Multistage interference cancellation with multipath decorrelating for QPSK asynchronous DS/CDMA system over multipath fading
Bibliographic record
Abstract
A combined multistage interference cancellation (MIC) and multipath decorrelating scheme (MIC-DECO) for asynchronous QPSK DS/CDMA over frequency-selective multipath Rayleigh fading channels is introduced. Unlike the conventional MIC, which attempts to cancel the multiple-access interference (MAI) and total self interference (SI) the introduced scheme aims to remove the MAI, and partial SI incurred by the self intersymbol interference (SII). After cancellation, decorrelating is used first to separate the multipath signals and then to re-combine the resulting fading replicas for symbol decision. If the noise correlation in the fading replicas is known, an optimum combining structure (MIC-OPTM) can be achieved. Furthermore, when the MAI and SII are successfully removed, the MIC-OPTM can be replaced by the MIC using RAKE combining (MIC-RAKE) with reduced complexity. The simulation results show that the MIC-DECO, MIC-OPTM, and MIC-RAKE in a multi-user environment provide a good performance close to the ideal performance in a single-user system, and outperform the conventional MIC even in the presence of channel estimation error.
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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) | 0.001 | 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) | 0.001 | 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".