Re-configuration of UAV Relays in 6G Networks
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) can be widely applied as aerial relays in 5G and beyond to provide communication services, since they are cost effective, provide large coverage, and can be deployed fast. In a UAV relaying network, each UAV is responsible for the communication of its ground cell. When a ground user intends to transmit traffic to a destination ground user, the data might be transmitted in a multi-hop manner over several UAVs. To this end, each UAV can transmit data to its neighboring UAVs over air-to-air wireless links, which in general have limited capacity. As data transmission is needed between multiple pairs of ground users, several connections will exist simultaneously in the UAV network. Each connection has a bandwidth and occupies some specific time slots of air-to-air links. Due to link failures and network agility, network resources might not be efficiently utilized by the connections, resulting in the refusal of new connections. Therefore, route management and resource allocation should be frequently renewed in UAV-assisted networks. In this paper, using an integer linear programming formulation, we first derive an algorithm for route refreshment. Since the link capacity is limited, the resource assignment of new routes plays an important role in the connection migration. The second goal of this paper is deriving a necessary and sufficient condition for the existence of disruption-free connection migration. Finally, we study the average percent of disruption and resource utilization reduction in a sample UAV relaying network.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".