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Record W2786175106 · doi:10.1109/pimrc.2017.8292234

UFMC-based wideband spectrum sensing for cognitive radio systems in non-Gaussian noise

2017· article· en· W2786175106 on OpenAlexaff
Djamel E. Kebiche, Ali Baghaki, Xiaomei Zhu, Benoı̂t Champagne

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitive radioWidebandComputer scienceDetectorGaussian noiseOrthogonal frequency-division multiplexingElectronic engineeringNoise (video)Transmission (telecommunications)GaussianRadio spectrumTelecommunicationsAlgorithmWirelessEngineeringPhysicsArtificial intelligenceChannel (broadcasting)

Abstract

fetched live from OpenAlex

Cognitive radio (CR) is an important technology that allows to deal with spectrum congestion, where secondary applications (users) attempt to access a frequency band that is reserved for a primary application. A challenging function for a CR is to sense a frequency band and detect the absence or presence of a licensed user, a task referred to as spectrum sensing. In this paper, we investigate the performance of the Rao-test based detector for wideband spectrum sensing under non-Gaussian noise in a multi-carrier transmission framework. Specifically, we incorporate this detector into the universal filtered multicarrier (UFMC) modulation scheme envisaged for 5G systems. Through numerical simulations, we show that the Rao-test based detector combined with UFMC outperforms the traditional OFDM based system in a realistic non-Gaussian noise environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly 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 score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.258
Teacher spread0.240 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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