Further Results on the Performance of Minimum Variance Channel Estimation Algorithms for MC-CDMA Systems
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Bibliographic record
Abstract
A comprehensive performance analysis of the minimum variance (MV) channel estimator for multicarrier code-division multiple access (MC-CDMA) systems is presented. In particular, we provide closed form expressions for the asymptotic bias and the mean-square-error (MSE) of the estimates as well as the corresponding Cramer-Rao bound (CRB). The derived formula for the bias due to the additive noise characterizes accurately the behavior of the actual MV estimator in the case of heavy system loading or small processing gain. In addition, in the derivation of the CRB, we suggest a novel approach which assumes the knowledge of only the spreading code of the desired user. This approach results in a tighter bound than the CRB derived based on the knowledge of all users' signatures.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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