Joint admission control and power allocation in hospital networks based on cognitive radios.
Bibliographic record
Abstract
In this thesis, we present an approach to solve the joint call admission control and power allo- cation problem in a hospital environment based on cognitive radio. Specifically, a multi-objective non-convex mixed integer non-linear programming (MINLP) problem with weighted-sum method for wireless access in an indoor hospital environment has been formulated in order to maximize the number of admitted secondary users and minimize transmit power while guaranteeing the through- put of all secondary users and satisfying the interference constraints for the protected and primary users. To solve this MINLP problem with different weights given to different objectives, we pro- pose to use the standard branch and bound algorithm as appropriately modified to find the optimal solution. We also coded a specific program using OPTI Toolbox to find the minimum objective function value, number of admitted secondary users and all related values such as total system power and throughput. To analyze the numerical results, we considered three cases with equal and non-equal weights. We also changed the values of interference and maximum source power to obtain and analyze different results comparing with the normal one. Our results indicate that more power is allocated and better throughput is guaranteed while the number of admitted users is increasing. However, as they increase, the objective function value increases steadily as well, which means that it is more difficult to reach our minimizing objective.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".