Combined coding and quadratic detection for DS/CDMA systems operating in Rayleigh fading channels
Bibliographic record
Abstract
We present the true union bound of a DS/CDMA system that employs convolutional coding and a quadratic detector. In our study, the total bandwidth expansion factor caused by coding and spreading is fixed and the channel is assumed to exhibit Rayleigh flat fading. The objective is to assess the performance of various quadratic detectors as a function of the spreading factor and the fade rate. Specifically, four detectors were considered: chip level differential detection, chip level maximum likelihood sequence estimation, noncoherent detection, and perfect coherent detection. It was found that in the presence of fast Rayleigh fading, different detectors behave differently when the spreading factor varies. For example, the chip level differential detector and the ideal coherent detector favour a small spreading factor and a low rate code, while the MLSE prefers a high rate code and a high spreading factor. For the noncoherent detector, there actually exists an optimal system configuration at certain fade rates.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".