Joint Computation and Communication Resource Allocation for Unmanned Aerial Vehicle NOMA Systems
Bibliographic record
Abstract
This paper explores the integration of unmanned aerial vehicle (UAV) systems in a dual-function capacity, serving as both remote base stations and mobile edge servers for offloaded computational tasks from remote users utilising non-orthogonal multiple access (NOMA) scheme. This challenge of minimising latency is addressed by formulating a comprehensive problem that encompasses key computation and communication variables, such as user transmit power, user association, and computing resource allocation. The complexity arises from the intricate interplay between binary and continuous variables, as well as the presence of non-convex constraints. To overcome these challenges, we present an innovative alternating optimisation approach that iteratively tackles the problem. The effectiveness of the proposed solution in reducing total latency and optimising the resource allocation within the considered system model is demonstrated using simulations. Furthermore, this work sheds light on the potential of leveraging UAV systems for enhancing communication and computation performance, offering insights into practical strategies for latency-sensitive applications.
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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) | 0.000 | 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 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".