (DSP) techniques to improve the system performance of digital subcarrier multiplexing (DSCM) optical transmission system with a silicon photonics transmitter. We first analyze the impacts of various transmitter parameters on the system performance of the single carrier system versus the DSCM system. We show quantitatively that the DSCM system suffers from a high peak-to-average power ratio (PAPR) and nonlinear transfer functions that cannot be easily compensated for. Furthermore, using a high-driving-voltage silicon photonics modulator exacerbates this penalty at the transmitter. To combat the performance degradation caused by nonideal transmitters, we propose and demonstrate the functionality of an encoding scheme based on the fast Fourier transform (FFT) to decrease the PAPR of the transmitted DSCM signals. Then a simple and effective pre-mapping technique is proposed to compensate for the nonlinearity from the transmitter. After developing the theory of our proposed approach, both DSP blocks are verified with coherent optical transmission simulations and experiments. Using a 64 GBd 4-bit/s/Hz DSCM signal containing 8 subcarriers transmitted over 43.2 km of standard single-mode fiber (SSMF), the FFT encoding achieves a gain of 3.458 dB in terms of link loss, and the pre-mapping achieves a gain of 0.486 dB when compared to the raw DSCM system, at the HD-FEC bit error rate (BER) threshold of 3.8e-3. We also tested the performance of the system when the two techniques are combined. We found that this led to a power budget increase of 4.159 dB at the HD-FEC threshold. Since the total gain is more than the addition of the two gains from each DSP block, there is a gain enhancement effect between the two proposed algorithms that generates extra gain when implemented together. The proposed transmitter algorithms and the overall schematic is favorable to the implementation of DSCM systems when using silicon-photonics-modulator-based transmitters specifically and coherent transmitters generally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".