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Record W2131497385 · doi:10.1109/aiccsa.2009.5069414

Performance of resource allocation schemes in adaptively modulated TDMA network

2009· article· en· W2131497385 on OpenAlexaff
Amiotosh Ghosh, A.K. Elhakeem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceComputer networkResource allocationOverhead (engineering)Weighted round robinNetwork packetTime division multiple accessTelecommunications linkHiperLANWireless networkWirelessQuality of serviceWireless lanTelecommunicationsDynamic priority scheduling

Abstract

fetched live from OpenAlex

In this paper we investigate performance of resource allocation schemes in WLANs of metropolitan area using HiperLAN type 2 standard. Inside the WLANs user moves from one place to another and user rates are dynamically adjusted based on its distance from the access point. To manage the wireless resources of the network we propose resource allocation schemes and evaluate their performance. We develop a generic simulation software for the network and use it for three resource allocation policies namely minimum overhead round robin (MORR), which does not depend on user's buffer condition, weighted minimum overhead round robin (WMORR) which is a function of user buffer as well as the waiting time for transmission opportunity and weighted round robin (WRR) which is a function of user buffer only. For performance comparison we evaluate average and variance of number of packets in the user buffer, user buffer overflow and overhead in the uplink phase. Our results show that the second adaptive resource allocation technique i.e., WMORR outperforms the other two.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.234
Teacher spread0.219 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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