Opportunistic Multicast Scheduling with Erasure-Correction Coding over Wireless Channels
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an opportunistic multicast scheduling scheme using erasure-correction coding to jointly explore the multicast gain and multiuser diversity. The proposed scheme sends only one copy to all users in the multicast group at a transmission rate based on a SNR threshold selected using only the knowledge of the average SNR and fading type of the fading environment. Analytical framework is developed to establish the optimum selection of the SNR threshold and coding rate for given channel conditions in a Nakagami-m fading environment to achieve the best throughput. Numerical results show that the proposed scheme outperforms both the worst-user (WU) and best-user (BU) schemes for a wide range of average SNR and multicast group size. Without the needs of perfect knowledge of the instantaneous channel responses of the user links, the proposed scheme can significantly reduce the overhead required for channel information feedback and is suitable for a fast time-varying fading environment. Another advantage of the proposed scheme is that its achievable normalized throughput is independent of the multicast group size.
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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 it