Bibliographic record
Abstract
This paper is an extension of the previous work of Rahman and Sesay (see Proceedings of IEEE Canadian Conference on Electrical and Computer Engineering, Halifax, Canada, p.245-49, 2000) where they evaluated the performance of a non-coherent MT-CDMA system using differential binary phase shift keying (DBPSK) modulation. Here, we investigate the performance of the same system using diversity combining. The paper presents the theoretical analysis in terms of the average bit error rate (BER). The study is carried out for a slowly Rayleigh fading and frequency selective channel in an indoor environment using M-branch post-detection diversity combining. The influences of the diversity order and number of tones are studied here, for a given bandwidth (BW), bit rate and transmitter power. The analysis and supporting simulation work show that the post-detection diversity provides significant BER improvement over non-diversity reception for a non-coherent MT-CDMA system.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".