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Record W2097572927 · doi:10.1109/glocom.2008.ecp.549

Compressive Sensing Receiver for Free-Space Optical Communication Through the Atmosphere

2008· article· en· W2097572927 on OpenAlexaff
Mohamed D. A. Mohamed, Steve Hranilovic

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSparse and Compressive Sensing Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDetectorPreamplifierAntenna diversityDigital micromirror deviceOpticsCompressed sensingBit error rateElectronic engineeringComputer scienceFree-space optical communicationSIGNAL (programming language)Optical communicationChannel (broadcasting)PhysicsTelecommunicationsBandwidth (computing)EngineeringWirelessAlgorithm

Abstract

fetched live from OpenAlex

This paper presents a novel application of compressive sensing (CS) to provide spatial diversity for free-space optical communication links. A CS receiver estimates the focal-plane signal distribution due to atmospheric turbulence. This estimate is then employed by a digital micromirror device to select the portions of the focal plane which contain significant energy for detection. The CS receiver is less complex, requires fewer highspeed optoelectronic components, has lower preamplifier noise and can operate at higher rates than a comparable multiple-detector array receiver. Bit-error rates are simulated for a photon counting channel and performance improvements near 2.4 dB can be obtained over a single detector receiver.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.242
Teacher spread0.212 · 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 designBench or experimental
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

Citations3
Published2008
Admission routes1
Has abstractyes

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