Proportional Fair Scheduling in Hierarchical Modulation Aided Wireless Networks
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Bibliographic record
Abstract
Theoretically, superposition coding (SPC) can achieve the capacity of a degraded Gaussian broadcast channel. A practical implementation of SPC, hierarchical modulation (HM), has recently been adopted in industry. Using HM, how to explore the multi-user diversity gain in a time-varying wireless environment to maximize throughput and maintain fairness is an open issue. Using greedy opportunistic scheduling algorithms will lead to a severe starvation problem. In this paper, we study the proportional fair scheduling (PFS) problem in an HM aided wireless network, jointly considering the user selection and utility maximization problems. Shannon capacity based and practical HM based optimal scheduling problems are formulated. An optimal algorithm and a low complexity suboptimal algorithm are proposed to solve the practical scheduling problem combining the opportunistic PFS and HM. Simulation results demonstrate that the proposed algorithms can achieve 50% to 100% throughput gain compared to the single-user opportunistic PFS solution depending on the number of users and have better fairness performance than the existing single-user and HM-based solutions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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