Quasi-Optimization of Distance and Blocklength in URLLC Aided Multi-Hop UAV Relay Links
Bibliographic record
Abstract
Achieving ultra-high reliability for short packets is a core challenge for future wireless communication systems, as current systems are designed only to transmit long packets based on classical information-theoretic principles. To tackle this challenge, this letter relies on multi-hop unmanned aerial vehicle (UAV) relay links to deliver short ultra-reliable and low-latency (URLLC) instruction packets between ground Internet of Things (IoT) devices. To accomplish this task, we perform non-linear optimization to minimize the overall decoding error probability in order to find the optimal values of the distance and the blocklength. In this vein, a novel, semi-empirical based non-iterative algorithm is proposed to solve the quasi-optimization problem. The algorithm executes in quasilinear time and converges to a globally optimal/sub-optimal solution based on the chosen parameters. Simulation results demonstrate that our algorithm allows operation under the ultra-reliable regime (URR), and yields the same performance as exhaustive search algorithms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".