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Record W2000093279 · doi:10.1029/2000rs002345

Characteristics of radio transmitter fingerprints

2001· article· en· W2000093279 on OpenAlexafffund
K. J. Ellis, N. Serinken

Bibliographic record

VenueRadio Science · 2001
Typearticle
Languageen
FieldComputer Science
TopicWireless Signal Modulation Classification
Canadian institutionsCommunications Research Centre Canada
FundersIndustry Canada
KeywordsTransmitterComputer scienceIdentification (biology)TelecommunicationsRadio-frequency identificationEnvelope (radar)Radio waveProcess (computing)Radio frequencyComputer securityRadarChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this paper, the characteristics of radio transmitter fingerprints will be examined by analyzing both the amplitude and phase information obtained from complex envelope recordings of transmitter turn‐on transients. The interest in the analysis of such transients is related to the identification of malfunctioning or illegally operated radio transmitters in support of radio spectrum management practices. Of the 28 VHF radios considered in this study, many were found to produce fingerprints having uniquely distinctive features which could be used for identification purposes. Unfortunately, some of these radios were found to have fingerprints that were virtually indistinguishable from each other, making the identification process more difficult, if not impossible. Details of the equipment, analyses, and data collection procedures will be presented along with a discussion of the experimental results. The merits of this technique over others currently in use will also be presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations161
Published2001
Admission routes2
Has abstractyes

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