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Record W2610471387 · doi:10.1017/9781316212493.009

Joint Power and Admission Control in Cognitive Radio Networks

2017· book-chapter· en· W2610471387 on OpenAlexaff
Ekram Hossain, Mehdi Rasti, Long Bao Le

Bibliographic record

VenueCambridge University Press eBooks · 2017
Typebook-chapter
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversité du Québec à MontréalUniversity of Manitoba
Fundersnot available
KeywordsCognitive radioQuality of serviceComputer networkComputer scienceWirelessRadio spectrumJoint (building)Wireless networkPower controlTelecommunicationsService (business)Radio resource managementPower (physics)BusinessEngineering

Abstract

fetched live from OpenAlex

Due to increasing demand for wireless access services, efficient utilization of the limited frequency spectrum has become crucial. Exclusive licensing of spectrum bands to specific users or services is very inefficient from the viewpoint of spectrum utilization, and it lacks the agility needed to support new applications. Cognitive radio networks (CRNs) have thus emerged as an adaptive cohabitation paradigm for wireless communication. The primary radio networks (PRNs) can dynamically share the spectrum with the secondary users (SUs) so that the SUs achieve their minimum acceptable quality-of- service (QoS), and at the same time, all the primary users (PUs) are protected in the sense that the SUs do not violate the QoS requirements of the PUs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.019
GPT teacher head0.206
Teacher spread0.187 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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