MétaCan
Menu
Back to cohort
Record W3021660881 · doi:10.1364/jocn.391945

From 25  Gb/s to 50  Gb/s TDM PON: transceiver architectures, their performance, standardization aspects, and cost modeling

2020· article· en· W3021660881 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 Optical Communications and Networking · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsNokia (Canada)
Fundersnot available
KeywordsTransceiverStandardizationComputer scienceTime-division multiplexingPassive optical networkTelecommunicationsComputer networkWavelength-division multiplexingMultiplexingOptoelectronicsPhysicsOperating system

Abstract

fetched live from OpenAlex

Standardization activities are nearly complete for single wavelength 25 Gb/s time-division multiplexed (TDM) passive optical networks (PONs) and well underway for 50 Gb/s TDM PONs. There is considerable debate in the industry about which technology will be the “next step” after 10 Gb/s TDM PON, now finally starting to ramp up to mass deployment. 50 Gb/s PON clearly brings a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> <mml:mo>×</mml:mo> </mml:math> bandwidth advantage over 25 Gb/s, at least in the downstream direction. On the other hand, the increase of speed to 50 Gb/s brings with it a substantial receiver sensitivity penalty of at least 4 dB, which has a chain effect on transceiver architecture, cost, and time-to-market. In this paper, each of those elements is investigated, quantified, and compared to 25 Gb/s.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.255
Teacher spread0.215 · 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