Effectiveness of Reconfigurable Intelligent Surfaces to Enhance Connectivity in UAV Networks
Bibliographic record
Abstract
Reconfigurable intelligent surfaces (RISs) have drawn considerable attention due to their ability to introduce controllable phase-shifts onto impinging electromagnetic waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs) are expected to make future 6G networks more connected, but they are prone to several failures, which cause network disintegration. To harness the benefits of both, we study their integration to improve connectivity of multi-RIS-assisted UAV networks. We first propose to define the criticality of nodes, which reflects the importance of some nodes over other nodes. We then employ the algebraic connectivity metric, which is adjusted by the reflected links of the RISs and their criticality weights, to formulate the problem of maximizing the network connectivity. Such problem is a computationally expensive combinatorial optimization. Using a relaxation method where the discrete scheduling constraint of the problem is relaxed to be continuous, we propose two efficient solutions, namely semi-definite programming (SDP) optimization and Laplacian matrix perturbation, which both solve the problem in polynomial time. We rigorously derive the lower and upper bounds of the algebraic connectivity obtained from the perturbation solution. Simulation results compare the performance of the proposed solutions with different schemes, including without RISs, unoptimized link scheduling and phase shifts, greedy search, and optimal. The results show that the proposed schemes achieve considerably improved performance with low computational complexity compared to other schemes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".