EXPERIMENTAL CHARACTERIZATION OF A MIMO UNDERGROUND MINE CHANNEL AT 2.45 GHZ
Bibliographic record
Abstract
Abstract—In this paper, an experimental characterization of a MIMO underground channel is presented. A simple statistical model is proposed at 2.45 GHz. The channel is characterized in terms of path loss, shadowing, RMS delay spread, and capacity. The measurements are carried out in an underground mine, which is a harsh confined environment. The path loss model is extracted from measured data for the line of sight (LOS) and non-line of sight (NLOS) scenarios for both MIMO and SISO channels. The path loss exponent in LOS is less than 2 in MIMO and SISO as the environment has a dense concentration of scatterers. A statistical study is carried out to find the delay spread. For MIMO and SISO, there is no relation between the delay spread and the transmitter receiver distance. Furthermore, the delay spread of the MIMO is less than the one of the SISO channel in the LOS measurement campaigns. Aikake information criteria are used as a goodness of fit for different statistical distributions to represent the delay spread. According to the calculated capacity for a constant signal to noise ratio in LOS case, the transmission performance is significantly improved by using the MIMO scheme over the traditional SISO. Therefore, MIMO is an ideal candidate for future wireless underground communications.
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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".