Deep Learning Modeling of a WBAN-MIMO Channel in Underground Mine
Bibliographic record
Abstract
In this paper, an efficient model of the channel matrix is developed for 2 × 2 Wireless Body Area Network Multiple Input Output (WBAN-MIMO) system, based on deep learning algorithms. The model is composed of three deep learning algorithms. Moreover, the model predicts simultaneously the channel matrixHin underground mine and identifies the position of the collected data in both Line of Sight (LoS) and Non-Line of the Sight (NLoS) scenarios. The model is trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within the frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrixHwith the position and identifies whether the channel is LoS or NLoS. Finally, the path loss and the channel impulse response models are compared with the measurements-based ones. The modeled channel prediction exhibited lower Root Mean Square Error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that the deep learning MIMO WBAN modeling offers a powerful solution for future wireless systems in underground mine environments.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".