Semiblind multiuser detection based on subspace tracking
Bibliographic record
Abstract
A novel adaptive semiblind multiuser detector based on subspace tracking is proposed for CDMA systems. Blind multiuser detectors consider only the in-cell users and neglect the inter-cell interference from out-of-cell users while semiblind multiuser detectors take into account both, intra-cell and inter-cell interference. Semiblind multiuser detectors are superior to blind multiuser detectors when the codes of some users are known to a base station e.g., codes of in-cell users vs. out-of-cell users. The proposed semiblind scheme does not require the knowledge of spreading codes of out-of-cell users. Compared to the existing blind and semiblind multiuser detectors, the proposed semiblind detector exhibits faster convergence, and is more robust in the case when the system is heavily loaded. If is also computationally less expensive.
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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 | 0.001 |
| 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 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".