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Record W2060748809 · doi:10.1109/mwc.2013.6507388

Next generation cognitive cellular networks: spectrum sharing and trading [Guest editorial]

2013· article· en· W2060748809 on OpenAlexaff
Kejie Lu, Bo Rong, Sastri Kota, Guangyi Liu, Xinbing Wang

Bibliographic record

VenueIEEE Wireless Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsCognitive radioComputer scienceSpectrum managementWiMAXWirelessTelecommunicationsLicenseComputer networkKey (lock)Wireless networkComputer security

Abstract

fetched live from OpenAlex

In the past few years, fundamental research has demonstrated great potentials of cognitive radio (CR) in increasing the spectrum agility and system capacity of wireless communications systems. With the ability to detect and adapt to the surrounding environment, CR has become one of the widely recognized features for future wireless communication systems. Specifically, CR has been recommended as a key technology to solve the spectrum scarcity problem in the next generation cellar networks. For example, the IEEE 802.16h standard was recently published for the license-exempt operation of WiMAX networks by defining a set of CR capabilities. On the other hand, a lot of efforts are being made to introduce CR features into 3GPP LTE-Advanced.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.043
GPT teacher head0.253
Teacher spread0.210 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations2
Published2013
Admission routes1
Has abstractyes

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