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Record W2792494965 · doi:10.1117/12.2290454

All-optical retro-modulation for terabit-per-second free-space optical communication

2018· article· en· W2792494965 on OpenAlexaff
Brandon Born, Ilija R. Hristovski, Simon Geoffroy-Gagnon, Jonathan F. Holzman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTerabitTelecommunications linkTransceiverFree-space optical communicationOptical communicationRetroreflectorComputer scienceModulation (music)Optical linkTransmission (telecommunications)Electronic engineeringLaserRemote sensingOpticsTelecommunicationsPhysicsWavelength-division multiplexingEngineeringOptical fiberWirelessGeography

Abstract

fetched live from OpenAlex

This work puts forward new technologies for free-space optical communications, with emphasis on deployments between ground and aerial transceivers. The proposed system targets the challenges of these aerial-ground links by applying direct laser transmission for the ground-to-aerial active uplink and applying all-optical retro-modulation (AORM) for the aerial-to-ground passive downlink. It is shown that such a system can function with multiple ground transceivers, over wide service coverage, and one aerial transceiver, with low demands for its mass and power. The AORM architecture applied in the passive downlink implements glass S-LAH79 hemispheres for effective retroreflection and CuO nanocrystal semiconductor thin film layer for all-optical modulation on ultrafast timescale. The fabricated AORM architecture is demonstrated to have an system response time of 770 fs, which limits the aggregate data rate. Such a fast system response establishes the possibility of terabit-per-second data rates. Ultimately, these findings can lay the foundation for future laser-based terabit-per-second links between satellites, unmanned aerial vehicles, and high-altitude platforms.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.789

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 science0.0010.000
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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