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Record W2793222875 · doi:10.1049/iet-com.2017.0719

Maximising the degrees of freedom of the physical‐layer secured relay networks with artificial jamming

2018· article· en· W2793222875 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

VenueIET Communications · 2018
Typearticle
Languageen
FieldEngineering
TopicWireless Communication Security Techniques
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsJammingPhysical layerDegrees of freedom (physics and chemistry)RelayComputer scienceLayer (electronics)TelecommunicationsComputer networkPhysicsNanotechnologyMaterials scienceQuantum mechanicsWireless

Abstract

fetched live from OpenAlex

In this study, the authors consider the physical‐layer security problem in the relay networks. The relay nodes are employed not only to aid the information transmission but also to improve the network security in the physical layer by sending artificial noise to resist the potential malicious eavesdropper. By allowing shared randomness between the jamming nodes, it is shown that the maximum degrees of freedom (DoF) of the considered network is almost surely. The necessary and sufficient conditions for the optimal DoF setup are established. Moreover, a simple DoF‐optimal collaborative beamforming algorithm is proposed, and it works very well in the high signal‐to‐noise ratio regime, which is verified by computer simulations.

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 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: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.424

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.030
GPT teacher head0.262
Teacher spread0.232 · 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