Optical link optimization using embedded forward error correcting codes
Bibliographic record
Abstract
The design of a single-chip optical transceiver to optimize the performance of a short-distance optical datalink is proposed. The transceiver includes an embedded hybrid automatic repeat request (ARQ) controller capable of operation at several gigahertz clock rates. The hybrid ARQ controller uses a combination of packet retransmission protocols and forward error correction (FEC) to minimize bit errors and achieve a transmitter power coding gain of several dB. Conventional FEC codes such as Reed-Solomon codes cannot be used due to their excessive hardware cost and delays. A practical multilevel coding scheme is explored. The inner codes consist of small linear block codes with reasonable FEC capability, such as small BCH codes, which can be encoded and decoded with reasonable hardware cost and delay. The outer code for a complete packet consists of a long linear block code with excellent error detection ability, such as a cycle redundancy check code. Low-power pipelined on-chip FEC decoders with estimated throughputs of several hundred gigabits per second per square millimeter are proposed. Mathematical analysis indicates that substantial coding gains are possible, which can be used to increase the data rate or the distance span of the link. The proposed designs can be used in short-distance optical transceivers for 10-Gb ethernet, fiberchannel, and very short reach optical datalinks, and are scalable to future two-dimensional optical datalinks with Terabits of capacity.
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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 | 0.000 | 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 | 0.000 |
| 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".