Low Complexity Digital Predistortion for Multiband Radio Over Fiber Systems
Bibliographic record
Abstract
Nonlinear distortion is one of the limiting factors in radio over fiber (RoF) transmission systems. To suppress the nonlinear distortion, digital predistortion (DPD) has been investigated considerably. However, DPD for multiband signals becomes very complex. In this work, a new low-complexity multiband DPD is proposed, in which in-band and out-of-band distortions are separated and the out-of-band distortion is evaluated by sum and differences of all input signals instead of all individual input signals; thus, complexity is reduced. A five-band 20-MHz 64-quadrature amplitude modulation (QAM) orthogonal frequency division multiplexing (OFDM) signals over an 8-km RoF link with the DPD is tested. The average improvement of error vector magnitude (EVM) is 8.1 dB. The model is further validated by simulation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".