Bandwidth optimization of optical data links by use of error-control codes
Bibliographic record
Abstract
A design approach to optimizing the bandwidth of optical data links while simultaneously decreasing the bit-error rate is proposed. Mathematical analysis indicates that bandwidth gains by factors of 10-60 with power gains of as much as 8.9 dB are possible. To achieve these performance levels requires several innovations. First, conventional forward error-correcting codes cannot be used because of their excessive hardware cost. A reasonably powerful multidimensional parity-based error-control code is proposed and analyzed. These codes offer excellent error detection and moderate error-correction capabilities. Most importantly, they can operate at the fast clock rates that are required. Second, a hybrid automatic-repeat-request protocol is exploited to correct complex error patterns. In thermal-noise-limited systems this unique combination allows the optical clock rate to be increased significantly, thereby resulting in large bandwidth increases. The proposed design approach can be used in optical data links in which propagation delays are moderate and is applicable to fibers that exploit wavelength-division multiplexing or time-division multiplexing, one-dimensional parallel-fiber ribbons, and two-dimensional optical data links that use free space or guided waves. Several design examples are illustrated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".