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Record W1568917656 · doi:10.1002/9781118684207.ch9

Channel Multiplexing Techniques

2014· other· en· W1568917656 on OpenAlexaff
M. Jamal Deen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMultiplexingWavelength-division multiplexingOrthogonal frequency-division multiplexingStatistical time division multiplexingElectronic engineeringTime-division multiplexingFrequency-division multiplexingComputer scienceBandwidth (computing)Channel (broadcasting)Channel spacingPolarization-division multiplexingTelecommunicationsComputer networkEngineeringPhysicsOptoelectronicsWavelength

Abstract

fetched live from OpenAlex

To utilize the full bandwidth of the fiber, several channels can be multiplexed and they can share the same fiber channel. An EDFA operating in C-band (1530–1565 nm) has a bandwidth of about 4.3 THz and, therefore, several channels can be amplified simultaneously by a single amplifier. The multiplexing techniques can be divided into three types: (i) polarization division multiplexing (PDM) or polarization multiplexing (PM), (ii) frequency or wavelength-division multiplexing (WDM), (iii) time-division multiplexing (TDM).Orthogonal frequency-division multiplexing (OFDM) has drawn significant research interest in optical communications recently. OFDM is widely used in wired and wireless communication systems because it is resilient to ISI caused by dispersive channels. This chapter presents a detailed discussion on these channel multiplexing techniques.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.008

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2014
Admission routes1
Has abstractyes

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