Using the Bhattacharyya parameter for design and analysis of cooperative wireless systems
Bibliographic record
Abstract
A simplified method of analysis and design based on the Bhattacharyya parameter (BP) in conjunction with the union bound and weight enumeration is presented for relay channels using coded cooperation. This method is particularly suitable for low-complexity relay systems employing demodulate-and-forward, focusing on the problems of relay selection and outage analysis. These applications are chosen to illustrate the use of the BP in scenarios where analytical solutions are otherwise unattainable. In terms of relay selection, it is shown that BP-based relay selection has essentially the same performance as density evolution, though with much lower complexity. It is further shown that BP-based relay selection can be applied to fractional cooperation, where each relay only forwards a fraction of the source codeword. In terms of analysis, it is shown that weight enumeration with BP can be used to provide a close approximate to the upper bound on the outage probability of fractional cooperation, again with much lower computational complexity than density evolution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".