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Record W2075838418 · doi:10.1109/mwc.2007.314545

Dynamic resource allocation in OFDMA wireless metropolitan area networks [Radio Resource Management and Protocol Engineering for IEEE 802.16]

2007· article· en· W2075838418 on OpenAlexafffund
Syed Ali, Ki-Dong Lee, Victor C. M. Leung

Bibliographic record

VenueIEEE Wireless Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
FundersMinistère de la Santé et des Services sociaux
KeywordsComputer scienceResource allocationRadio resource managementOrthogonal frequency-division multiple accessSubcarrierResource management (computing)Computer networkFrequency-division multiple accessIEEE 802Wireless networkWirelessAccess controlOrthogonal frequency-division multiplexingTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this article we present important resource allocation problems in IEEE 802.16 wireless metropolitan area networks employing orthogonal frequency division multiple access. We first highlight the unique aspects of these networks and identify challenges and opportunities provided by the physical and medium access control layers. Next, we concentrate on four interrelated resource allocation problems: dynamic subcarrier allocation, adaptive power allocation, admission control, and capacity planning. We describe solution techniques, provide preliminary results, and discuss open problems for future research

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)0.001
metaresearch head score (Gemma)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.260
Teacher spread0.246 · 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

Citations72
Published2007
Admission routes2
Has abstractyes

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