On the Security of Full-Duplex Relay-Assisted Underwater Acoustic Network With NOMA
Bibliographic record
Abstract
Wireless underwater acoustic (UWA) networks serve several civilian and military applications. The multiple reflections and dispersion, along with the long propagation delay limit the sum rate of UWA networks. Earlier works discussed adding full-duplex (FD), relay assistance, and non-orthogonal multiple access (NOMA) to enhance the system sum rate. Another challenge in UWA networks is the power limitation of devices. Hence, power optimization is crucial to maximize the energy efficiency. Furthermore, securing the UWA network against eavesdropping is essential to guarantee the confidentiality of communication. This work optimizes the power to maximize the secrecy sum rate (SSR) of a FD relay-assisted NOMA (FD-R-NOMA) underwater acoustic network subjected to an eavesdropper (Eve) attack. The network is studied in two states: when the network has or not the channel information (CI) of the threat. FD-R-NOMA UWA network shows to be more resilient to eavesdropping with higher secrecy energy efficiency when compared to the conventional half-duplex orthogonal multiple access network. Also, the results reveal that knowing the CI of the Eve improves the SSR of the network. Besides, the results show the effect of factors like the location of Eve, interference cancellation efficiency, noise in the environment, and sensor distributions in the system.
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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 | 0.003 |
| 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 | 0.002 |
| 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".