Artificial-Noise-Aided Energy-Efficient Secure Beamforming for Multi-Eavesdroppers in Cognitive Radio Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we investigate optimal beamforming at a multiantenna primary base station (PBS) and a multiantenna cognitive base station (CBS) for energy-efficient (EE) secure downlink communication in cognitive radio networks with one single-antenna primary user (PU), one single-antenna cognitive user (CU), and multiple single-antenna eavesdropping nodes. An artificial noise transmission scheme is used by CBS to protect the data against the eavesdropping security attacks at the cost of extra power consumption. To improve the secrecy energy efficiency (SEE), we propose a SEE maximization (SEEM) scheme by exploiting the instantaneous channel state information (CSI) of the eavesdroppers under the secrecy rate (SR) constraints of the PBS-PU and CBS-CU channels, the quality-of-service requirement of the PU, and the transmit power constraint of the CBS. When the eavesdropping links' instantaneous CSI are unknown at the legitimate transmitters (i.e., PBS and CBS), we propose another SEEM scheme based on the statistical CSI of the eavesdropping links. Since the formulated optimization problems with fractional objective functions are nonconvex and mathematically intractable, we first transform them into equivalent subtractive problems, and then, employ the difference of two-convex functions approximation method to arrive at approximate convex problems. In addition, new two-tier optimal BF algorithms are proposed. Finally, simulation results are presented to illustrate the effectiveness and performance gains of our proposed SEEM schemes over conventional SR-only maximization and EE-only maximization schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it