Design of Infrastructure Wireless Mesh Networks: Formulations and Solutions
Bibliographic record
Abstract
The design/planning of WMNs is a key phase before any deployment. Few proposals can be found in the open literature that deals with the design problem; however, they do not take into account all the parameters that have an impact on the outcome of the design and they assume the existence of a physical topology where the location and the characteristics of nodes (e.g., number of channels, number of radios) are fixed.In this paper, we define a generalized model for the WMNs design problem that takes into account all the parameters that have a significant impact on the network (interference, multi-channel, transmission power, etc.), expected traffic, the constraints of the physical environment (potential locations of wireless routers and gateways), etc. To resolve the generalized model, we propose a combination of genetic and tabu search algorithms. The objective is to minimize the cost of the network and its operations while satisfying the requirements.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".