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Record W4255552756 · doi:10.36227/techrxiv.12671099

Eigenvalue-Based RF Interference Detector for Multi-Antenna Wireless Communications

2020· preprint· en· W4255552756 on OpenAlexafffund
Tilahun M. Getu, Wessam Ajib, René Landry

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDetectorInterference (communication)Monte Carlo methodSubspace topologyLikelihood-ratio testElectromagnetic interferencePhysicsElectronic engineeringEigenvalues and eigenvectorsWirelessTest statisticMIMOComputer scienceChannel (broadcasting)AlgorithmTelecommunicationsMathematicsEngineeringStatisticsStatistical hypothesis testingArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Radio frequency interference (RFI) is occurring in both satellite and terrestrial communication systems. In order to mitigate RFI efficiently, it has to be detected robustly. Toward this end, through the computation of an eigenvalue-based test statistic, an eigenvalue-based blind RFI detector is proposed for single-input multiple-output systems that may suffer from RFI. For medium to large interference-tonoise ratio (INR) regimes and under sample starved settings, Monte-Carlo simulations corroborate that the proposed blind detector manifests a comparable detection performance with a generalized likelihood ratio test (GLRT) detector fed with the knowledge of the signal of interest (SOI) channel, and a matched subspace detector fed with the knowledge of the SOI and RFI channels. Such performance signifies the applicability of the proposed RFI detector for real-time applications.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.104
GPT teacher head0.291
Teacher spread0.187 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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Same topicAntenna Design and OptimizationFrench-language works237,207