Resource Allocation for URLLC-Oriented Two-Way UAV Relaying
Bibliographic record
Abstract
Due to the high altitude and deployment flexibility, unmanned aerial vehicles (UAVs) can be used as relays to avoid obstacles and extend the coverage of wireless networks. On the other hand, ultra-reliable and low-latency communication (URLLC) is often required to deliver the information reliably and timely for many emerging applications. In this correspondence, we combine the advantages of both UAV and URLLC to investigate the resource allocation for a URLLC-enabled two-way UAV relaying system. Our goal is to maximize the transmission rate of the backward link with the constraint of URLLC requirement for the forward link. The optimization is non-convex and difficult to solve. Therefore, the optimization variables are divided to several blocks, and three sub-problems are formulated and solved. Finally, an iterative algorithm is proposed to solve these sub-problems alternately. Simulation results show that the proposed joint optimization scheme can achieve excellent performance for the URLLC-enabled two-way UAV relaying system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".