Adaptive asymmetric linearization of radio over fiber links for wireless access
Bibliographic record
Abstract
The biggest concern in the use of radio-over-fiber (ROF) links in wireless access is their limited dynamic range due to nonlinear distortion (NLD). In this paper, a higher order adaptive filter based nonlinearity compensation scheme is proposed. Pre-compensation is done for the downlink while post-compensation is done for the uplink to result in asymmetry with respect to complexity. This centralized signal processing is attractive in that it keeps the remote unit simple. Accurate measurements of ROF link parameters are not required with this approach because the filters are adapted from the distortion of the input/output base band signal. This technique also facilitates fast tracking of modifications and drifts in the link characteristics. Measurements and simulation results show that gradually saturating amplitude nonlinearity can be adequately linearized with some backoff from the clipping limit. A 42% backoff is required for pre-compensation to protect the laser while only a 16.7% backoff is required for post-compensation. Phase pre-compensation is accomplished with a higher accuracy than phase post-compensation.
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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 | 0.001 |
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".