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Record W1965546224 · doi:10.1109/lpt.2012.2216259

Polarization-Time Code and 4$\,\times\,$4 Equalizer-Decoder for Coherent Optical Transmission

2012· article· en· W1965546224 on OpenAlexaff
Mahdi Zamani, Chuandong Li, Zhuhong Zhang

Bibliographic record

VenueIEEE Photonics Technology Letters · 2012
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsDemodulationEqualizerComputer scienceAdaptive equalizerPolarization mode dispersionElectronic engineeringPolarization-division multiplexingMultiplexingPolarization (electrochemistry)Optical communicationDifferential group delayOpticsPhysicsSignal processingTelecommunicationsOptical fiberEngineering

Abstract

fetched live from OpenAlex

A linear polarization-time code in conjunction with an adaptive multi-tap 4 4 joint equalizer-decoder is proposed to average the effects of polarization-dependent impairments on two polarizations. For the first time in the context of polarization-time coding, there is no assumption of polarization-dependent loss (PDL) of knowledge at the receiver. This method compensates the performance loss penalty due to polarization-dependent impairments in polarization division multiplexed coherent optical single-carrier systems. Using the proposed method, a 1.25-dB optical signal-to-noise ratio gain is achieved in the presence of 6-dB PDL. The proposed 44 joint equalizer-decoder has twice the complexity of conventional time-domain equalizers. Further changes at the receiver including the demodulator are not required. Since the outputs of the equalizer are noisy quadrature phase shift keying signals, adoption of the method to work with both hard and soft forward error corrections is straightforward.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.010
GPT teacher head0.230
Teacher spread0.221 · 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.

Study designBench or experimental
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

Citations9
Published2012
Admission routes1
Has abstractyes

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