RIS Assisted Near-Field NOMA Communications: A Security-Fairness Trade-Off
Bibliographic record
Abstract
A reconfigurable intelligent surface (RIS) assisted near-field secure non-orthogonal multiple access (NOMA) communication system is investigated. In particular, a challenging secure NOMA communication scenario is considered, where the user closing to the RIS is untrusted. Exploiting the near-field beamfocusing capability, afar-to-nearsuccessive interference cancellation (SIC) operation is employed to facilitate the secure NOMA communications. Based on this, the trade-off between security and fairness is characterized by maximizing the weighted sum of security capacity and minimum capacity. An alternating optimization based algorithm is developed to solve this highly coupled problem, where RIS beamforming and power allocation are optimized using semidefinite relaxation and successive convex approximation methods, respectively. Numerical results demonstrate: (1) The secure communication for the far user can be achieved in the near field but is impossible in the far field; (2) As the distance between two users increases, the security capacity initially increases and then decreases, while the minimum capacity continuously declines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".