Capacity Analysis of NOMA-Enabled Underwater VLC Networks
Bibliographic record
Abstract
Visible light communication (VLC) has recently emerged as an enabling technology for high capacity underwater wireless sensor networks. Non-orthogonal multiple access (NOMA) has been also proven capable of handling a massive number of sensor nodes while increasing the sum capacity. In this paper, we consider a VLC-based underwater sensor network where a clusterhead communicates with several underwater sensor nodes based on NOMA. We derive a closed-form expression for the NOMA system capacity over underwater turbulence channels modeled by lognormal distribution. NOMA sum capacity in the absence of underwater optical turbulence is also considered as a benchmark. Our results reveal that the overall capacity of NOMA-enabled Underwater VLC networks is significantly affected by the propagation distance in underwater environments. As a result, effective wireless transmission at high and moderate spectral efficiency levels can be practically achieved in underwater environments only in the context of local area networks. Moreover, we compare the achievable capacity of NOMA system with its counterpart, i.e., orthogonal frequency division multiple access (OFDMA). Our results reveal that NOMA system is not only characterized by achieving higher sum capacity than the sum capacity of its counterpart, OFDMA system. But also, the distances between sensor nodes and the clusterhead for achieving the highest sum capacity in these two multiple access systems are different.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".