RIS Alignment via Virtual Partitioning for Resilient Uplink Multi-RIS-Assisted UAV Communications
Bibliographic record
Abstract
The integration of reconfigurable intelligent surfaces (RISs) and unmanned aerial vehicle (UAV) communications has emerged as a promising solution for improving link quality and massive connectivity for beyond 5G wireless networks. This paper presents an innovative approach to maximizing connectivity of uplink multi-RIS-assisted UAV networks enabled by RIS placement and virtual partitioning, wherein RISs are deployed to assist in the communications between user-equipment (UEs) and UAVs. In the considered model, the UEs intend to transmit data to the UAVs, and RISs can assist in improving network connectivity by connecting the UEs to the blocked UAVs. First, exact and approximated closed-form (CF) expressions for signal-to-noise ratio (SNR) are derived based on aligned and non-aligned portions of the RISs. Then, we formulate the problem of maximizing the network connectivity that jointly considers 1) UE-RIS-UAV link selection and 2) RIS placement and virtual partitioning. This problem is a computationally expensive combinatorial optimization. Using the block coordinate descent (BCD) approach, we propose novel UE-RIS-UAV selection and RIS placement and partitioning methods. Specifically, we develop clustering and perturbation methods for UE-RIS-UAV selection, and derive a closed-form solution for the partitioning of the RISs. Moreover, for optimizing the RISs placement, Adam optimizer is used. Simulation results demonstrate that the proposed approaches yield a gain in the range of 12% to 45% compared to benchmark schemes. The finding emphasizes the potential of integrating RIS with UAV communications as a robust and reliable connectivity solution for future wireless communication systems.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".