Noncoherent Receivers for Multichip Differentially Encoded DS-CDMA
Bibliographic record
Abstract
We design and analyze novel noncoherent receivers for direct-sequence code-division multiple access (DS-CDMA) with multi-chip (MC) differential encoding (DE). The proposed receivers for MC-DE are based on multiple-symbol detection and decision-feedback differential detection, which have been previously applied for symbol-level DE. While the complexity of the proposed receivers is moderate, it is shown that they enable large performance gains over conventional differential detection for various channel environments, such as additive white Gaussian noise (AGWN) channels, Rayleigh and Ricean fading channels, and channels with frequency offset and phase noise. Furthermore, we show that in most cases MC-DE outperforms single-chip differential encoding and, in addition, decreases receiver complexity. Another result of this paper is that quaternary differential phase-shift keying (QDPSK) is more suitable for chip-level DE than binary DPSK.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".