Optimal design of distributed concatenated Alamouti codes for relay networks using uniquely-factorable QAM constellations
Bibliographic record
Abstract
In this paper, a novel distributed concatenated Alamouti code is devised for a one-way relaying network consisting of two end nodes with each having a single antenna and one relay node equipped with two antennas. With the aid of the newly developed uniquely-factorable constellation pair (UFCP) generated from square quadrature amplitude modulation (QAM) constellation and by jointly processing the noisy signals received at the relay node, such a design allows the terminal nodes and the relay node to transmit their own information concurrently at the symbol level, and turns the equivalent channel between the two end nodes into a product of two Alam-outi channels, thus, called UFCP concatenated Alamouti space-time block code (STBC) while maintaining the equivalent noise is still white Gaussian, thereby, leading to a symbol-by-symbol decodable optimal maximum-likelihood (ML) receiver. In addition, an asymptotic symbol error probability (SEP) formula is derived with the ML detector, showing that the maximum diversity gain function is achieved, which is proportional to ln SNR/SNR2. Furthermore, an optimal power loading scheme minimizing the asymptotic SEP is proposed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".