Hierarchical Resource Optimization in Multi-UAV RIS-Assisted ISAC Networks With Uplink NOMA
Bibliographic record
Abstract
This paper investigates a novel spectral-efficient design for a multi-Unmanned Aerial Vehicle (UAV) system assisted by Reconfigurable Intelligent Surfaces (RIS) within the emerging Integrated Sensing and Communication (ISAC) framework. The proposed system leverages uplink Non-Orthogonal Multiple Access (NOMA) and RIS-enhanced multi-UAV collaboration to jointly serve mobile users and perform target sensing. A hierarchical double-timescale solution is introduced, combining an adaptive Affinity Propagation Clustering (APC) approach for long-term UAV deployment and user-target association, with a short-term iterative algorithm for optimizing user transmit power, UAV beamforming, and RIS phase shifts. To tackle the non-convex optimization problem, a solution is proposed, leveraging Lagrangian dual transform, fractional programming, and minorization methods. Simulation results validate the effectiveness of the proposed approach, demonstrating significant improvements in both communication data rates and sensing information rates compared to existing benchmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".