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Record W4376274223 · doi:10.1364/ol.491449

Simple polarization-insensitive coherent RoF link with increased capacity

2023· article· en· W4376274223 on OpenAlexafffund
Long Huang, Zhenguo Lü, Ke Wu, Jianping Yao

Bibliographic record

VenueOptics Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsPolytechnique MontréalNational Research Council CanadaUniversity of Ottawa
FundersNational Research Council Canada
KeywordsOpticsPhysicsLocal oscillatorTransmitterOrthogonal polarization spectral imagingPolarization (electrochemistry)Phase noiseTelecommunicationsComputer scienceLaser

Abstract

fetched live from OpenAlex

A simple polarization-insensitive coherent radio-over-fiber (RoF) link with increased spectrum efficiency and transmission capacity is proposed and demonstrated. Instead of using two polarization splitters (PBSs), two 90° hybrids, and four pairs of balanced photodetectors (PDs) in a conventional polarization-diversity coherent receiver (PDCR), a simplified PDCR with only one PBS, one optical coupler (OC), and two PDs is employed in the coherent RoF link. At the simplified receiver, a novel, to the best of our knowledge, digital signal processing (DSP) algorithm is proposed to achieve polarization-insensitive detection and demultiplexing of two spectrally overlapping microwave vector signals as well as the elimination of the joint phase noise originating from the transmitter and the local oscillator (LO) laser sources. An experiment is performed. The transmission and detection of two independent 16QAM microwave vector signals at identical microwave carrier frequencies of 3 GHz with a symbol rate of 0.5 GSym/s over a 25-km single-mode fiber (SMF) is demonstrated. Thanks to the spectrum superposition of the two microwave vector signals, the spectral efficiency as well as the data transmission capacity is increased.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.524

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 integrity0.0000.000
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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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
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

Citations3
Published2023
Admission routes2
Has abstractyes

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