Two channel estimation methods for amplify-and-forward relay networks
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Bibliographic record
Abstract
In this paper, we investigate the performance of amplify-and-forward relaying with two different pilot-symbol-assisted channel estimation methods. In the first estimation method, the cascaded channel consisting of source-to-relay and relay-to-destination links is estimated at the destination terminal. In the second estimation method, the estimation of cascaded channel is disintegrated into separate estimations of source-to-relay and relay-to-destination links which are carried out at the relay and destination terminals, respectively. The latter method involves feed-forwarding a quantized version of the source-to-relay channel estimate to the destination terminal. Our simulation results demonstrate that cascaded channel estimator outperforms its competitor with small number of quantization bits. As the number of employed quantization bits increase, disintegrated channel estimator approaches to its competitor eventually outperforming it.
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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 | 0.000 |
| Open science | 0.001 | 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