Record W3135179754 · doi:10.1364/ol.418690

Simple SSMF-based two-channel MGDM system operating in the 0.8 µm wavelength region

2021· article· lv· W3135179754 on OpenAlexaff

Sunghyun Bae, Byung Gon Kim, Minsik Kim, Y. C. Chung

Bibliographic record

VenueOptics Letters · 2021

Typearticle

Languagelv

FieldEngineering

TopicOptical Network Technologies

Canadian institutionsKootenay Association for Science & Technology

FundersInstitute for Information and Communications Technology Promotion

KeywordsOpticsWavelengthChannel (broadcasting)Simple (philosophy)Wavelength-division multiplexingMaterials scienceChannel spacingPhysicsTelecommunicationsComputer science

DOI10.1364/ol.418690

Abstract

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We propose a simple two-channel mode-group-division-multiplexing (MGDM) system operating in the 0.8 µm region over the standard single-mode fiber (SSMF). For the cost-effectiveness, we implement its receiver by using only two photodetectors (PDs) [instead of three PDs required for the detection of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> <mml:mo>,</mml:mo> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>11</mml:mn> <mml:mi>a</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>11</mml:mn> <mml:mi>b</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> modes]. We then detect the signal carried by the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> mode by using a PD and a mode filter. On the other hand, the other signal carried by the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>11</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> mode group is detected by using another PD and a multiple-input single-output equalizer (i.e., by subtracting the signal carried by the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> mode from the multiplexed signal). For a demonstration, we transmit <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>×</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>28</mml:mn> </mml:mrow> </mml:mrow> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">G</mml:mi> <mml:mi mathvariant="normal">b</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> </mml:mrow> </mml:math> MGDM on-off keying signal operating at 852.6 nm over 2.2 km of the SSMF by using the proposed technique. The results show that we can achieve the bit-error rate of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo><</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3.8</mml:mn> </mml:mrow> <mml:mo>×</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> for both the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>01</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">P</mml:mi> </mml:mrow> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>11</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> modes.

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)0.000

Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated

Candidate categoriesMeta-epidemiology (narrow)

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 score0.040

Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

Category	Codex	Gemma
Metaresearch	0.000	0.000
Meta-epidemiology (narrow)	0.000	0.000
Meta-epidemiology (broad)	0.000	0.000
Bibliometrics	0.000	0.001
Science and technology studies	0.000	0.000
Scholarly communication	0.000	0.000
Open science	0.001	0.000
Research integrity	0.000	0.001
Insufficient payload (model declined to judge)	0.000	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.

Opus teacher head0.017

GPT teacher head0.220

Teacher spread0.204 · 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.

Meta-epidemiology (narrow)

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

Citations2

Published2021

Admission routes1

Has abstractyes

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