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Record W3197648599 · doi:10.1109/lcomm.2021.3109888

Secrecy Energy Efficiency in Full-Duplex AF Relay Systems With Untrusted Energy Harvesters

2021· article· en· W3197648599 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 Communications Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsRelayBeamformingArtificial noiseComputer scienceEfficient energy useMaximizationEnergy harvestingMathematical optimizationOptimization problemEnergy (signal processing)Iterative methodSignal-to-noise ratio (imaging)Convex optimizationWirelessPower (physics)AlgorithmTelecommunicationsMathematicsRegular polygonElectrical engineeringPhysical layerEngineering

Abstract

fetched live from OpenAlex

We investigate an artificial noise (AN) aided beamforming scheme for full-duplex amplify-and-forward relay network in the presence of multiple untrusted energy harvesting receivers (EHRs). We establish an optimization problem to maximize the secrecy energy efficiency (SEE), while meeting the self-interference nulling and transmit power constraints at relay as well as maintaining the energy harvesting threshold at the EHRs by jointly designing the relay beamforming (BF) and AN covariance matrices. Due to the non-convex structure of the SEE maximization problem, we first design a null-space BF scheme at relay to eliminate the SI and simplify the joint optimization and then propose an iterative algorithm to achieve a local optimum based on the penalty function and the successive convex approximation methods. Numerical results are provided to validate the effectiveness of our proposed design.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
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
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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