Connectivity Maximization in UAV Networks using RIS Placement and SDP Optimization
Bibliographic record
Abstract
In this paper, we study the problem of placing a reconfigurable intelligent surface (RIS) and tuning its reflected links to improve the resiliency and connectivity of uncrewed aerial vehicle (UAV) networks. We formulate an optimization problem of maximizing network connectivity that jointly optimizes RIS position, its phase shift, and UE-RIS-UAV link scheduling. Such problem is computationally expensive combinatorial optimization. To tackle this problem, we first design the phase control strategy at the RIS for the UE-RIS-UAV link scheduling. With such design, we propose an optimal linear search method, which has high computational complexity for large networks. Then, leveraging the convex relaxation method and the designed phase strategy, we propose another solution using semi-definite programming (SDP) optimization, which solves the problem in polynomial time. Simulation results show that our proposed solutions outperform other benchmark schemes.
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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 | 0.000 |
| 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) | 0.000 | 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".