Error Performance Analysis of UAV-Mounted RIS for NOMA Systems With Practical Constraints
Bibliographic record
Abstract
Uncrewed aerial vehicles (UAVs) have attracted recent attention for sixth-generation (6G) networks due to their low cost and flexible deployment. In order to maximize the ever-increasing data rates, spectral efficiency, and wider coverage, technologies such as reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are adapted with UAVs (UAV-RIS NOMA). However, the error performance of UAV-RIS NOMA has not been considered, yet. In this letter, we investigate the error probability of UAV-RIS NOMA systems. We also consider the practical constraints of hardware impairments (HWI) at the transceivers, inter-cell interference (ICI), and imperfect successive interference cancellation (SIC). The analytical derivations are validated by Monte-Carlo simulations. Our results demonstrate that our proposed system achieves higher performance gain (more than 5 dB with increasing the number of RIS elements) with less error probability compared to UAVs without RIS. Moreover, it is found that the HWI, ICI, and imperfect SIC have shown a negative impact on the system performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".