Minimum Interference Channel Assignment for Multicast in Multi-Radio Wireless Mesh Networks
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Bibliographic record
Abstract
Multi-radio, multi-channel wireless mesh networking is an emerging wireless technology which enables the use of multiple radios in each wireless mesh router. Each radio is assigned to a particular channel based on a channel assignment algorithm in order to solve some objective function, e.g., maximizing network throughput or minimizing wireless interference. Multicast is a form of communication that delivers information from a source to a set of destinations simultaneously. In this paper, we propose a channel assignment (CA) algorithm for multicast using both orthogonal and partially overlapping channels. The algorithm enables the nodes in a multicast tree to operate with minimum interference. We evaluate the performance of the proposed CA using various multicast group sizes and numbers of available channels, and compare it with that of the multi-channel multicast (MCM) algorithm proposed by Zeng et al. (2007).
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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 | 0.001 | 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