Distortion Exponents for Multi-Relay Cooperative Networks with Limited Feedback
Bibliographic record
Abstract
In this paper, we consider the transmission of a Gaussian source in a multi-relay cooperative network, where limited channel-state feedback is combined with separate source and channel coding to help the transmission. We analyze the end-to-end distortion of the system at a high signal-to-noise ratio (SNR) in terms of the distortion exponent. The achievable distortion exponents of the limited-feedback-based scheme are optimized under various cooperation protocols, including the orthogonal amplify-and-forward (AF)/decode-and-forward (DF) protocols, the nonorthogonal AF/DF protocols, and the slotted AF protocol. Our analysis reveals the impact of the feedback resolution, the bandwidth ratio, the number of relays, and cooperation strategies on the optimized distortion exponent. It is shown that the feedback scheme outperforms the best known nonfeedback strategies for multiple-relay systems with only a few bits of feedback information.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".