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Enabling High Capacity Flexible Optical Backhaul Data Transmission Using PDM-OAM Multiplexing

2024· article· en· W4408325561 on OpenAlexaff
Mehtab Singh, Abdellah Chehri, Ahmad Atieh, Hassan Yousif Ahmed, Medien Zeghid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsOptiwave Systems (Canada)Royal Military College of Canada
Fundersnot available
KeywordsBackhaul (telecommunications)MultiplexingComputer scienceTransmission (telecommunications)Wavelength-division multiplexingOptical performance monitoringElectronic engineeringComputer networkWirelessOptoelectronicsTelecommunicationsMaterials scienceEngineering

Abstract

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This study presents and investigates a free space optics (FSO) transmission technique that utilizes polarization division multiplexing (PDM) and orbital angular momentum (OAM) multiplexing. The aim is to provide an effective and reliable backhauling solution. The transmission system uses four-level-pulse amplitude modulation (PAM-4) signals achieving a high capacity of 400 Gbps. Two orthogonal polarized beams of 1550 nm Laser diode are used. For each polarized beam, 4 OAM beams are further generated, and each OAM beam transports 50 Gbps data using PAM-4 modulation over the free space channel. The impact of varying FSO ranges on the system performance under clear weather sky (sunny), light rain (LR), medium rain (MR), and heavy rain (HR) is considered. The bit error rate (BER) metric is used to evaluate the proposed system's performance. Results from the simulation indicate that transmission of 400 Gbps is achieved successfully with a range of 1800 m, 900 m, 775 m, and 537 m under clear weather, LR, MR, and HF, respectively, with an acceptable Log(BER) of -2.42 for ultra-FEC limit.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score0.884

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 communication0.0000.001
Open science0.0010.000
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 head0.086
GPT teacher head0.279
Teacher spread0.192 · 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
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

Citations5
Published2024
Admission routes1
Has abstractyes

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