Joint User Association, Power Optimization and Trajectory Control in an Integrated Satellite-Aerial-Terrestrial Network
Bibliographic record
Abstract
Internet-of-Things (IoT) is being widely embraced with the number of connected devices growing rapidly. Moreover, IoT applications are emerging in diverse verticals such as connected cars, connected factories, and smart agriculture. For new business models, in order to meet key network performance indicators, connectivity must be flexible and agile. An integrated satellite-aerial-terrestrial network (I-SAT) has recently stimulated interest in providing wireless communication due to its high maneuverability, versatile deployment, and pervasive connectivity. The resource planning, task distribution, and action management of an I-SAT can be accomplished through effective acquisition, coordination, transmission, and aggregation of diverse information. This paper considers an I-SAT network, in which multiple unmanned aerial vehicles (UAVs) with aerial stations and a terrestrial base station (BS), in a cognitive setting, in the presence of satellite-receiver communication, are deployed to support smart vehicles on the ground. By taking into account different limitations and Quality of Service (QoS) constraints, the goal is to maximize the average throughput among users by jointly optimizing user association, BS/UAV transmission power, and UAV trajectory. The formulated problem is a non-convex optimization problem with a complicated expression that is hard to solve. To tackle this problem, an alternating iterative algorithm based on the block descent method is proposed. Precisely, the problem is subdivided into three subproblems, transmitter-vehicle association optimization, BS/UAV power allocation optimization, and UAV trajectory control. Then, in an iterative process, these subproblems are solved sequentially. The proposed solution uses a segment-by-segment technique, which breaks the complete UAV flight trajectory into smaller time segments to reduce computation time when the network service period is considerable. As a result, each time segment’s optimization can be solved more quickly. Furthermore, the paper presents the results of network simulations carried out to assess the efficiency of the proposed solution. The findings show that the presented scheme outperforms different benchmark schemes in terms of the average user throughput when observing multiple different scenarios.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".