Solution Space Characterization and a Fast Algorithm for the Channel Assignment Problem in Wireless Mesh Networks
Bibliographic record
Abstract
In this paper we analyze the characteristics of the solution space of the channel assignment problem in wireless mesh networks where routers are equipped with multiple radio interfaces and can use multiple channels. We show that the solution space is exponentially scaled with respect to the number of communication links, however, high quality solutions lie in dense regions within the solution space. These regions exhibit a high degree of similarity and redundancy. We also analyze the effects of the radio interface constraints on the structure and the size of the solution space. Based on our analysis, we develop a new scheme for channel assignment that dramatically reduces the size of the original solution space into that of a much smaller unconstrained weighted graph coloring problem. This goal is achieved by finding sets of link groups or bindings to represent a good solution structure that meets the radio interface constraints by construction. This structure is then employed to construct a weighted graph coloring problem that is equivalent to the original problem but with a much smaller solution space. Finally, the graph is colored by a heuristic based graph coloring algorithm that takes advantage of the space symmetry to further speed up the assignment process. Experimental results illustrate the superiority of the proposed scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".