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Record W4406170739 · doi:10.1109/ojap.2025.3527334

Near-Ground Propagation Channel Modeling and Analysis in Underground Mining Environment at 2.4 GHz

2025· article· en· W4406170739 on OpenAlexaff
Isam Eddine Lamri, Mourad Nedil, Mohamed Nasr eddine Temmar, Nahi Kandil

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsChannel (broadcasting)Environmental scienceMining engineeringGeologyTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

This paper presents a detailed measurement and performance analysis of near-ground propagation channels in an underground mine for both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios. The analysis is derived from channel measurements conducted at a frequency of 2.4 GHz with a bandwidth of 200 MHz, utilizing four different combinations of transmitter-receiver (Tx-Rx) antenna heights ranging from 10 cm to 120 cm. Key channel characteristics such as large-scale path loss, time dispersion, and coherence bandwidth are reported and evaluated. The study suggests that a multi-slope (four-slope) path loss model is more effective in predicting path loss across various propagation segments in the mining environment. The path loss coefficient (n) and shadowing variance (σ) show significant sensitivity to both the Tx-Rx distance and the heights of the Tx-Rx antennas, while the root mean square delay spread (τ rms) and coherence bandwidth (BC) are less affected by these variables. Moreover, distinct path loss patterns are observed for each combination of Tx-Rx antenna heights, resulting in varying signal attenuation levels. The identification of an optimal height for minimizing signal loss is thus required. These observations provide valuable and crucial insights into the near-ground propagation characteristics within the intricate and scattering-rich conditions of underground mines.

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)0.001
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: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.238
Teacher spread0.219 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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