Average error rate evaluation of digital modulations in slow fading by Prony approximation
Bibliographic record
Abstract
A novel, remarkably simple semianalytical method for average error-rate evaluation over slowly fading channels is presented. Assume that an error-rate curve conditioned on the instantaneous signal-to-noise ratio at the detector input is known either analytically or can be estimated at a few points by a computer simulation. In the first step, a sum of the first-order exponentials is fitted to the conditional error-rate curve. The curve fitting by a sum of exponentials is well-known in many areas of data processing as Prony approximation. A universal numerical algorithm to find the parameters of Prony approximation is developed. In the second step, knowledge of the moment generating function of the signal-to-noise ratio is required to obtain an average error-rate. Hence, the proposed method can be shown to be an extension of the moment generating function method. The method is illustrated on an example of average bit-error rate evaluation for M-ary phase-shift keying over a generalized Ricean fading and over correlated multichannel Rayleigh fading with maximum ratio combining.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".