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Record W1571428298 · doi:10.1109/twc.2010.04.081437

Joint connection admission control and routing in IEEE 802.16-based mesh networks

2010· article· en· W1571428298 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 Wireless Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsAdmission controlComputer scienceComputer networkQuality of serviceRouting (electronic design automation)HandoverWireless mesh networkCall Admission ControlWireless networkWirelessTelecommunications

Abstract

fetched live from OpenAlex

Connection admission control and routing are two important mechanisms in the provision of quality of service (QoS) in IEEE 802.16-based wireless mesh networks. In this paper, we propose a joint admission control and routing scheme for multiple service classes with the objective to maximize the overall revenue from all carried connections. QoS constraints such as handoff dropping probability can be guaranteed. Multiple service classes can be prioritized by imposing different reward rates. We formulate the problem as a decision process, and apply optimization techniques to obtain the optimal admission control policies. The effectiveness of the proposed approach is illustrated by numerical and simulation results. We show that the proposed joint admission control and routing scheme can produce maximum revenue obtainable by the system under QoS constraints.We also show that the optimal joint admission control policy is a randomized policy, i.e., connections are admitted to the system with some probabilities when the system is in some states.

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 score0.927
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.236
Teacher spread0.222 · 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