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Record W2285963488

Dual-Trigger Handover Algorithm for WiMAX Technology

2011· article· en· W2285963488 on OpenAlexaff
Nabil Al-Rousan, Omar Altrad, Ljiljana Trajković

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWiMAXHandoverComputer scienceComputer networkNode (physics)Base stationSoft handoverAlgorithmInteroperabilityReal-time computingWirelessEngineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

IEEE 802.16e is a Worldwide Interoperability Microwave Access (WiMAX) standard that supports mobility. Handover is one of the most important factors that affect the performance of a WiMAX network. Various handover schemes have been proposed and implemented. In this paper, we propose Dual-Trigger Handover (DTHO) algorithm for WiMAX networks. The proposed handover algorithm depends on the computation of signal to noise ratio (SNR) received at the Mobile Station (MS) from various Base Stations (BSs). Relying on SNR measurements and free capacity measurements of the serving BS and the target BS improves the accuracy of handover decisions. The handover is not triggered by the MS node or the BS node individually. Instead, it is a combined decision between the two nodes. The proposed algorithm is implemented in both MS and BS nodes. We implemented the proposed algorithm using OPNET Modeler version 14 running on Windows operating system. The algorithm was simulated using multiple scenarios with various channel parameters.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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