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Record W1983229732 · doi:10.1109/vetecf.2010.5594300

Cognitive Multicast Pilot Scheduling for Heterogeneous Networks

2010· article· en· W1983229732 on OpenAlexaff
Zhiyong Feng, Jing Zhong, Wei Li, T. Aaron Gulliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsMulticastComputer scienceComputer networkCognitive networkHeterogeneous networkScheme (mathematics)Cognitive radioScheduling (production processes)Wireless networkUser equipmentChannel (broadcasting)Distributed computingWirelessTelecommunicationsEngineeringBase station

Abstract

fetched live from OpenAlex

With the increasing convergence and cooperation among wireless networks, network awareness of user equipment (UE) has become very important. A practical solution for network information delivery for UE, Cognitive Pilot Channel (CPC), has recently been proposed. It can provide UE with the necessary network information for network selection by using the public signaling channel. In this paper, a cognitive multicast CPC scheme is proposed. It can greatly improve the on-demand CPC delivery efficiency, and also reduce the time delay of information delivery. The cognitive characteristics of the proposed multicast CPC scheme can easily be adapted to different heterogeneous network architectures.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.269
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2010
Admission routes1
Has abstractyes

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