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Record W2007711371 · doi:10.1364/jocn.4.001008

Binary-Input Non-Line-of-Sight Solar-Blind UV Channels: Modeling, Capacity and Coding

2012· article· en· W2007711371 on OpenAlexaff
Mohamed A. El-Shimy, Steve Hranilovic

Bibliographic record

VenueJournal of Optical Communications and Networking · 2012
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBaudComputer sciencePulse-position modulationCoding (social sciences)Decoding methodsKeyingLine codeElectronic engineeringChannel (broadcasting)Channel capacityBinary numberAlgorithmReal-time computingTelecommunicationsEngineeringMathematicsDetectorPulse (music)Pulse-amplitude modulationStatisticsBasebandBandwidth (computing)

Abstract

fetched live from OpenAlex

There has been recent interest in establishing non-line-of-sight links in the solar-blind ultraviolet region for outdoor optical wireless communications. This paper presents a novel channel model combining both photon propagation and detection statistics. The channel capacity with binary inputs is numerically computed for on–off keying and 4-pulse-position modulation (4-PPM) at different baud rates. To approach the capacity, error control coding is applied and a message passing decoding technique is outlined. Simulation results for a running example through the paper indicate that, at a given power, there is an optimum transmitted baud rate that maximizes the achievable data rate on such links. With the application of proper coding techniques, it is demonstrated that a near fifty-fold increase in rate over previous reported designs for this channel is feasible.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.085
GPT teacher head0.284
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 source (direct Gemma or distilled Codex), 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

Citations49
Published2012
Admission routes1
Has abstractyes

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