Priority‐based resource optimisation and user association in integrated networks
Bibliographic record
Abstract
Abstract The future sixth‐generation (6G) networks are envisioned to integrate satellites, aerial, ground, and sea networks to provide seamless connectivity. However, some challenges are associated with integrated networks, including optimal resource utilisation, energy efficiency, delay, higher data rates, heterogeneity, and on‐demand connectivity. This paper focuses on optimising energy efficiency, resource utilisation, and task priority‐based user association. To achieve this, a mathematical framework is formulated to maximise energy efficiency, resource utilisation, and user connectivity in integrated networks while satisfying constraints related to transmit power, data rate, and computation resources. The formulated problem is a binary linear programming problem, as the decision variable is binary and the constraints are linear. The authors solve this optimisation problem using three methods: the branch and bound algorithm (BBA), the interior point method (IPM), and the barrier simplex algorithm (BSA). The authors use the results obtained from BBA as a benchmark to evaluate the performance of IPM and BSA. Simulation results show that the performance of IPM and BSA is comparable to the BBA but with lower complexity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".