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Record W2948781686 · doi:10.1109/tvt.2019.2919873

Secure Primary Transmission Assisted by a Secondary Full-Duplex NOMA Relay

2019· article· en· W2948781686 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 Transactions on Vehicular Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsWestern University
FundersEngineering and Physical Sciences Research CouncilXinjiang Normal UniversityNational Natural Science Foundation of China
KeywordsRelayEavesdroppingBeamformingTransmitterTransmission (telecommunications)Decoding methodsComputer scienceCognitive radioArtificial noiseNomaComputer networkElectronic engineeringSecure transmissionTelecommunications linkEngineeringTelecommunicationsWirelessPhysics

Abstract

fetched live from OpenAlex

In this paper, secure primary transmission is proposed by using a multi-antenna secondary full-duplex non-orthogonal multiple access (NOMA) relay in cognitive radio (CR) networks. First, the primary signal is transmitted from the primary transmitter to the relay. Artificial noise is generated by using part of the antennas at the relay to disrupt eavesdropping, without affecting the primary transmission. Then, superimposed signals are transmitted from the relay to the primary receiver and secondary receivers via NOMA. The primary security is guaranteed by the modified decoding order and beamforming optimization, which is converted to convex and solved by an iterative algorithm. Simulation results are presented to show the effectiveness of the proposed scheme in guaranteeing the primary security in CR 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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