Exact BER analysis of distributed alamouti's code for cooperative diversity networks
Bibliographic record
Abstract
We analyze the bit-error rate (BER) performance of the distributed Alamouti's code for cooperative diversity networks consisting of a source, two relays and a destination node over Rayleigh fading channels. It is assumed that the relays adopt the amplify-and-forward protocol. Firstly, assuming the existence of the direct path component from the source to the destination, we derive the exact BER expression in a one-integral form for M-pulse amplitude modulation (PAM) and M-quadrature amplitude modulation (QAM) constellations. We also present a series expansion of a very accurate BER approximation, which does not require any numerical calculation, and we prove this series is convergent. Secondly, considering the system where the direct path component does not exist, we obtain the exact BER expression in a one-integral form and a series expansion of the exact BER expression. Numerical results confirm that the two exact BER expressions in a one-integral form perfectly match the simulation results and the two series expressions of BER are very accurate, even with a small truncation window.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".