Optimization of Leaky Feeder Slot Spacing for Better Beam Forming in Mines and Tunnels
Bibliographic record
Abstract
The radiating cable (or leaky feeder) is the leading solution for distributed broadband wireless access in underground mines and tunnels. The traditional radiating cable has equally spaced slots along its axis radiating evenly. However, with the new requirement for tracking and localization of miners, this paper shows how to modify the leaky feeder to function as a beam forming antenna array by appropriately optimizing the slot spacing. Optimizing the leaky feeder slot distances using Genetic algorithm has a good impact on the channel parameters, making it an excellent choice for leaky feeder antennas. Linear, as well as bent practical leaky feeder shapes, were considered, and we tested five different optimization algorithms and compared side lobe energies, varying the slot distance which had an impact on side lobe level. Also, we derived the expression for the BER, spectral efficiency and throughput considering a time-varying wireless channel. We observed that as the radius of curvature of the feeder increased (approaching a straight line), the spectral efficiency decreased as expected. Interestingly different optimization algorithms perform differently at different carrier frequencies. An optimization method should be chosen according to the required goal to be achieved (maintaining uniform equal power along the cable, or a more directed beam, or using a certain frequency of operation).
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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 | 0.001 |
| 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".