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Record W2106459237 · doi:10.1109/jstqe.2003.813314

Optical link optimization using embedded forward error correcting codes

2003· article· en· W2106459237 on OpenAlexaff
Ted H. Szymanski

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2003
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceForward error correctionError detection and correctionReed–Solomon error correctionRetransmissionLow-density parity-check codeCoding gainAutomatic repeat requestHybrid automatic repeat requestComputer hardwareNetwork packetElectronic engineeringConcatenated error correction codeBlock codeAlgorithmDecoding methodsComputer networkTelecommunications link

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.261
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2003
Admission routes1
Has abstractyes

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