Performance of RSMA-Based UOWC Systems Over Oceanic Turbulence Channel With Pointing Errors
Bibliographic record
Abstract
Underwater optical wireless communication (UOWC) systems face significant challenges due to oceanic turbulence and pointing errors (PE), which can degrade system performance. Moreover, interference is another challenge in UOWC, considering the consistent increase in underwater optical devices. In this study, the performance of rate splitting multiple access (RSMA)-based UOWC systems is investigated over an exponential-generalized gamma (EGG)-distributed oceanic turbulence channel with generalized PE. The RSMA scheme is employed to facilitate communication between a source and multiple users in the UOWC system while accounting for the combined effects of oceanic turbulence and generalized PE. The statistical characterization of the UOWC system is analyzed, including the probability density function (PDF) of the signal-to-noise ratio (SNR), outage probability, throughput, and sum ergodic capacity. Additionally, closed-form expressions for the outage probability and throughput are derived, and asymptotic expressions for the outage probability in the high SNR regime are provided. Moreover, the diversity order of the system is evaluated, and the impact of different parameters on the system performance is discussed. Our results demonstrate the effectiveness of the RSMA-based UOWC system in mitigating the adverse effects of oceanic turbulence and PE while achieving improved performance in terms of capacity and outage probability. The findings of this study provide valuable insights for the design and optimization of UOWC systems in challenging underwater environments.
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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 | 0.001 | 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 | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".