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Record W2121938034 · doi:10.1109/glocom.2005.1578374

On the optimal number of hops in infrastructure-based fixed relay networks

2005· article· en· W2121938034 on OpenAlexaff
Alina Alexandra Florea, Halim Yanıkömeroğlu

Bibliographic record

VenueGLOBECOM '05. IEEE Global Telecommunications Conference, 2005. · 2005
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsHop (telecommunications)RelayComputer scienceComputer networkWireless networkSpectral efficiencyWirelessTopology (electrical circuits)MathematicsTelecommunicationsChannel (broadcasting)Power (physics)

Abstract

fetched live from OpenAlex

In this paper we analyze the optimal number and locations of fixed radio relay stations forming multi-hop links in infrastructure-based wireless networks. Under the assumptions of using orthogonal channels for the hops and all links having the same average path loss exponent, we show using the spectral efficiency as a metric that the optimal relay locations are at equal intervals along the straight line between source and destination whenever multihop is to be utilized. We show that a single-hop fixed radio link can be efficiently (from a spectral efficiency perspective) replaced with the multi-hop link only if the single-hop SNR has a relatively low value. We introduce a "multi-hop criterion" to quantify the single-hop SNR value below which a more efficient multi-hop replacement exists. We also determine the optimal number of hops for the multi-hop links having relays disposed in straight line at equal intervals

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)0.002
metaresearch head score (Gemma)0.010
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
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 head0.025
GPT teacher head0.287
Teacher spread0.261 · 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

Citations54
Published2005
Admission routes1
Has abstractyes

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