Modified Ito Generalization of the Hermite Polynomials for the Linearization of Radio Over Fiber Links With Increased Numerical Stability
Bibliographic record
Abstract
Nonlinearity is a main impairment for radio over fiber (RoF) link as it can severely degrade the signal quality and limit the overall system performance. Digital predistortion (DPD) is an effective technique to linearize an RoF link. In a DPD technique, polynomials are used to model a nonlinear RoF link. However, a conventional polynomial model exhibits numerical instabilities. In this article, we introduce a novel set of orthogonal polynomials based on the modified Ito generalization of the Hermite polynomials to improve the numerical stability. Compared with other orthogonal polynomials, the proposed orthogonal polynomials have two additional advantages. First, the proposed polynomials are orthogonal for an input signal with complex Gaussian distribution. Second, the proposed orthogonal polynomials can be applied to a RoF link for multiband signal transmission. An experiment is performed to evaluate the performance of the proposed approach. Two 100 MHz orthogonal frequency division multiplexing (OFDM) signals at 1.6 and 2.4 GHz are transmitted over a RoF link in which an electro-absorption modulated laser (EML) is employed to perform signal modulation. The experimental results show that a RoF link employing the proposed orthogonal polynomials has a better performance than using the conventional polynomials in terms of error vector magnitude (EVM) and spectral regrowth suppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".