Superposition transmission of layered encoded sources over non-orthogonal amplify-forward relay networks
Bibliographic record
Abstract
The paper investigates the broadcast of n-layered source codes over a single-relay network using a half-duplex nonorthogonal amplify-forward (HD-NAF) relaying protocol. Taking the distortion exponent, (i.e., the SNR exponent of the average end-to-end distortion) as the performance metric, we consider system operation in the high SNR regime. We first prove that the HD-NAF relay network with an n-layer code is subject to the successively refinable Diversity Multiplexing Tradeoff (DMT) curve, which is exercised to derive a closed-form expression for an achievable upper bound of the system distortion exponent. Rate allocation optimization is conducted to analyze and gain insight into system behavior. Numerical evaluations are performed based on derived analytical formulations, and the performance advantage of single-relay HD-NAF networks is justified in terms of the distortion exponent versus its conventional counterparts. Furthermore, it is observed that increases in the number of encoded layers increases system performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".