Integrated Computation Offloading, UAV Trajectory Control, Edge-Cloud and Radio Resource Allocation in SAGIN
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines.
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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 | 0.000 | 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 it