Impact of secondary user communication on security communication of primary user
Bibliographic record
Abstract
Abstract Cognitive radio network concept has been considered as a promising solution to improve the spectrum utilization. However, it may be vulnerable to security problems as the primary user (PU) and secondary user (SU) access the same resource. In this paper, we consider a system model where an eavesdropper (EAV) illegally listens to the PU communication in the presence of a SU transmitter (SU‐Tx) communicating with a SU receiver (SU‐Rx). The SU‐Tx transmit power is subject to the peak transmit power constraint of the SU and outage probability constraint of the PU. Given this context, the effect of the interference from the SU‐Tx to the EAV on the primary system security is investigated. In particular, analytical expressions of the probability of existence of non‐zero secrecy capacity and secrecy outage probability of the PU are derived. Moreover, the performance analysis of the secondary network is examined where closed‐form expressions of the symbol error probability and achievable rate are presented. Numerical examples are provided to evaluate the impact of the primary system parameters and channel conditions among users on the system performance of secondary and primary networks. Interestingly, our results reveal a fact that the security of the primary network strongly depends on the channel condition of the SU‐Tx to the EAV link and the transmit power policy of the SU‐Tx. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".