A Chase Based Multistage Parallel Interference Cancellation Scheme for Asynchronous Fading Channels
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, one category of PIC schemes keeps the hard decision values after MFs intact and focuses on choosing the suitable weight for each user in current stage so that the hard decision accuracy is improved as much as possible in the next stage. Another category of PIC focuses on optimizing the hard decision values in current stage. Rather than directly using the hard decision values after MFs to do the interference cancellation, this approach reviews the possible hard decision combinations and selects the one minimizing a cost function as the final hard decision result to do the interference cancellation. In Liu Feng et al. (2006), a new PIC scheme using the normalized least-mean-square (NLMS) at the earlier stages and Chase in the later stages for synchronous Rayleigh fading channels is proposed. In this paper, the proposed technique is applied to asynchronous Rayleigh fading channels. Simulation shows that the proposed scheme can achieve a better performance than multistage NLMS-PIC but with complexity reduction.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".