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Record W2118934664 · doi:10.1109/tcst.2011.2181975

Rate Assignment in Wireless Networks: Stability Analysis and Controller Design

2012· article· en· W2118934664 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 Control Systems Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsComputer scienceTelecommunications linkController (irrigation)Stability (learning theory)Control theory (sociology)Interference (communication)Wireless networkControl engineeringWirelessControl (management)EngineeringComputer networkChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this brief, data-rate assignment in IS-856 uplink (reverse link) is studied. The problem is first formulated using an interference model, and then a dynamic control strategy is developed for efficient rate assignment. In the first step, the controller is designed for the special case when the number of users in the network is fixed. Then, the designed controller is further developed for a dynamic network (where the number of users is subject to change) to achieve the desired performance. To this end, the network is formulated in the framework of switched systems, where any new activation or deactivation of users is considered as switching from one system to another. The controllers obtained are then modified properly to retain network stability and performance in the presence of time-delay. Simulation results are presented to elucidate the effectiveness of the proposed approach.

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)0.001
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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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