Priority‐based resource optimisation and user association in integrated networks
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
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.
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