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Achievable Secrecy Rate in mmWave Multiple-Input Single-Output Ad Hoc Networks

2020· article· en· W3038431284 on OpenAlexaff
Ahmed F. Darwesh, Abraham O. Fapojuwo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSecrecyWireless ad hoc networkComputer scienceStochastic geometryTransmitterNakagami distributionComputer networkFadingArtificial noiseElectronic engineeringWirelessTopology (electrical circuits)TelecommunicationsElectrical engineeringMathematicsStatisticsComputer securityEngineering

Abstract

fetched live from OpenAlex

This paper analyzes the achievable secrecy rate in a millimeter wave (mmWave) multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. First, using the tools of stochastic geometry, the mathematical analysis of the average achievable secrecy rate is performed, taking into consideration the impact of blockage and Nakagami-m fading. Moreover, a multi-array artificial noise transmission technique is applied at the typical transmitter (Tx-AN) to enhance the average secrecy rate. Consequently, the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-AN technique is presented. Numerical and simulation results show that, at given values of colluding eavesdroppers' intensity and total transmit power, using Tx-AN technique achieves 56.4% improved average secrecy rate over that without. Furthermore, by applying the Tx-AN technique, increasing the colluding eavesdroppers' intensity provides no negative impact on the average secrecy rate. However, without the AN technique, the average secrecy rate faces a fast degradation when the intensity of colluding eavesdroppers increases. The results therefore show that the Tx-AN technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.204
Teacher spread0.165 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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