Resource allocation in a K-user wireless broadcast system with N-layer superposition coding
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Bibliographic record
Abstract
In this paper, we study the resource allocation problem in a K-user wireless broadcast system with N-layer superposition coding (SPC). The problem is formulated as a sumutility maximization problem based on the average throughput. Using stochastic approximation, iteratively solving an approximated problem yields the optimality. The approximated problem can be solved by selecting the user group with the maximal weighted-sum-rate, which has a high computational complexity. Two low-complexity suboptimal algorithms are proposed. The simulation results show that the SPC gain highly depends on the variability of the channel and the SNR range of users. SPC is more favourable in the scenario with small-variation fast-fading channel and a large SNR range of users. The performance of the proposed low-complexity algorithms are close to the optimal solution, and the SPC gain achieved is substantial.
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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