Toward Facilitating Power Efficient URLLC Systems in UAV Networks Under Jittering
Bibliographic record
Abstract
Consumer electronics can support sixth-generation (6G) systems and their services, including ultra-reliable and low-latency communications (URLLC). In this context, unmanned aerial vehicles (UAVs) have become increasingly popular because they can: be dynamically positioned, take advantage of channel gains, and communicate directly via line-of-sight. UAVs, on the other hand, are unable to maintain a stable flight for prolonged periods of time and suffer from jittering impairments caused by strong winds. As a result of atmospheric conditions and environmental interference, the perfect channel state information (CSI) becomes obsolete. The aim of this study is to propose a power-efficient resource allocation scheme for URLLC-enabled UAV communication systems under finite block lengths, imperfect CSIs, and adverse jittering effects caused by wind. This involves optimizing UAV positioning and blocklength distribution together. Additionally, we propose a perturbation-based semidefinite programming (SDP) approach to reduce the sum power and demonstrate that it outperforms fixed benchmark algorithms. As such, it can reach power savings up to 77.18% compared to fixed benchmark algorithms. Our extensive simulation results demonstrate that our approach performs similarly to the exhaustive search and has low complexity. Thus, the proposed method thus provides a practical power-efficient URLLC implementation for memory-constrained UAV networks.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".