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Record W3043119649 · doi:10.1049/iet-com.2019.0892

Physical layer security of interference aligned mixed RF/unified‐FSO relaying network

2020· article· en· W3043119649 on OpenAlexaff
Deeb Tubail, Mohammed El‐Absi, Anas M. Salhab, Salama Ikki, Salam A. Zummo, Thomas Kaiser

Bibliographic record

VenueIET Communications · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsLakehead University
FundersDeutsche Forschungsgemeinschaft
KeywordsPhysical layerInterference (communication)Computer scienceComputer networkLayer (electronics)TelecommunicationsWirelessMaterials scienceChannel (broadcasting)Nanotechnology

Abstract

fetched live from OpenAlex

In this study, the authors secure the mixed radio frequency/free space optical (FSO) relay‐aided interference aligned system using a proposed physical layer security algorithm. This algorithm reduces the quality of the received signal at the eavesdropper through two procedures. First, it minimises the data transmission power from the legitimate users and the relays. Second, it jams the eavesdropper by broadcasting artificial noise from the users and the relays. Therefore, a joint optimisation problem is formulated to degrade the received signal at the eavesdropper from the users and the relays and to maximise the jamming artificial noise power, which is solved using an iterative optimisation algorithm beside a semi‐definitive programming algorithm. Furthermore, the pre‐coding and decoding matrices of the users and the relay are designed to enable the legitimate users to cancel the artificial noise, while the eavesdropper is disabled from distinguishing the artificial noise from the real streams. Moreover, the security performance of the proposed algorithm is analysed, and the impact of the FSO link's state on the security performance is studied. The extensive simulation results show the efficiency of the proposed algorithm and illustrate the role of the FSO link's state on the security performance.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.754

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.046
GPT teacher head0.279
Teacher spread0.233 · 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 teacher head, 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

Citations4
Published2020
Admission routes1
Has abstractyes

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