Superimposed Training-Based Joint CFO and Channel Estimation for CP-OFDM Modulated Two-Way Relay Networks
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Bibliographic record
Abstract
Joint carrier frequency offset (CFO) and channel estimation is considered for two-way relay networks (TWRNs). Existing estimators provide only the convolved channel parameters and the mixed CFO values. In contrast, estimators using a superimposed training strategy are developed for the individual frequency and channel parameters. Depending on the number of pilots, three different estimators are developed. An iterative estimator with low complexity is also developed to further improve the estimation accuracy. The Cramér-Rao Bounds (CRBs) are derived. The simulations show that the iterative estimator converges rapidly, and the resultant estimation mean square error (MSE) approaches the CRB. For the special case of small CFO between the two source terminals, the MSE achieves the CRB at high SNRs, and the iterative algorithm is not necessary. However, for the general case, the gap between the MSE and the CRB indicates that there is room for further improvement of the estimation accuracy.
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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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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