Turbo DFE Assisted Time-Frequency Packing for Probabilistically Shaped Terabit Superchannels
Bibliographic record
Abstract
We present a probabilistically shaped (PS) time- frequency-packing (TFP) wavelength-division multiplexing superchannel system employing higher order modulation (HoM) formats, with an objective to improve the spectral efficiency (SE). However, TFP systems suffer from inter-symbol interference (ISI) and/or inter-carrier interference (ICI). Additionally, the bandwidth limitations of the wavelength selective switches in the fiber link may cause severe ISI for the edge sub-channels (SCs) in a superchannel. Mitigation of such interferences is par- ticularly challenging for HoM systems, since the implementation of the well-known turbo equalization schemes, such as the Bahl- Cocke-Jelinek-Raviv equalizer, is computationally challenging for larger constellations. In this paper, we investigate an expectation propagation based, computationally efficient, turbo decision feed- back equalizer for ISI cancellation, in tandem with a parallel interference cancellation based ICI mitigation. By optimizing the parameters in the shaping and the packing dimensions, we show through our numerical results that the proposed PS-TFP superchannels enabling 1.8 Tbps data rates offer up to 1.05 dB packing gain over an unpacked system using the same modulation format, and a 1.15 dB shaping gain over an unshaped system that uses a lower modulation order to achieve the same SE.
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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) | 0.000 | 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".