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Record W4417248839 · doi:10.1109/mcom.001.2500098

Underwater Multi-Wavelength Optical Links with Blue Targets and Constraints: Opportunities and Challenges

2025· article· W4417248839 on OpenAlexaff
Alain R. Ndjiongue, Octavia A. Dobre, Hyundong Shin, Harald Haas

Bibliographic record

VenueIEEE Communications Magazine · 2025
Typearticle
Language
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsUnderwaterTransmission (telecommunications)Underwater acoustic communicationKeyingOptical wirelessOptical wireless communicationsWirelessFree-space optical communicationOptical performance monitoringOptical communication

Abstract

fetched live from OpenAlex

Underwater optical wireless technologies offer multiple advantages over the acoustic technology. Acoustic signals, for instance, are susceptible to noise from marine sources due to marine life and human activities. This is not the case with optical signals. However, absorption and scattering significantly attenuate optical signals. This limits the communication range and requires higher transmission power or more sensitive receivers to detect transmitted light. Therefore, it is necessary to design underwater optical systems with a higher transmission rate and reduced attenuation. To this end, we introduce a foundational and theoretical framework for designing optical signaling constellations employing multi-wavelength light sources to account for the transmission distance and achievable rate. In particular, we redefine the color-shift keying (CSK) constraint region to target blue light and adapt to marine environments. We discuss an example of 4-point underwater CSK. The corresponding analytical results demonstrate the trade-offs between the symbol error probability, achievable rate, and transmission range of the proposed scheme.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.002
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 head0.082
GPT teacher head0.280
Teacher spread0.197 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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