Data Center Mode Division Multiplexing at Net 1.6 Tb/s Per Wavelength w/o MIMO Processing
Bibliographic record
Abstract
Space division multiplexing (SDM) is a promising solution to increase the capacity of optical communications within data centers. We focus on mode division multiplexing (MDM) architectures with fibers supporting multiple modes. We show that network hardware is significantly less complex for MDM with orbital angular momentum (OAM) compared to MDM with linearly polarized (LP) modes. The advantage stems from low crosstalk among OAM modes, hence limited digital signal processing (DSP) complexity. Unlike demonstrations of MDM with LP, our demonstrations have no multiple-input multiple-output (MIMO) processing. We transmit 16 OAM channels at 56 Gbaud quadrature phase-shift keying (QPSK) over 400 m (distance sufficient for links within data centers). The net throughput of 1.6 Tbit/s per wavelength is the highest reported OAM-MDM throughput, even among demonstrations with much greater DSP complexity. We can span the entire C-band, with throughput of 1.3 Tbit/s at the band edges. To reduce latency and power consumption, 8-channel transmission can achieve a net throughput of 0.8 Tbit/s per wavelength across the C-band with wide margin. The OAM MDM approach can flexibly trade-off throughput, latency, and complexity.
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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) | 0.007 | 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".