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Record W2066437020 · doi:10.1364/jocn.4.000173

RPR–EPON–WiMAX Hybrid Network: A Solution for Access and Metro Networks

2012· article· en· W2066437020 on OpenAlexaff
Abdou Ahmed, Abdallah Shami

Bibliographic record

VenueJournal of Optical Communications and Networking · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer networkComputer scienceMedia access controlAccess networkAccess controlWiMAXNetwork packetNetwork architectureAccess network discovery and selection functionWirelessRadio access networkTelecommunicationsBase station

Abstract

fetched live from OpenAlex

The integration of Ethernet passive optical networks (EPONs) with wireless worldwide interoperability for microwave access (WiMAX) is an approved solution for an access network. A resilient packet ring (RPR) is a good candidate for a metro network. Hence RPR, EPON, and WiMAX integration is a viable solution for metro–access network bridging. The present paper examines such integration, including an architecture and a joint media access control (MAC) protocol, as a solution for both access and metro networks. The proposed architecture is reliable due to the dependability of the RPR standard and the protection mechanism employed in the EPON. Moreover, the architecture contains a high fault tolerance against node and connection failure. The suggested MAC protocol includes a multi-level dynamic bandwidth allocation algorithm, a distributed admission control, a scheduler, and a routing algorithm. This MAC protocol aims at maximizing the advantages of the proposed architecture by distributing its functionalities over different parts of the architecture and jointly executing the parts of the MAC protocol.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0010.000
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 head0.062
GPT teacher head0.321
Teacher spread0.259 · 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

Citations34
Published2012
Admission routes1
Has abstractyes

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