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Record W2100667358 · doi:10.1109/tvt.2010.2041941

Optimal Cooperative Internetwork Spectrum Sharing for Cognitive Radio Systems With Spectrum Pooling

2010· article· en· W2100667358 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 Vehicular Technology · 2010
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of British ColumbiaCarleton University
Fundersnot available
KeywordsCognitive radioPoolingComputer scienceScheme (mathematics)Spectrum (functional analysis)Computer networkFrequency allocationDistributed computingMathematical optimizationTelecommunicationsWirelessMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Spectrum pooling in cognitive radio systems is an approach to manage the available spectrum bands from different licensed networks. Most previous work on spectrum pooling concentrates on the system architecture and the design of flexible-access algorithms and schemes. In this paper, we present a cooperative scheme for internetwork spectrum sharing among multiple secondary systems, which takes into account the price and spectrum efficiency as the design criteria. Specifically, the spectrum-sharing problem is formulated as a stochastic bandit system; thus, the optimal spectrum-sharing scheme is simply allocating the new available band to the secondary network with the lowest index. Extensive simulation examples illustrate that the proposed scheme significantly improves the performance compared with the existing scheme that ignores optimal spectrum sharing.

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 score0.801
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
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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