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Record W2119516413 · doi:10.1109/ccece.1998.682551

Automatic modulation type recognition

2002· article· en· W2119516413 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnvelope (radar)Computer scienceModulation (music)Hilbert transformComputationComputational complexity theoryPattern recognition (psychology)SIGNAL (programming language)Time domainFeature extractionIdentification (biology)Frequency domainAlgorithmArtificial intelligenceFrequency modulationTime–frequency analysisSpeech recognitionDomain (mathematical analysis)Radio frequencyMathematicsTelecommunicationsSpectral densityComputer vision

Abstract

fetched live from OpenAlex

Identification of the modulation type of a received signal is a problem encountered in radio spectrum surveillance and control. It is attractive to design methods that use only one or two time-domain classification parameters, in order to minimize the computational complexity. A number of new classification parameters are proposed and studied in this paper. They are compared to an existing one-parameter method. Also, a new method of envelope extraction, which does not require Hilbert transform computation, is proposed. The proposed methods achieve better recognition rates at short data records, e.g. 89% vs. 82% at 10 dB, 1024 samples, and 95% for a combination of two parameters.

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 categoriesInsufficient payload (model declined to judge)
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 score0.976
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.081
GPT teacher head0.246
Teacher spread0.166 · 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

Quick stats

Citations17
Published2002
Admission routes1
Has abstractyes

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