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Enhancing Physical Layer Security Using Underlay Full-Duplex Relay-Aided D2D Communications

2020· article· en· W3036551334 on OpenAlexaff
Majid H. Khoshafa, Telex M. N. Ngatched, Mohamed H. Ahmed, Ahmed Ibrahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsUnderlayComputer scienceComputer networkRelayPhysical layerNode (physics)Transmission (telecommunications)Cellular networkThroughputSecrecySpectral efficiencySignal-to-noise ratio (imaging)WirelessTelecommunicationsEngineeringChannel (broadcasting)Computer security

Abstract

fetched live from OpenAlex

This paper investigates physical layer security and data transmission in cellular networks with inband underlay Device-to-Device (D2D) communication, where there is no direct links between D2D users. We propose the use of full-duplex (FD) transmission and dual antenna selection at the relay node. Thanks to the FD transmission, the relay node can simultaneously act as a jammer to enhance the cellular network secrecy performance, while improving the D2D communication data transmission. This describes a practical attractive scheme, where spectrum sharing is beneficial for both the D2D and cellular networks in terms of throughput enhancement and security provisioning, respectively. We analyze the secrecy performance of the cellular network and derive closed-form expressions for the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity. We also derive a closed-form expression of the D2D outage probability. Furthermore, asymptotic analysis for SOP is performed. Simulation and numerical results are provided to verify the efficiency of the proposed scheme and to validate the accuracy of the derived expressions.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.289
Teacher spread0.223 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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