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Record W4206548792 · doi:10.1109/jlt.2021.3134155

Performance Oriented DSP for Flexible Long Haul Coherent Transmission

2021· article· en· W4206548792 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Lightwave Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsElectronic engineeringDigital signal processingTransceiverBandwidth (computing)Computer scienceOptical cross-connectOptical fiberSignal processingTransmission (telecommunications)Optical communicationOptical performance monitoringOptical communications repeaterPhysical layerOptical Transport NetworkFiber-optic communicationPassive optical networkFiber optic splitterWavelength-division multiplexingTelecommunicationsEngineeringWirelessFiber optic sensorMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

In long haul optical fiber communication networks, whichcan span thousands of kilometers, bandwidth is at a premium due to the relatively low availability of optical fibers when compared with network traffic demands. Therefore, these networks require the highest performance in the physical layer, with transceivers that are capable of extracting all the available capacity from each optical fiber. Digital coherent transmitters and receivers, which enhance optical transmission systems by using digital signal processing, are essential for achieving this goal. This tutorial discusses the digital signal processing techniques that are used in the design of high performance coherent modems to compensate for adverse channel effects such as fiber impairments and optoelectronic device non-idealities.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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