Power allocation policy and performance analysis of secure and reliable communication in cognitive radio networks
Bibliographic record
Abstract
This paper investigates the problem of secure and reliable communications for cognitive radio networks. More specifically, we consider a single input multiple output cognitive model where the secondary user (SU) faces an eavesdropping attack while being subject to the normal interference constraint imposed by the primary user (PU). Thus, the SU must have a suitable power allocation policy which does not only satisfy the constraints of the PU but also the security constraints such that it obtains a reasonable performance for the SU, without exposing information to the eavesdropper. We derive four power allocation policies for different scenarios corresponding to whether or not the channel state information of the PU and the eavesdropper are available at the SU. Further, we introduce the concept secure and reliable communication probability (SRCP) as a performance metric to evaluate the considered system, as well as the efficiency of the four power allocation policies. Finally, we present numerical examples to illustrate the power allocation polices, and the impact of these policies on the SRCP of the SU.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".