Approximate BER Expressions of Distributed Alamouti's Code in Dissimilar Cooperative Networks with Blind Relays
Bibliographic record
Abstract
This paper focuses on error performance analysis of distributed Alamouti's code. Recently, many works have been devoted to the performance analysis of this code. In order to simplify the analysis, however, they either assumed the channels in the cooperative network had the same variances or only considered asymptotic error performance at high signal-to-noise ratio (SNR) range. In this paper, we study a general dissimilar cooperative network, where the channels have different variances. Two accurate approximate bit error rate (BER) expressions are proposed in order to evaluate the error performance of the distributed Alamouti's code. We also investigate how the values of channel variances affect the accuracies of the proposed approximate BER expressions. Our results demonstrate that the proposed approximate BER expressions are very close to the exact BER over the whole SNR range. Furthermore, we show that the average BER of the distributed Alamouti's code behaves like <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(ln(E)/E)<sup>2</sup></i> when the transmission power <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> is sufficiently large.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".