Channel assignment for multicast in multi‐channel multi‐radio wireless mesh networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract One of the most effective approaches to enhance the throughput capacity of wireless mesh networks (WMN) is to use systems with multiple channels and multiple radios per node. Multi‐channel multi‐radio (MCMR) networks require efficient channel assignment (CA) algorithms to determine which channel a link should use for data transmission in order to maximize network throughput. The problem of CA has been studied extensively for unicast communications, but addressed only recently for multicast. We propose a CA algorithm named Minimum interference Multi‐channel Multi‐radio Multicast (M4) that minimizes interference among nodes in a multicast routing tree and uses both orthogonal and overlapping channels such as those in IEEE 802.11b/g systems. Simulation results show that M4 outperforms the Multi Channel Multicast algorithm proposed. in various scenarios with respect to average packet delivery ratio, throughput and end‐to‐end delay. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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