Task Offloading Optimization for UAV-Aided NOMA Networks With Coexistence of Near-Field and Far-Field Communications
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Bibliographic record
Abstract
Mobile edge computing (MEC) is widely employed to allow users to offload computation-intensive tasks due to high energy efficiency, low latency, enhanced privacy, and security. Thanks to advances in manufacturing technologies, MEC-based unmanned aerial vehicle (UAV) networks can be extensions or replacements for edge servers at ground base stations to improve the network flexibility and quality of communication. This study focuses on the non-orthogonal multiple access (NOMA) scheme, emphasizing the coexistence of near-field and far-field regions, particularly in the context of multiple UAVs integrated with edge servers. We address the challenge of the latency minimization problem by efficiently optimizing both communications and computing variables such as user association, capacity allocation, and transmit power. The designed optimization problem is a mixed integer programming problem that has extremely high complexity. To solve this problem, we propose an iterative algorithm that is designed by using block coordinate descent, convex transformation, and relaxation. Through extensive simulations, our proposed solution demonstrates effectiveness in minimizing total task offloading latency across various scenarios. The findings not only contribute a practical convex optimization method to reduce the latency in MEC systems using UAV-aided NOMA networks but also enable the operations of modern applications such as augmented reality and virtual reality on handheld user devices.
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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 | 0.000 | 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 | 0.000 | 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 it