Estimation of Primary User Parameters in Cognitive Radio Systems via Hidden Markov Model
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Bibliographic record
Abstract
For a cognitive radio (CR) system, we investigate the estimation problem in which a secondary user (SU) estimates the channel parameters such as the sojourn times on the active and the inactive states of the primary user (PU) as well as the PU signal strength on the basis of the sequence of the sensing results. By modeling the CR system as a hidden Markov model (HMM), the channel parameters are estimated by the standard expectation-maximization (EM) algorithm. We focus on mathematically analyzing the condition under which the EM algorithm can find the true channel parameters. For this, we apply the theory of the equivalence and the identifiability to the proposed HMM model for a CR system. Based on the identifiability analysis, we propose a parameter estimation algorithm for our problem by extending the EM algorithm. The numerical results show that the proposed algorithm successfully estimates the true channel parameters as long as the condition for finding the channel parameters is satisfied.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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