Mm‐waves propagation measurements in underground mine using directional MIMO antennas
Bibliographic record
Abstract
This study reveals the interests towards the use of directional (D)‐multiple‐input and multiple‐output (MIMO) setup as a potential solution to overcome the severe propagation loss of inherent line of sight (LOS) mm‐waves communications in underground mine. To show the advantages of the proposed D‐MIMO, two separate measurement campaigns are assessed in a comparative way; the first uses a single‐input single‐output (SISO), while the second uses a 2 × 2 D‐MIMO system. Furthermore, due to the unavoidable blockage of direct LOS in underground mines, the miner's shadowing effects (NLOS‐MSE) are investigated. Thus, using both D‐SISO and D‐MIMO setup, the channel propagation characteristics are extracted and investigated with and without the presence of a miner completely blocking LOS. Under LOS, results show that, besides offering a reliable mm‐waves link budget, D‐MIMO restrains the average path loss (PL) by more than 4.7 dB, further suppresses the root mean square delay to 1.85 ns and offers an average capacity of 23.3 bits/s/Hz. As a miner completely blocks LOS, the proposed D‐MIMO system has shown a greater signal ability to overcome the effects of a miner's body; NLOS‐MSE compensation of about 4.3 dB and a capacity gain of 19.6 bits/s/Hz have been achieved over the conventional SISO system.
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 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 | 0.000 |
| 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) | 0.000 | 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".