Risk-Aware Fast Initial 3D Beam Alignment for UAV-Assisted mmWave/THz URLLC
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are emerging as key enablers for extending coverage and reliability in next-generation wireless networks using millimeter-wave (mmWave) and terahertz (THz) links. However, their narrow directional beams make initial cell search and alignment challenging under stringent latency and reliability demands. We study a risk-aware beam alignment problem where both the expected access delay and its variability are minimized under strict reliability constraints. To tackle this, we develop the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), designed to balance convergence speed and computational efficiency. Simulations confirm that LSRFDA achieves faster alignment, lower latency, and higher reliability compared to Particle Swarm Optimization (PSO) and random search, making it suitable for UAV-assisted mmWave/THz URLLC and HRLLC scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".