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Record W4362591253 · doi:10.1007/s11082-023-04742-8

OCDMA transmission-based underwater wireless optical communication system: performance analysis

2023· article· en· W4362591253 on OpenAlexaff
Somia A. Abd El-Mottaleb, Mehtab Singh, Ahmad Atieh, Moustafa H. Aly

Bibliographic record

VenueOptical and Quantum Electronics · 2023
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsOptiwave Systems (Canada)
FundersArab Academy for Science, Technology and Maritime Transport
KeywordsBit error rateAlgorithmOptical wirelessComputer scienceTransmission (telecommunications)Extinction ratioTelecommunicationsOpticsPhysicsWirelessDecoding methods

Abstract

fetched live from OpenAlex

Abstract This paper introduces a novel underwater (UW) optical communication system that utilizes optical code division multiple access transmission technique using the permutation vector code. Three scenarios of water are considered, pure sea (PS), clear ocean (CL), and coastal ocean (CO). The performance is evaluated analytically in terms of bit error rate (BER), received power, signal to noise ratio for different UW links and data rates. The results show that the shortest UW range is achieved in the case of CO, achieving the highest extinction ratio compared to CL and PS. Considering a BER below the forward error correction (FEC) limit of 3 × $${10}^{-3}$$ 10 - 3 , the maximum UW ranges reached are 21 m for PS, 12 m for CL, and 8 m for CO at 3 Gbps with 20 dBm transmitted power. The propagation range could be increased when the transmitted power is increased to 25 dBm, achieving the ranges of 31 m, 18 m, and 9 m, for PS, CL, and CO, respectively.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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