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Record W2896355182 · doi:10.1109/mms.2017.8497166

Outage Probability of Full Duplex Relay in OLOS Underground Mine Environments

2017· article· en· W2896355182 on OpenAlexaff
Kazi Mustafizur Rahman, Nadir Hakem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsRelayMIMOComputer scienceComputer networkPopulationTelecommunicationsEngineeringChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

Expanding the coverage of network with different techniques is necessity and demand of today's life. As the population increases the demand for more solutions to give more capacity and to reach everyone on the whole world with network increases. This paper considers the design of dual-hop full-duplex (FD) Multiple-Input Multiple-Output (MIMO) Relay for workers in the underground mine tunnel area, with an obstructed line of sight OLOS conditions, where the relay is equipped with two antennas, while the source and destination are armed with a single antenna. This paper aims to investigate the outage probability performance when the communication channel is obstructed by a vehicle in a mine tunnel. In this case, the radio signal is obstructed, and no communication is possible, we proposed an investigative relay, installed around that vehicle, with scheme Zero-Forcing Beam-Forming (ZFBF) to avoid obstruction and enable communication again. The simulation results show that the proposed scheme can efficiently minimize the outage probability.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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