Physical-Layer Security with Multiuser Scheduling in Cognitive Radio Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider a cognitive radio network that consists of one cognitive base station (CBS) and multiple cognitive users (CUs) in the presence of multiple eavesdroppers, where CUs transmit their data packets to CBS under a primary user's quality of service (QoS) constraint while the eavesdroppers attempt to intercept the cognitive transmissions from CUs to CBS. We investigate the physical-layer security against eavesdropping attacks in the cognitive radio network and propose the user scheduling scheme to achieve multiuser diversity for improving the security level of cognitive transmissions with a primary QoS constraint. Specifically, a cognitive user (CU) that satisfies the primary QoS requirement and maximizes the achievable secrecy rate of cognitive transmissions is scheduled to transmit its data packet. For the comparison purpose, we also examine the traditional multiuser scheduling and the artificial noise schemes. We analyze the achievable secrecy rate and intercept probability of the traditional and proposed multiuser scheduling schemes as well as the artificial noise scheme in Rayleigh fading environments. Numerical results show that given a primary QoS constraint, the proposed multiuser scheduling scheme generally outperforms the traditional multiuser scheduling and the artificial noise schemes in terms of the achievable secrecy rate and intercept probability. In addition, we derive the diversity order of the proposed multiuser scheduling scheme through an asymptotic intercept probability analysis and prove that the full diversity is obtained by using the proposed multiuser scheduling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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