Multi-objective Resource Optimization in Space-Aerial-Ground-Sea Integrated Networks
Bibliographic record
Abstract
Sixth-generation (6G) networks envision space-air-ground-sea integrated (SAGSI) networks connecting satellite, aerial, ground, and sea networks to provide constant connectivity. A mathematical framework for maximizing energy efficiency, resource utilization, and user association in SAGSI networks is formulated. Different algorithms are analyzed to optimize user association while satisfying transmit power, data rate, and computation resources constraints. The binary decision variable associates users with ground, aerial, or satellite networks. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. To solve the formulated optimization problem using the branch and bound algorithm (BBA), interior point method (IPM), and barrier simplex algorithm (BSA). The results for BBA are used as a benchmark to evaluate the performance of the other two algorithms. Simulation results show that the performance of IPM and BSA is comparable to the BBA with low complexity.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".