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Record W1561271547 · doi:10.1109/twc.2015.2434945

Relay Placement for FSO Multihop DF Systems With Link Obstacles and Infeasible Regions

2015· article· en· W1561271547 on OpenAlexaff
Bingcheng Zhu, Julian Cheng, Mohamed‐Slim Alouini, Lenan Wu

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2015
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsRelayComputer scienceRelay channelOptimization problemMathematical optimizationWirelessOutage probabilityTopology (electrical circuits)Computer networkMathematicsAlgorithmTelecommunicationsFadingPower (physics)Channel (broadcasting)

Abstract

fetched live from OpenAlex

Optimal relay placement is studied for free-space optical multihop communication with link obstacles and infeasible regions. An optimal relay placement scheme is proposed to achieve the lowest outage probability, enable the links to bypass obstacles of various geometric shapes, and place the relay nodes in specified available regions. When the number of relay nodes is large, the searching space can grow exponentially, and thus, a grouping optimization technique is proposed to reduce the searching time. We numerically demonstrate that the grouping optimization can provide suboptimal solutions close to the optimal solutions, but the average searching time linearly grows with the number of relay nodes. Two useful theorems are presented to reveal insights into the optimal relay locations. Simulation results show that our proposed optimization framework can effectively provide desirable solution to the problem of optimal relay nodes placement.

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)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.057
GPT teacher head0.271
Teacher spread0.214 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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