An Algorithm That Predicts CSI to Allocate Bandwidth for Healthcare Monitoring in Hospital's Waiting Rooms
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Bibliographic record
Abstract
In wireless healthcare monitoring systems, bandwidth allocation is an efficient solution to the problem of scarce wireless bandwidth for the monitoring of patients. However, when the central unit cannot access the exact channel state information (CSI), the efficiency of bandwidth allocation decreases, and the system performance also decreases. In this paper, we propose an algorithm to reduce the negative effects of imperfect CSI on system performance. In this algorithm, the central unit can predict the current CSI by previous CSI when the current CSI is not available. We analyze the reliability of the proposed algorithm by deducing the standard error of estimated CSI with this algorithm. In addition, we analyze the efficiency of the proposed algorithm by discussing the system performance with this algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it