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Record W2009745523 · doi:10.1002/wcm.318

A performance evaluation of distributed dynamic channel allocation protocols for mobile networks

2006· article· en· W2009745523 on OpenAlexafffund
Azzedine Boukerche, Khalil El‐Khatib, Tingxue Huang

Bibliographic record

VenueWireless Communications and Mobile Computing · 2006
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkBase stationBlocking (statistics)Channel (broadcasting)Mutual exclusionWirelessChannel allocation schemesProtocol (science)Distributed computingResource allocationInterference (communication)ReuseTelecommunications

Abstract

fetched live from OpenAlex

Abstract Technological advances coupled with the proliferation of wireless devices among mobile users require efficient resource management and reuse of the scare radio spectrum allocated to wireless and mobile communication systems. Several channel allocation protocols based on a mutual exclusion paradigm have been developed. However, very little data have been reported to compare these protocols. In this paper, we review four of the best known distributed dynamic channel and resource allocation algorithms based on the mutual exclusion paradigm. While the first three channel alloctaion protocols (Cao et al ., Choy et al . and Prakash et al .) are based on the co‐channel interference, the fourth protocol, which is known as DDRA, adopts the co‐group interference approach. We present an extensive set of simulation experiments to evaluate and compare the performance of these four protocols using realistic scenarios. Our results indicate clearly that DDRA algorithm has shown the shortest response time and highest blocking rate among all of the four channel allocation protocols. Cao et al . algorithm exhibits a better blocking rate when compared to the three other schemes. This is due to the fact that it reuses communication channels optimally. Last, but not least, we discuss the basic fault tolerant machanisms that can be used to enhance further these protocols while dealing with the base stations, mobile hosts, or links' failure. Copyright © 2006 John Wiley & Sons, Ltd.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.823
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.331
Teacher spread0.289 · 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 teacher head, 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

Citations4
Published2006
Admission routes2
Has abstractyes

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