Deep Neural Network-Based Precoder for Fairness Aware Secure NOMA Scheme
Bibliographic record
Abstract
This work proposes an artificial noise (AN)-aided secure multiple-input single-output non-orthogonal multiple access (NOMA) scheme. In the design of the proposed scheme, we consider fairness that all the users have higher secrecy rates as compared to those given by a competing orthogonal multiple access (OMA) scheme. Despite its importance, the fairness aware design has remained rarely touched since it is mathematically intractable. This work shows that this problem can be efficiently solved by utilizing a deep neural network as the precoder for the information and AN signals even without resorting to some assumptions such as a large antenna array and/or a high signal-to-noise ratio. We will also propose an adaptive mode that switches the access protocol from the OMA scheme to the NOMA scheme only when the fairness is met. Performance of the proposed secure NOMA scheme will be extensively evaluated and compared with existing NOMA and OMA schemes. The comparisons clearly show that the sum secrecy rate can be significantly improved while guaranteeing the fairness, which however cannot be achieved with the existing NOMA scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".