Optimizing power allocation in mission critical cognitive radio networks
Bibliographic record
Abstract
In order to advance the Mission Critical Networks (MCNs), new technologies such as Cognitive Radio (CR) has to be adopted. However, power is an important resource in CR Networks (CRNs) especially in disastrous environments where power outages are expected and communications become very important for search and rescue teams. Power allocation among Secondary Users (SUs) needs to be optimized particularly when the available transmission power is limited. In this paper, two algorithms are proposed to optimize power allocation among SUs that were successful in accessing the spectrum using hybrid interweave/underlay access scheme. The objective is to maximize the Spectral Efficiency (SE) while respecting the power budget constraints. The scenario in which the CRN has multiple SUs that are interfering with several PUs is addressed. Hence, different SUs will have different power and interference limits depending on PUs' activity. Moreover, since the complexity of the optimization algorithms can be high, a suboptimal discrete Cap-Limited Heuristic (CLH) algorithm is proposed. The CLH algorithm considers assigning power to SUs from a discrete set of power levels. Extensive simulations are performed and the results of the proposed suboptimal algorithm show a near optimal performance with lower complexity and reduced computational-cost.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".