Recursive channel estimation for wireless communication via the EM algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The on-line expectation-maximization (EM) algorithm along with stochastic approximations are employed in this paper to estimate unknown time-invariant/variant parameters recursively in an adaptive manner based on the maximum likelihood (ML) criterion. The impulse response of a linear transmission channel is modeled in different ways; as an unknown deterministic vector/process and as an Gaussian vector/process with unknown stochastic characteristics. In association with these channel impulse response (CIR) models, different types of recursive least squares (RLS) and Kalman filtering and smoothing algorithms are derived directly from the on-line EM algorithm. The EM algorithm as a powerful tool unifies the derivations of some adaptive estimation methods (which include RLS and Kalman) whose original criterion is minimum mean square error (MMSE), but under linear and Gaussian conditions can achieve ML or maximum a posterior (MAP) criterion.
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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