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Record W2620059960 · doi:10.1109/lsens.2017.2707280

Disrupting Anti-Jamming Interference Alignment Sensor Networks with Optimal Signal Design

2017· article· en· W2620059960 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

VenueIEEE Sensors Letters · 2017
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsUniversity of British ColumbiaCarleton University
Fundersnot available
KeywordsJammingNear-far problemInterference (communication)Computer scienceChannel (broadcasting)SIGNAL (programming language)Noise (video)Electronic engineeringTransmission (telecommunications)TelecommunicationsEngineeringWirelessArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Anti-jamming interference alignment (IA) can battle with the adversarial jamming signals for IA sensor networks effectively. In this letter, we design jamming signals to optimally disrupt the transmission of anti-jamming IA sensor networks. Specifically, with only the jamming channel information known for the jammer, we design the jamming signal to minimize the signal-to-jamming-plus-noise ratio (SJNR) at a certain targeted IA receiver with the jamming power constraints. Furthermore, an approximate expression of the minimized SJNR is presented to estimate the jamming performance theoretically. Simulation results are provided to verify the effectiveness of the proposed optimal jamming scheme towards IA sensor networks.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.000
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 head0.024
GPT teacher head0.245
Teacher spread0.221 · 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