Bibliographic record
Abstract
In this paper, we investigate the performance of several advanced discrete-time RAKE receivers under the effect of channel statistics estimation errors. The name of the first receiver is the decorrelating discrete-time RAKE receiver (D-DTR), which improves the performance in the presence of channel estimation errors in diffuse channels. The second receiver is the discrete-time version of the generalized RAKE (G-DTR) receiver. The G-DTR has been proposed for correlated interference mitigation. The last system is the generalized decorrelating discrete-time RAKE receiver (GD-DTR), which combines the benefits of the D-DTR and the G-DTR. In the literature, it is shown that the D-DTR is sensitive to the estimation of the channel covariance matrix. In this work, we reduced this sensitivity and according to our results in worst case scenario the performance of the D-DTR is equal to a conventional discrete-time RAKE receiver (C-DTR). For the case of the GD-DTR, we introduce a very simple method for the estimation of the noise plus interference statistics and we modify the channel statistics estimation method of the D-DTR to be valid for the GD-DTR. Our simulations show that the GD-DTR needs an estimation quality above some threshold for a performance gain compared to the C-DTR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".