Distortion exponents for decode-and-forward multi-relay cooperative networks
Bibliographic record
Abstract
In this paper, we consider the transmission of a Gaussian signal in a multi-relay cooperative system, where each relay is half-duplex and employs the decode-and-forward relaying protocol. We focus on the analysis of the distortion exponent, which characterizes the high signal-to-noise ratio (SNR) behavior of the end-to-end distortion. Specifically, we investigate the layered source coding with progressive or broadcast transmission. Each transmission scheme is further combined with the repetition-based or relay-selection-based multi-relay cooperation protocol. We derive the distortion exponents of all four cases and illustrate the effect of the bandwidth expansion ratio, number of relays and cooperation protocols on the optimal distortion exponent. We also establish the successive refinability of the diversity-multiplexing tradeoff of the repetition-based and relay-selection-based cooperation protocols in multi-relay cooperative systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".