Using Standard 2×2 MIMO to Increase Capacity of Spatial Multiplexing with OAM Modes
Bibliographic record
Abstract
Modal multiplexing can greatly increase fiber capacity, but often at the cost of high digital processing with multiple-input multiple-output (MIMO) algorithms. The use of orbital angular momentum (OAM) for multiplexing can support MIMO-free reception, but it always operates in an interferencelimited regime. Many previous MIMO-free demonstrations for OAM used a type of optical MIMO that is impractical for deployed systems. We compare the performance of MIMO-free and standard electronic 2 × 2 MIMO in a demonstration with no recourse to optical MIMO. We show that the receiver complexity of these two reception strategies is very similar in hardware and signal processing. We explore commercial coherent receivers with 2 × 2 MIMO, hence this solution for OAM demultiplexing can be deployed effectively with current technology. The 2 × 2 MIMO reception enhances the total bit rate and is more compatible with wavelength division multiplexing (WDM) as it equalizes performance across the C-band. At 1.3 km transmission, the net bit rate spans 426–450 Gb/s across wavelengths with 2 × 2 MIMO, while variability is high for MIMO-free reception, spanning 200–418 Gb/s.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".