Multiple‐input multiple‐output beam‐space for high‐speed wireless communication in underground mine
Bibliographic record
Abstract
This study presents a new low‐cost multiple‐input multiple‐output (MIMO) beam‐pace technique to increase the overall link capacity of wireless MIMO communications systems in scatter‐rich underground mines. This technique is based on generating multiple orthogonal beams for data spatial multiplexing using conformal cylindrical‐shape patch arrays. Hence, this study is intended to reveal interests towards the use of conformal MIMO systems for underground mine communications. Two separate 4 × 4 MIMO measurement campaigns are performed and then investigated in a comparative way; the first uses conformal microstrip patch arrays (CMPA), while the second uses conventional planar microstrip patch arrays (PMPA). Based on sweeping‐frequency technique (2.35–2.55) GHz, the channel performance is characterised in terms of path loss, fading distribution, capacity and RMS time dispersion parameters. Whether under a line of sight (LOS) or a non‐LOS (NLOS) condition, the extracted results confirm the performance priority of CMPA for underground mines communications as they further enhance the propagation characteristics and the capacity gain of the channel; under NLOS, a maximal capacity of 12.95 bits/s/Hz is achieved compared to 9.6 bits/s/Hz obtained using PMPA. For LOS and NLOS cases, maximal capacity boosts of up to 0.43 and 3.35 bits/s/Hz are achieved over PMPA, respectively.
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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 | 0.000 |
| 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) | 0.001 | 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".