SFET-Based Multiple Antenna Spectrum Sensing Using the Second Order Moments of Eigenvalues
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a blind scenario for multiantenna spectrum sensing in which both the channel gains and noise variance are assumed to be unknown. For such a scenario, we find an appropriate Separating Function (SF) whose Maximum Likelihood Estimate (MLE) leads us to a SFET-based detector which uses the second order moments of the eigenvalues of the Sample Covariance Matrix (SCM). We also find closed-form expressions for the detection and false-alarm probabilities of the proposed detector. The performance of the proposed detector asymptotically tends to that of the Uniformly Most Powerful Unbiased (UMPU) detector as the number of independent and identically distributed observations increases. In addition, simulation results show that the proposed detector outperforms the state-of-art eigenvalue- based detectors because of using the second order moments of the SCM eigenvalues.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| 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