Improving Connectivity of RIS-Assisted UAV Networks using RIS Partitioning and Deployment
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) is pivotal for beyond 5G networks in regards to the surge demand for reliable communication in unmanned aerial vehicle (UAV) networks. This paper presents an innovative approach to maximize connectivity of UAV networks using RIS deployment and virtual partitioning, wherein an RIS is deployed to assist in the communications between an user-equipment (UE) and blocked UAVs. Closed-form (CF) expressions for signal-to-noise ratio (SNR) of the two-UAV setup are derived and validated. Then, an optimization problem is formulated to maximize network connectivity by optimizing the 3D deployment of the RIS and its partitioning subject to predefined quality-of-service (QoS) constraints. To tackle this problem, we propose a method of virtually partitioning the RIS given a fixed 3D location, such that the partition phase shifts are configured to create cascaded channels between the UE and the blocked two UAVs. Then, simulated-annealing (SA) method is used to find the 3D location of the RIS. Simulation results demonstrate that the proposed joint RIS deployment and partitioning framework can significantly improve network connectivity compared to benchmarks, including RIS-free and RIS with a single narrow-beam link.
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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".