Physical-Layer Security on Maximal Ratio Combining for SIMO Cognitive Radio Networks Over Cascaded κ-μ Fading Channels
Bibliographic record
Abstract
This paper investigates the physical-layer security (PLS) of a single-input-multiple-output (SIMO) underlay cognitive radio network (CRN) over cascaded$\kappa $-$\mu $fading channels. A secondary user (SU) transmitter communicates with a secondary user receiver over cascaded$\kappa $-$\mu $channels. Moreover, an eavesdropper residing in the SUs transmission’s range would be capable of intercepting the SUs’ transmission. The SU destination and the eavesdropper are both assumed to be equipped with multiple antennas. Both of the receivers adopt maximal-ratio combining (MRC) over the multiple copies of the signal. In an underlay CRN, the SU transmitter should keep adjusting the transmission power to ensure not to disturb the primary users’ (PUs) transmission. PLS is analyzed in terms of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity$(P_{nz})$. Results indicate the evident effect of the cascade level and the number of antennas at the eavesdropper over the secrecy of the SUs pair. In addition, results reveal that PLS can be strengthened by increasing the number of antennas at the legitimate receiver. The impact of the interference level tolerable by the PU receiver over SUs’ secrecy is also analyzed. Monte-Carlo simulations and analytical results are presented to assess the system performance.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".