CHARACTERIZATION OF AN OFF-BODY CHANNEL AT 2.45 GHZ IN AN UNDERGROUND MINE ENVIRONMENT
Bibliographic record
Abstract
Underground mines are challenging environments for off-body wireless communication, since the signal propagation is majorly affected by small scale and large scale fading.The use of multiple antennas at the transmitter and the receiver sides is a known technique to combat fading and enhance capacity.In this paper, the channel parameters of a 2 × 2 Multiple-Input Multiple-Output (MIMO) off-body system are investigated in an underground gold mine and compared to the Single-Input Single-Output (SISO) system parameters.Measurement campaigns were conducted using monopole antennas at a center frequency of 2.45 GHz for both Line Of Sight (LOS) and None Line of Sight (NLOS) scenarios.The measured frequency responses are converted into impulse responses through an Inverse Fourier Transform (IFT).The results show that for a constant transmitted power, the path loss exponents at NLOS are smaller than their counterpart values at LOS.The channel capacity values decrease as the propagation distance increases and when the link is obstructed at NLOS.The RMS delay spread is generally increasing with distance for both LOS and NLOS situations.When a fixed Signal-to-Noise Ratio (SNR) is assumed, MIMO topologies improved the SISO capacity by roughly 8 bps/Hz.The channel characterization results demonstrate that the MIMO configurations provided a remarkable improvement in terms of capacity, coherence bandwidth, and time delay spread compared to the SISO topologies.
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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) | 0.000 | 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".