The modified GTCF method and its application to multi-branch multihop relaying systems with full selection diversity
Bibliographic record
Abstract
A modification to the new transform method, the generalized transformed characteristic function (GTCF), is proposed to reduce its computational complexity. The GTCF and the modified GTCF (M-GTCF) use a new transform domain rather than the conventional moment generating function or characteristic function approaches. The M-GTCF gives exact solutions in single integral form for the probability density function (PDF) and the cumulative distribution function (CDF) of the end-to-end received signal-to-noise ratio (SNR) of multihop amplify-and-forward (AF) relaying systems. The M-GTCF method is applied to obtain exact, integral solutions for the average symbol error probability and the outage probability of multi-branch, multihop AF relaying systems with full selection diversity. The results are precise for any number of hops, N, any number of branches, L, and can accommodate any channel fading distribution. The GTCF method can provide exact solution for such systems. However, the M-GTCF method provides the first exact theoretical solution for such systems with complexity less than the GTCF method.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".