Computation Offloading Strategies for LEO Satellite Edge Computing Systems Based on Different Multiple Access Methods
Bibliographic record
Abstract
Mobile Edge Computing (MEC) is pivotal for supporting compute-intensive and latency-critical applications in forthcoming mobile networks. It plays an essential role in providing network services through Low Earth Orbit (LEO) satellites, especially in demanding environments. This study proposes an Orthogonal Frequency Division Multiple Access (OFDMA)-based joint optimization framework for Offloading Decision and Dynamic Resource Allocation (ODDRA) Strategy. This framework dynamically allocates computing and bandwidth resources based on the current task load managed by LEO satellites. Moreover, it introduces a Time Division Multiple Access (TDMA)-based joint optimization for Offloading Decision and Task Offloading Sequence (ODTOS) Strategy. This strategy models the task offloading sequence as a permutation flow shop problem, targeting the minimization of total flowtime, and employs the heuristic Liu and Reeves algorithm (LR). The offloading decision challenge is addressed using matching theory and coalition game theory. Simulation outcomes demonstrate that the proposed strategies substantially decrease system delay and energy consumption relative to existing methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".