A Simple Envelope-Assisted RF/IF Digital Predistortion Model for Broadband RoF Fronthaul Transmission
Bibliographic record
Abstract
For emerging 5G fronthaul, broadband radio over fiber (RoF) transmission has been found much more cost-effective than conventional digital fiber transmission. Current digital predistortion (DPD) techniques may not be effective in linearizing broadband RoF links. This work presents a simple envelope-assisted radio frequency (RF)/intermediate frequency (IF) DPD model for linearization of broadband/multiband RoF transmission. The model uses both RF/IF signals and baseband envelopes, resulting in the short-term and long-term memory effect mitigated efficiently even for high signal bandwidth. The model complexity is lower than the existing baseband DPDs and does not increase with the increase of the signal bands. The model is experimentally evaluated in three scenarios: two 20 MHz long-term evolution (LTE) signals spaced by 100 and 40 MHz, and three 20 MHz LTE signals spaced by 50 MHz. It is shown that the proposed DPD results in comparable performance as today's baseband models.
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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 | 0.001 |
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".