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 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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".