Effective Capacity of Internet of Underwater Things Networks Enabled By Multi-Aperture UOWC
Bibliographic record
Abstract
Internet of Underwater Things (IoUT) is the underwater network of connected objects and systems that have a wide range of applications, ranging from undersea critical infrastructure monitoring and marine biodiversity observation to underwater surveillance. Underwater optical wireless communication (UOWC) systems, which are characterized by large bandwidth and high data rate, are a key enabler of IoUT networks. However, oceanic turbulence and pointing error (PE) are essential performance limiting factors for enabling UOWC in IoUT networks. In this paper, we investigate the quality-of-service (QoS) performance of multi-aperture UOWC-based IoUT networks while adopting the mixture exponential-generalized gamma (EGG) distribution to jointly model the oceanic turbulence fading and PE. To this end, we derive a closed-form expression of the effective capacity (EC) from evaluating the system’s maximum achievable data rate in the presence of EGG turbulence and PE subject to certain statistical delay constraints. Special cases of the EC for asymptotic high signal-to-noise ratio (SNR) regimes and loose statistical delay constraints are also analyzed. Through extensive simulations, we justify the validity of the derived EC expressions. Our numerical results also demonstrate that the multi-aperture technique improves EC in the EGC turbulence fading and PE channels, thereby supporting strict statistical delay constraints with a high data rate.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".