Iterative joint channel and noise variance estimation and primary user signal detection for cognitive radios
Bibliographic record
Abstract
In this paper, we introduce a joint channel and noise variance estimation, and primary user (PU) signal detection scheme using the expectation-maximization (EM) algorithm for cognitive radios. In our investigation, we consider two scenarios: In the first scenario, the channel and noise variance are assumed to be perfectly known by the secondary user (SU). Here, we propose a maximum-likelihood (ML) solution of the PU signal detection as an upper bound on the performance of the proposed joint estimation and detection (JED) scheme. We also provide an iterative implementation of the ML-based detector using the EM algorithm. In the second case, we extend our work to the problem of channel and noise variance estimation in cognitive radios, where we propose an iterative JED scheme based on the EM algorithm. The simulation results show that the proposed JED scheme can iteratively attain a reliable performance with few iterations and modest computational complexity.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".