Secrecy Performance of Small-Cell Networks With Transmitter Selection and Unreliable Backhaul Under Spectrum Sharing Environment
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Bibliographic record
Abstract
We investigate the secrecy performance of an underlay small-cell cognitive radio network under unreliable backhaul connections. The small-cell network shares the same spectrum with the primary network, ensuring that a desired outage probability constraint is always met in the primary network. To improve the security of the small-cell cognitive network, we propose three sub-optimal small-cell transmitter selection schemes, namely sub-optimal transmitter selection, minimal interference selection, and minimal eavesdropping selection. Closed-form expressions of the non-zero secrecy rate, secrecy outage probability, and ergodic secrecy capacity are provided for the schemes along with asymptotic expressions. We also propose an optimal selection scheme and compare performances with the sub-optimal selection schemes. Computable expressions for the non-zero secrecy rate and secrecy outage probability are presented for the optimal selection scheme. Our results show that by increasing the primary transmitter's power and the number of small-cell transmitters, the system performance improves. The selection scheme, the backhaul reliability, and the primary user quality-of-service constraint also have a significant impact on secrecy performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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