Experimental demonstrations of DSP-enabled flexibility, adaptability and elasticity of multi-channel >72Gb/s over 25 km IMDD transmission systems
Bibliographic record
Abstract
DSP-enabled multi-channel aggregation techniques are promising for cost-effectively improving the flexibility, adaptability and elasticity of fronthaul transport networks. By utilizing orthogonal digital filtering in multi-channel aggregation in IMDD transmission systems, two DSP-enabled matching filter (MF)-free multi-channel aggregation techniques respectively based on SSB OFDM and orthogonal DSB OFDM have been reported; however, the SSB (DSB) technique has a drawback of relatively high digital filter DSP complexity (reduced adaptability to physical layer system characteristics). To effectively overcome these drawbacks associated with these two techniques, in this paper, a DSP-enabled MF-free adaptively variable SSB/DSB OFDM multi-channel aggregation technique is proposed and experimentally demonstrated, in which >72Gb/s@25 km IMDD transmissions have been achieved. This work also evaluates, for the first time, the flexibility, adaptability, and elasticity of the orthogonal digital filtering-enabled multi-channel aggregation techniques. The results show that the proposed technique not only maintains the SSB technique’s excellent adaptability but also possesses the DSB technique’s low digital filter DSP complexity features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".