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, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">far-to-near</i> successive 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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".