On Demand User Association and Load Distribution with Guaranteed Rate in Multi UAV-Assisted Cellular Networks
Bibliographic record
Abstract
The recent advent of emergent networks (5G and 6G) has led to an exponential growth in the number of smart devices in circulation. Deployment of a group of unmanned aerial vehicles (UAV s) to a site to satisfy customers' minimal QoS demands is one way to meet the ever-increasing need for network coverage. The main challenge that we address in this paper is how to placement these UAV s such that the load is fairly distributed among the UAVs, and the data rate per customer is maximized. Both consumers and providers have a stake in solving this issue. As for consumers, better and more predictable QoS of the networks makes for a better experience. And for providers, happier customers mean more revenue and longer customer retention. In this work, we propose a heuristic algorithm to compute the placement of the UAVs taking into account the locations of the users, their minimum QoS capacities, and the UAV service capacities. Empirical results show that our approach is superior to a planned server network in terms number of served users and fairness among base stations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".