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Record W4290996205 · doi:10.1109/icc45855.2022.9838528

Blind Distributed Spectrum Sensing with Binary Local Decisions through the Maximum Energy Indicator

2022· article· en· W4290996205 on OpenAlexaff
Yindi Jing, Tsang-Yi Wang, Xinwei Yu

Bibliographic record

VenueICC 2022 - IEEE International Conference on Communications · 2022
Typearticle
Languageen
FieldComputer Science
TopicCognitive Radio Networks and Spectrum Sensing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCognitive radioBinary numberEnergy (signal processing)Computer scienceGaussianAlgorithmAdditive white Gaussian noiseGaussian noiseSIGNAL (programming language)Detection theoryHomogeneity (statistics)Signal-to-noise ratio (imaging)Noise powerPower (physics)Channel (broadcasting)Mathematical optimizationTelecommunicationsMathematicsStatisticsWirelessDetectorMachine learningPhysics

Abstract

fetched live from OpenAlex

This paper proposes a new scheme for distributed spectrum sensing in the blind scenario, wherein the channel gains, the signal power, and the noise power are unknown. By utilizing energy detection and homogeneity test concepts, the cognitive radios (CRs) make binary local decisions based on whether a CR has the maximum sample energy within a sampling window among all CRs. With independent channels, the use of this maximum energy indicator generates a desirable discrepancy among the CR decisions when the frequency band is in use. Closed-form results on the distribution of the CR decisions are obtained for Gaussian noises and signal. Asymptotic analytical expressions are derived on the detection performance of the proposed scheme. Simulation results show the advantage of the proposed scheme and validate the analysis.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0040.002
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 head0.068
GPT teacher head0.307
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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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