An improved decorrelator-based multiuser receiver
Bibliographic record
Abstract
In this paper an algorithm based on the decorrelator multiuser detector is proposed. The main achievement is the introduction of an architecture, which is moderately more complex than, but offers a superior performance to, the decorrelator receiver. The conventional receiver is optimized to combat the Additive white Gaussian noise (AWGN), while the purpose of the decorrelator receiver is the complete elimination of the multiple access interference (MAI). The proposed algorithm uses the conventional detector output in conjunction with the decorrelator output in arriving at its decisions, therefore a substantial improvement could be expected. For a synchronous CDMA system, our simulation results show a significant improvement over the decorrelator receiver. Using our scheme, the degradation factor of a synchronous CDMA system is reduced by more than 6 dB compared to the decorrelator receiver. The proposed algorithm is only slightly more complex than the decorrelator 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".