Block-error rate model for DPSK in Rayleigh- and sub-Rayleigh-fading channels
Bibliographic record
Abstract
Wireless transmission channels experience substantial signal degradation due to irregularities in the propagation path that impact synchronization between two communicating ends. Consequently, differential and noncoherent techniques that require no such synchronization have gained wide popularity. However, the transmitted data are still highly prone to transmission errors and appropriate error-control mechanisms have to be employed. Knowledge of the error statistics becomes crucial when designing these systems. A block-error rate (BLER) characteristic defines the error distribution within data blocks and in this paper, we propose a finite-state Markov chain-based method to evaluate BLERs in slow frequency-nonselective Rayleigh-fading channels for differential phase-shift keying. The methodology is then extended to the cases of faster Rayleigh and sub-Rayleigh fading. The model is verified by means of numerical simulation. The proposed model possesses many advantages of the well-developed Markov theory and may be easily extended to other modulation schemes such as noncoherent frequency shift keying.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".