Bibliographic record
Abstract
Optical code-division multiple-access (OCDMA) is a technique well-suited for providing the required photonic connectivity in local access networks. Although the principles of OCDMA have been known for many years, it has never delivered on its potential. In this paper, we will describe the key challenges and impediments that have prevented OCDMA from delivering on its potential, as well as discuss possible solutions. We focus on the limitations of one-dimensional codes and the benefit of exploiting the additional degrees of freedom in using multiple dimensions for defining the codes. We discuss the advantages of using differential detection in order to implement bipolar communications. We then show how two-dimensional wavelength-time codes can be appropriately combined with differential detection in order to achieve high performance OCDMA systems with a large number of users operating with good BER performance for a large aggregate capacity. We also discuss the impact of channel coding techniques, for example forward error correction or turbo coding, on BER performance.
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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 | 0.001 | 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 | 0.001 | 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".