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Record W4319663618 · doi:10.1109/jlt.2023.3243864

Analysis of Bandwidth Narrowing in Wavelength Selective Switch Enabled DP-64QAM Systems With Transceiver Noise

2023· article· en· W4319663618 on OpenAlexafffund
Ankita Jain, John C. Cartledge

Bibliographic record

VenueJournal of Lightwave Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Network Technologies
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransceiverQuadrature amplitude modulationElectronic engineeringBandwidth (computing)MultiplexingPhase noiseWavelength-division multiplexingNoise figureComputer scienceBit error ratePhysicsChannel (broadcasting)OpticsWavelengthTelecommunicationsEngineeringAmplifier

Abstract

fetched live from OpenAlex

The impact of filtering impairments resulting from cascaded, wavelength selective switches is investigated for 32 Gbaud dual-polarization 64-ary quadrature amplitude modulation in the presence of transceiver noise. Training-sequence based adaptive equalization and pilot-aided carrier phase estimation are employed to cope with the combined effects of the transceiver-imposed limitation in achievable signal-to-noise ratio and the filtering-induced signal distortion. For a given level of amplified spontaneous emission noise, transceiver noise reduces the total signal-to-noise ratio resulting in an increased dependence of the bit-wise achievable information rate on filtering-induced bandwidth narrowing. The implications of statistical variations in the overall filter frequency response are also assessed by considering randomly selected responses for each of the individual filters in a cascade. Further, the impact of the frequency response of the demultiplexing filter at the receiver is investigated by considering a 3-channel 32 Gbaud wavelength division multiplexed system with a channel spacing of 37.5 GHz.

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 score0.047
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.009
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.206
Teacher spread0.199 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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