Multistage Parallel Interference Cancellation Based on Chase Algorithm
Bibliographic record
Abstract
A key issue in a multistage parallel interference cancellation (PIC) scheme is to improve the hard decision accuracy after matched filters (MFs). For achieving this purpose, most PIC schemes focus on choosing the suitable weight for each user in each stage but keeping the hard decision values after MFs intact. This paper presents a new PIC scheme that uses Chase algorithm to edit the possible hard decision combinations and selects the one minimizing a cost function as the final hard decision result for performing the interference cancellation. Performance and complexity comparisons are presented among Chase-PIC, normalized least-mean-square adaptive multistage PIC (NLMS-PIC) schemes and their variants. A new PIC scheme using NLMS as the earlier stages and Chase in the later stages is proposed, which can achieve a better performance than multistage NLMS-PIC but with lower complexity
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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 | 0.001 | 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".