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Record W2167822064 · doi:10.1109/tvt.2009.2014383

Energy Management Analysis and Enhancement in IEEE 802.16e WirelessMAN

2009· article· en· W2167822064 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSleep modeEnergy consumptionComputer scienceIEEE 802Energy managementEfficient energy useEnergy (signal processing)Power managementWirelessComputer networkPower (physics)Reliability engineeringPower consumptionEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

One strategy for energy management is an indispensable component in the emerging IEEE 802.16e wireless metropolitan area networks (WirelessMAN) supporting mobility. An efficient energy saving mechanism is the basis for the guarantee of a long operating lifetime for a mobile subscriber station (MSS), because MSSs are normally powered by rechargeable batteries. In this paper, we will characterize and model the energy-saving scheme in the IEEE 802.16e WirelessMAN. A comprehensive analysis is performed to study the specified sleep mode with generalized traffic processes. The performance metrics are derived with respect to energy consumption and packet delay to evaluate the tradeoff in the energy management strategy. We then propose an enhanced scheme to offer favorable performance tradeoff by adaptively adjusting the sleep windows. The numerical result indicates that the new policy can significantly reduce power consumption. Simulation results are presented to validate the analytical model, which provides potential guidelines for the efficient management of limited energy.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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