Evaluation of predetection diversity techniques for RAKE receivers
Bibliographic record
Abstract
This paper extends the results presented by Eng, Kong and Milstein (see IEEE Trans. Commun., vol.44, p.1117-29, 1996) to include the presence of a direct-path component, and subsequently, to investigate the efficacy of an Mth order pre-selection maximal-ratio combiner (M-SCMRC) in different fading environments. The performance of a maximal-ratio combiner with finite tap decisions (M-MRC), and the optimum linear diversity combiner (L-MRC) which combines all the L resolvable multipaths, is also evaluated for comparison. The results reveal that M-SCMRC outperforms M-MRC, although the combination of a strong specular component and a heavily decayed multipath intensity profile may result in only a minimal degradation of the receiver performance for an M-MRC RAKE receiver.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".