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Record W1540987342 · doi:10.1109/iccw.2015.7247375

A two-dimensional signal space for bandlimited optical intensity channels

2015· article· en· W1540987342 on OpenAlexaff
Dingchen Zhang, Steve Hranilovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBandlimitingBandwidth (computing)AmplitudeSIGNAL (programming language)Spectral densityMathematicsPhysicsIntensity modulationOptical communicationOpticsComputer scienceMathematical analysisFourier transformTelecommunicationsPhase modulationStatistics

Abstract

fetched live from OpenAlex

Bandlimited optical intensity channels, such as visible light communication (VLC) systems, require that all signals satisfy a bandwidth constraint as well as average and non-negativity amplitude constraints. In this paper, a two-dimensional signal space for optical intensity channels is presented in which all signals are strictly bandlimited. A novel feature of this model is that the strict non-negativity constraint is relaxed and the signal space parameterizes the probability that the resulting output amplitude is negative. The motivation for this relaxation is that even though the optical intensity channel only supports non-negative amplitudes, if the likelihood of a negative amplitude excursion is small enough the impact of clipping or biasing on system performance will be negligible. For a given signal space, the probability that the output signal assumes a negative amplitude is rigorously upperbounded and also numerically found with a tractable and tight approximation. The uncoded power and spectral efficiencies are computed for two-dimensional hexagonal lattice constellations. For a given optical power, constellations developed with the new signal space have larger spectral efficiencies over M-PAM using the minimum bandwidth optical intensity Nyquist pulse.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.431

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 head0.042
GPT teacher head0.258
Teacher spread0.216 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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