Computation Offloading, UAV Placement, and Resource Allocation in SAGIN
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
We study the computation offloading and resource allocation optimization in space-air-ground integrated networks (SAGIN), where computation tasks are partitioned into subtasks which are executed locally at the ground users (GUs) and/or offloaded and executed at the edge servers deployed on the associated unmanned aerial vehicles (UAVs) or a cloud server, which can be reached via multihop communications over multiple low-earth-orbit (LEO) satellites. Our design aims to minimize the weighted energy consumption while satisfying the maximum delay constraints of underlying tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we employ the alternating optimization approach where we iteratively optimize the user association, partial offloading control, computation resource and bandwidth allocation, and UAV placement until convergence. In addition, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex bandwidth allocation and UAV placement sub-problems. Via numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under different network settings. Specifically, our design improves about 35 – 40% in the weighted sum of energy compared to the baselines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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