Joint Service Deployment and Mobile Edge Computing in Space-Air-Ground Integrated Network
Bibliographic record
Abstract
To cope with the constraints of conventional mobile edge computing (MEC) in coverage, flexibility, and capacity, space-air-ground integrated network (SAGIN) is broadly expected to provide ubiquitous service access and data offloading. However, the diversity of user requirements and heterogeneity of resource challenges in network management and resource allocation. In this paper, we study the joint service deployment and computing offloading problem in SAGIN. Considering the constraints on capacity, energy, and user requirements, the problem is formulated as an integer non-linear programming (INLP) problem to minimize the total latency. Then, a two-stage graph-attention-network (GAT)-based deep reinforcement learning approach is proposed to jointly optimize the service deployment, computation resource allocation, network pairing, and service access under various user requirements on service type, task dependency, and data volume. Simulation results demonstrate that our proposed approach outperforms the benchmark by a substantial margin in terms of average latency.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".