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Record W2135287429 · doi:10.1109/tcomm.2010.01.080142

On space-time coding for free-space optical systems

2010· article· en· W2135287429 on OpenAlexaff
Ehsan Bayaki, Robert Schober

Bibliographic record

VenueIEEE Transactions on Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFadingPairwise error probabilityFree-space optical communicationCoding gainComputer scienceCoding (social sciences)Optical communicationElectronic engineeringCommunications systemSpace–time codeOpticsDecoding methodsMathematicsAlgorithmTelecommunicationsPhysicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Atmospheric turbulence-induced fading is one of the main impairments affecting free-space optics (FSO) communications. In this paper, we consider FSO systems with intensity modulation and direct detection (IM/DD) and derive a closed- form expression for the asymptotic pairwise error probability of general FSO space-time codes (STCs) for two lasers and an arbitrary number of photodetectors for channels suffering from Gamma-Gamma fading. Furthermore, we provide a simple design criterion for FSO STCs which is used to establish the quasi-optimality of previously proposed FSO repetition codes. We also show that STCs optimized for RF systems achieve full diversity in FSO systems but are suboptimal as far as the coding gain is concerned. Simulation results confirm the analytical findings of this paper.

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)0.007
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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.253
Teacher spread0.231 · 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
GenreMethods

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

Citations68
Published2010
Admission routes1
Has abstractyes

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