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Record W2016739648 · doi:10.1109/glocom.2006.792

WLC30-6: Admission Control in Power Constrained OFDM/TDMA Wireless Mesh Networks

2006· article· en· W2016739648 on OpenAlexaff
Dusit Niyato, Ekram Hossain

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceWireless mesh networkRouterComputer networkNetwork packetSleep modeMesh networkingSwitched meshMarkov decision processTime division multiple accessShared meshMarkov processPower (physics)WirelessWireless networkPower consumptionTelecommunications

Abstract

fetched live from OpenAlex

We investigate connection-level, packet-level, and energy-saving performances in a power constrained (e.g., solar-powered) OFDM/TDMA wireless mesh network. When a mesh router goes to the sleep mode to reduce energy consumption, packet-level performances (as observed by a mesh client) will be degraded since in sleep mode, a mesh router can neither receive nor transmit packets. An analytical model is developed to analyze the relationship among the packet-level and the connection-level performances. Based on the analytical model, the system performance is analyzed in terms of system utility. To this end, an optimization problem is formulated by using a Markov decision process to choose the optimal policy on sleep management and maximum number of admissible connections at a mesh router so that the system utility is maximized.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.003
GPT teacher head0.188
Teacher spread0.185 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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