Spatial Configuration of Agile Wireless Networks with Drone-BSs and\n User-in-the-loop
Bibliographic record
Abstract
Agile networking can reduce over-engineering, costs, and energy waste.\nTowards that end, it is vital to exploit all degrees of freedom of wireless\nnetworks efficiently, so that service quality is not sacrificed. In order to\nreap the benefits of flexible networking, we propose a spatial network\nconfiguration scheme (SNC), which can result in efficient networking; both from\nthe perspective of network capacity, and profitability. First, SNC utilizes the\ndrone-base-stations (drone-BSs) to configure access points. Drone-BSs are\nshifting paradigms of heterogeneous wireless networks by providing radically\nflexible deployment opportunities. On the other hand, their limited endurance\nand potential high cost increase the importance of utilizing drone-BSs\nefficiently. Therefore, secondly, user mobility is exploited via\nuser-in-the-loop (UIL), which aims at influencing users' mobility by offering\nincentives. The proposed uncoordinated SNC is a computationally efficient\nmethod, yet, it may be insufficient to exploit the synergy between drone-BSs\nand UIL. Hence, we propose joint SNC, which increases the performance gain\nalong with the computational cost. Finally, semi-joint SNC combines benefits of\njoint SNC, with computational efficiency. Numerical results show that\nsemi-joint SNC is two orders of magnitude times faster than joint SNC, and more\nthan 15 percent profit can be obtained compared to conventional systems.\n
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".