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Record W2111431392

Capacity Bounds for Power- and Band-Limited Wireless Infrared Channels Corrupted by Gaussian Noise

2002· article· en· W2111431392 on OpenAlexaff
Steve Hranilovic, Frank R. Kschischang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMathematicsGaussian noiseBandwidth (computing)Disjoint setsUpper and lower boundsChannel capacityShot noiseChannel (broadcasting)Noise spectral densityAdditive white Gaussian noiseGaussianNoise (video)White noiseTelecommunicationsAlgorithmDiscrete mathematicsPhysicsMathematical analysisComputer scienceNoise figureStatisticsQuantum mechanicsDetector
DOInot available

Abstract

fetched live from OpenAlex

This paper finds asymptotically exact upper and lower bounds on the channel capacity of power and band-limited optical intensity channels corrupted by white Gaussian noise. This work differs from the oft investigated case of the Poisson photon counting channel in that not only are rectangular pulse amplitude schemes considered, but results for more general time-disjoint intensity modulation schemes are presented. The role of bandwidth is expressed by way of the effective dimension of the set of signals. The bounds show that at high optical signal-to-noise ratios the use of bandwidth efficient pulse sets is essential to achieve high spectral efficiencies. This result can be considered as an extension of previous work on photon counting channels which more closely model low optical intensity channels. 1

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0010.005
Scholarly communication0.0040.006
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.211
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2002
Admission routes1
Has abstractyes

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