Optimization of RIS-Assisted RSMA-Enabled Tethered-UAV Communications
Bibliographic record
Abstract
Several technologies have been driving the development of next-generation wireless networks, including unmanned aerial vehicles (UAVs), reconfigurable intelligent surfaces (RISs), and rate-splitting multiple access (RSMA). UAVs, in particular tethered UAVs (TUAVs), are seen as an enabler of ubiquitous and continuous cellular services. Similarly, RIS technology can improve spectral efficiency by smartly directing radio signals toward receivers, while RSMA emerged as an efficient multiple-access technique that controls interference to serve multiple users. To benefit from these paradigms, we explore here a RIS-assisted RSMA-enabled TUAV communication system and investigate the joint optimization of its parameters. Specifically, we formulate the joint TUAV placement, RIS phase-shift configuration, and RSMA parameters optimization problem to maximize the weighted sum data rate (WSR) of ground users. Due to the problem's complexity, we study the sub-problems of phase-shift optimization, RSMA precoding and rate-splitting, and TUAV placement. Through the combination of the proposed solutions, we present a novel exhaustive/alternating optimization-based algorithm. The obtained results illustrate the efficiency of our method in enhancing the WSR performance. Then, through an impact study of parameters such as TUAV altitude, RIS size, and location, we provide novel insights into the design of RIS-assisted RSMA-enabled TUAV systems.
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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 | 0.001 |
| 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".