Differentially coherent parallel interference cancellation for DS-CDMA
Bibliographic record
Abstract
In this paper, we propose a novel differentially coherent (DC) parallel interference cancellation (PIC) scheme for direct-sequence code-division multiple access (DS-CDMA) employing differential phase-shift keying (DPSK). The proposed scheme combines decision-feedback differential detection (DF-DD) and PIC. For optimization of the DF-DD and interference cancellation (IC) filters three different criteria are adopted. The first (DC-PIC I) and the second (DC-PIC II) criteria assume a constant channel and a channel with a small constant frequency offset, respectively, whereas the third criterion (DC-PIC III) optimizes the filters in the minimum mean-squared error (MMSE) sense taking into account the statistical properties of the underlying channel. Simulations show the high achievable performance and the robustness of the novel DC-PIC receiver. A comparison with linear DC receivers and a previously proposed DC-PIC scheme show the superiority of the proposed approach.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".