Joint Optimization for Secure IRS-Assisted NOMA SWIPT Networks with Artificial Jamming
Bibliographic record
Abstract
Although intelligent reflecting surface (IRS) can reconfigure the propagation environment to enhance the performance of both non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT), the security remains a key challenge. We design a secure beamforming scheme for IRS-assisted NOMA SWIPT networks in this paper, where the artificial jamming is inserted into NOMA signals by the base station to ensure the network security with the aid of IRS. Specifically, we jointly optimize the transmit beamforming and jamming vectors, the IRS reflecting matrix and the power splitting ratio to maximize the sum rate, satisfying the rate requirement and energy harvesting threshold for each user. The optimization problem is difficult to be solved directly due to its non-convexity with coupled variables. Thus, we first apply auxiliary variables to reformulate it into a more tractable form, and then decompose it into three subproblems that can be converted into convex ones via successive convex approximation. Finally, we solve them iteratively using an alternating optimization algorithm. Simulation results validate that the proposed scheme can yield significant improvement in both secrecy performance and energy harvesting efficiency in comparison with benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".