Hybrid OFDM-Digital Filter Multiple Access PONs Utilizing Spectrally Overlapped Digital Orthogonal Filtering
Bibliographic record
Abstract
Known hybrid orthogonal frequency division multiplexing-digital filter multiple access (OFDM-DFMA) PONs show promise of seamless and cost-effective convergence of optical and mobile networks for 5G and beyond. This paper reports, for the first time, a new hybrid OFDM-DFMA PON based on intensity modulation and direct detection (IMDD), obtained by modifying digital signal processing (DSP) algorithms embedded in both the OLT and ONUs. The proposed PON allows two spectrally overlapped sub-bands to occupy each individual sub-wavelength spectral region to independently transmit upstream ONU information. A model of the proposed PON is developed and its upstream transmission performances are numerically explored for different application scenarios. Compared with the previously published PON, the proposed PON doubles the number of supported ONUs and provides >1.7-fold aggregate upstream signal transmission capacity increases with2.2-fold aggregate upstream signal transmission capacity increases and >0.7 dB upstream power budget improvements are achievable. The performance improvements vary by <18% for a transmission distance range as large as 50 km. In addition, the proposed PON is tolerant to finite digital filter tap length-induced channel interferences.
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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 | 0.000 |
| 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".