Effect of antenna array properties on multiple‐input–multiple‐output system performance in an underground mine
Bibliographic record
Abstract
There is no deployment strategy or capacity prediction available for wireless multiple‐input–multiple‐output (MIMO) communication systems inside underground mines. In such environments with low angular spread, the authors showed how antenna properties including antenna spacing, polarisation and height impact a 4 × 4‐MIMO system performance. They used channel‐frequency‐response data near the 2.4 GHz obtained from measurements collected in a short underground mine, along with the recently developed multimode waveguide model. Several uniform‐linear‐array configurations were assessed for various propagation scenarios in the mine. They used the singular value, correlation coefficient and capacity analysis to compare their performance. Based on the results, they proposed an array orientation and element spacing, which provides sufficient spatial decorrelation among MIMO subchannels. The spatial decorrelation results are close to that of an i.i.d. Rayleigh channel and are not sensitive to different propagation scenarios. The authors' study of the array height and element polarisation revealed that they mainly impact the subchannels’ power, which leads to offering different MIMO channel capacities. They also observed that in spite of geometrical dissimilarities between underground mines and large tunnels, some of their measurement results in the mine are similar to those of subway tunnels obtained by previous studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".