A Data-Driven Digital Demodulator Based on Deep Learning for Radio Over Fiber Transmission System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . Furthermore, our proposed demodulator outperforms other demodulators for the RoF system with wireless transmission at different received optical powers and wireless distances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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