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Record W2100181015 · doi:10.1109/t-wc.2008.071352

Relay-assisted free-space optical communication

2008· article· en· W2100181015 on OpenAlexaff
Majid Safari, Murat Uysal

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFadingRelayComputer scienceTransmission (telecommunications)Path lossMaximal-ratio combiningChannel (broadcasting)TelecommunicationsTopology (electrical circuits)Outage probabilityMonte Carlo methodDiversity gainRelay channelFree-space optical communicationComputer networkWirelessAlgorithmElectronic engineeringOptical communicationStatisticsMathematicsPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this paper, we present relay-assisted transmission as a powerful fading mitigation tool for free-space optical systems operating in atmospheric turbulence channels. We study both serial (i.e., multi-hop transmission) and parallel (i.e., cooperative diversity) relaying encoupled with amplify-and-forward and decode-and-forward modes. We consider an aggregated channel model which takes into account both path-loss and turbulence-induced log-normal fading. Since fading variance is distance-dependent in free-space optical systems, relay-assisted transmission takes advantage of the resulting shorter hops and yields significant performance improvements. We derive outage probability of the relaying schemes under consideration which are further confirmed through Monte-Carlo simulations. Our outage probability analysis demonstrates that an impressive performance improvement of 18.5 dB is possible with the use of a single relay at a target outage probability of 10-6.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.250
Teacher spread0.216 · 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

Citations566
Published2008
Admission routes1
Has abstractyes

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