Less Complex Algorithm to Max-Min the Resource Allocation for Unmanned Aerial Vehicles Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work studies unmanned aerial vehicles (UAVs) as supporters of future wireless networks. We focus on channel assignment and study it as a joint optimization problem, where we pick from a pool of channels provided by a main core network. We find an optimal solution for the association problem between the wireless access points (WAPs) and UAVs, and this way we can maximize the total weighted sum rate by formulating a max-min optimization problem. This formulation is subject to quality of service (QoS), to a maximum number of links from the pool channel, and to available bandwidth constraints. The formulated problem is an NP-hard problem and requires exponential time to be solved as the number of WAPs increases. We propose a low-complexity centralized algorithm to solve the association problem. Our results demonstrate that the solution of the proposed algorithm approaches that of the exhaustive search technique with much less computational complexity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it