Bibliographic record
Abstract
Given the ever-increasing popularity of wireless body area networks (WBANs), in some application scenarios, many WBANs may operate densely and lead to a high mutual interference. Excessive interference may severely degrade the network performance, which is called the network co- existence problem. It is critical to fully understand the network co-existence problem to ensure the effectiveness and efficiency of WBANs. In this paper, we investigate the network interference and co-existence problem for the scenarios with densely deployed WBANs. Specifically, we model the probability distribution of interference among co- existing WBANs using the advanced Geometrical Probability approach. We then approximate the total inter-cell interference by a simple gamma distribution which is accurate according to the simulation results. We further use the interference distribution model to solve the practical network planning issues for WBANs. That is, we quantify the minimum network distance to ensure the signal to interference and noise ratio (SINR) for the boundary nodes and the average SINR of the whole system, 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".