Artificial Noise Assisted Secure Interference Networks With Wireless Power Transfer
Bibliographic record
Abstract
Interference alignment (IA) is a remarkable technique to manage interference, and artificial noise (AN) can be utilized to combat one main threat of security, passive eavesdropping. Nevertheless, in the existing schemes, AN is only eliminated at each legitimate receiver, which is a waste of energy. In this paper, we propose an AN-assisted IA scheme with wireless power transfer. In the proposed scheme, AN is generated by each transmitter along with data streams, which can disrupt the eavesdropping without introducing any additional interference. Due to the fact that the transmit power of AN should be high enough to ensure the security, energy harvesting (EH) is also performed in the scheme. A power splitter is equipped at each receiver, which can divide the received signal, including desired signal, interference and AN, into two parts: one for information decoding and the other for EH. To optimize the antieavesdropping performance, the total transmit power of AN is maximized by jointly optimizing the information transmit power and the coefficient of power splitting, with the requirements of signal-to-interference-plus-noise ratio and harvested power satisfied. Due to the nonconvex nature of the problem, a suboptimal solution is also derived to calculate the closed-form solutions with extremely low computational complexity. Extensive simulation results are presented to show the effectiveness of the proposed scheme.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".