A Cross-Layer Optimization Framework for Multicast in Multi-hop Wireless Networks
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Bibliographic record
Abstract
Achieving optimal transmission throughput in data networks is known as a fundamental but hard problem. The situation is exacerbated in multi-hop wireless networks due to the interference among local wireless transmissions. In this paper, we propose a general modeling and solution framework for the throughput optimization problem in wireless networks. In our framework, data routing, wireless medium contention and network coding are jointly considered to achieve the optimal network performance. The primal-dual solution method in the framework represents a cross-layer optimization approach. It decomposes the original problem into data routing sub-problems at the network layer, and power allocation sub-problems at the physical layer. Various effective solutions are discussed for each sub-problem, verifying that our framework may handle the throughput optimization problem in an efficient and distributed fashion for a broad range of wireless network scenarios.
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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 | 0.000 |
| Open science | 0.001 | 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