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Record W4254124099 · doi:10.1002/wcm.577

An efficient distributed fault‐tolerant protocol for dynamic channel allocation

2008· article· en· W4254124099 on OpenAlexaff
Tingxue Huang, Azzedine Boukerche, Kaouther Abrougui, Jeff Williams

Bibliographic record

VenueWireless Communications and Mobile Computing · 2008
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceChannel allocation schemesChannel (broadcasting)Computer networkCorrectnessBase stationDistributed computingScalabilityConstraint (computer-aided design)TelecommunicationsAlgorithmWireless

Abstract

fetched live from OpenAlex

Abstract Recent demand for mobile telephone services has been growing rapidly while the electromagnetic spectrum of frequencies allocated for this purpose remains limited. Any solution to the channel assignment problem is subject to this limitation, as well as the interference constraint between adjacent channels in the spectrum. The early research focused on the fixed channel allocation and centralized schemes. Recently, distributed channel allocation schemes have received much attention because of their high reliability and scalability. In these schemes, a base station (BS) has to consult with its neighboring BSs in order to assign a channel to a call. If it cannot communicate with its neighbors, it fails in allocating a channel. However, it is a common phenomenon that a BS fails in communicating with its neighboring BSs due to some reasons, such as heavy traffic load. In this paper, we propose a distributed fault‐tolerant channel allocation schemes which can work well under the mobile host (MH) failures, BS failures, and communication link failures. This algorithm is based upon the mutual exclusion model where the channels are grouped into three equal sized groups and each group of channels cannot be shared concurrently within the same cluster. We prove its correctness. We also report our algorithm's performance with several channel systems using different types of call arrival pattern through comparing with a popular generic distributed algorithm for channel allocation DDRA. Copyright © 2008 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0040.002
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 head0.046
GPT teacher head0.356
Teacher spread0.310 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2008
Admission routes1
Has abstractyes

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