A Data-Driven Digital Demodulator Based on Deep Learning for Radio Over Fiber Transmission System
Bibliographic record
Abstract
A data-driven digital demodulator based on a Fourier layer Transformer network (FTnet) for radio over fiber (RoF) transmission system with quadrature amplitude modulation (QAM) is developed and experimentally demonstrated. The FTnet combines the Transformer encoder with the Fourier layer to learn the waveforms and directly recover the bitstreams from the impaired received signals. The FTnet-based demodulator does not rely on a series of digital demodulation algorithms such as frequency offset compensation, down-conversion, equalization, and decoding, making the process more efficient and accurate. The 10 GHz 2 Gsym/s 25 km RoF transmission systems are established to evaluate the proposed FTnet-based digital demodulator experimentally. The results show that the bit error rates (BERs) performance of the proposed demodulator for the 16-QAM RoF is better than the ones based on a fully connected neural network, Transformer, and traditional digital demodulator with the least mean square error equalizer (TDD-LMS). The optical receiving sensitivity for the 64-QAM RoF system based on the proposed demodulator is improved by 3 dB compared to TDD-LMS under a BER limit of 3.8 × 10−3. Furthermore, our proposed demodulator outperforms other demodulators for the RoF system with wireless transmission at different received optical powers and wireless distances.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".