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Link Budget Analysis for Free-Space Optical Satellite Networks

2022· article· en· W4225157600 on OpenAlexafffund
Jintao Liang, Aizaz U. Chaudhry, Eylem Erdoğan, Halim Yanıkömeroğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsCarleton University
FundersNational Research Council Canada
KeywordsTelecommunications linkOptical linkLink budgetSatelliteTransmission (telecommunications)Computer scienceMargin (machine learning)Computer networkFree-space optical communicationLink (geometry)Electronic engineeringTelecommunicationsOptical communicationEngineeringOptical fiberAerospace engineeringWireless

Abstract

fetched live from OpenAlex

Free-space optical satellite networks (FSOSNs) will employ free-space optical links between satellites and between satellites and ground stations, and the link budget for optical inter-satellite links and optical uplink/downlink is analyzed in this paper. The satellites in these FSOSNs will have limited energy and thereby limited power, and we investigate the effect of link distance and link margin on optical inter-satellite link transmission power, and the effect of slant distance, elevation angle, and link margin on optical uplink/downlink transmission power. We model these optical links and compute the results for various parameters. We observe that the transmission power increases when the link distance increases for inter-satellite and uplink/downlink communications, while the transmission power decreases when the elevation angle increases for uplink/downlink transmission. We also observe an inverse relationship between link margin and link distance. Furthermore, we highlight some practical insights and design guidelines gained from this analysis.

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)0.005
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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

Citations48
Published2022
Admission routes2
Has abstractyes

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