Experimental Assessment of 5 GHz WiFi Coverage Expansion in Underground Mine Using Reconfigurable Intelligent Surface
Bibliographic record
Abstract
This paper explores the application of Reconfigurable Intelligent Surfaces (RIS) to expand 5 GHz WiFi coverage in underground mining environments, where traditional RF networks face significant challenges due to complex propagation conditions and Non-Line-of-Sight (NLOS) scenarios. Using RIS with transmitarray-like behavior, we demonstrate the ability to dynamically redirect electromagnetic waves into orthogonal tunnels, overcoming sharp angles and confined spaces. Our experimental study, conducted in a decommissioned underground mine, shows that a daisy chain of RIS can improve signal strength by up to 30 dB and extend coverage to more than 90% of previously unreachable areas. The LATYS FOCUS RIS prototypes used in this work exhibit high directivity, enabling efficient signal bending without degradation of the already covered areas. To complement the experimental findings, we propose a simulation methodology integrating RIS models into a standard RF planning tool. By accurately modeling the propagation behavior in tunnels with sharp angles, the simulations align closely with real-world measurements, validating the feasibility of using RIS for RF network planning. These results highlight the transformative potential of RIS technology in underground mining, offering a scalable, cost-effective alternative to traditional deployments involving extensive cabling and multiple access points.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".