Computing partial cancellation factors for PPIC receiver in large CDMA over a fading channel
Bibliographic record
Abstract
In this paper, we introduce a simple, closed-form expression for finding the optimum partial cancellation factors (PCF) for the linear multistage partial parallel interference cancellation (PPIC) receiver. These factors are found in a direct- sequence code division multiple access (DS-CDMA) system over a frequency-flat fading channel for a large-system case. In this case, the number of active users and the processing gain tend to infinity while their ratio is finite. It will be shown that the expression for the PCF is a function of the moments of the eigenvalues of the correlation matrix, the number of interference cancellation (IC) stages, the system load, and the signal-to-noise ratio (SNR). Compared to recently-proposed methods, our method has the following advantages: a) It is less complex because the calculated PCF will be a direct function of the moments of the eigenvalues of the large correlation matrix; b) there is no need for ordering the PCF resulting in lower complexity; and c) it is not necessary to know the number of IC stages ap riori .
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".