A DNN-Based Digital Predistortion for Harmonically-Driven Radio-Over-Fiber Systems
Bibliographic record
Abstract
Radio-over-fiber (RoF) systems are integral to 5G and beyond centralized radio access networks (C-RAN). They enable the transport of radio signals from the Central Baseband Unit (BBU) to Remote Radio Heads (RRHs) for subsequent delivery to mobile users. This paper presents an application of neural network-based digital predistortion (DPD) to dual-band RoF systems operating at harmonically related frequencies (1.8 GHz and 3.6 GHz). The proposed architecture employs augmented input features that explicitly capture and mitigate harmonic distortion products through the inclusion of relevant crossterms, enabling superior linearization compared to conventional polynomial models and standard neural networks. Experimental validation demonstrates that the proposed approach achieves −39.70 dB NMSE and −49.97 dBc ACPR while outperforming convolutional neural networks by over 3 dB in linearity despite using fewer parameters.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".