Optimal Design of Broadband Wireless Mesh Networks
Bibliographic record
Abstract
Design/planning of WMNs is the key phase before any deployment. Few proposals can be found in the open literature that deal with the design problem; moreover, 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.), the requirements of providers (expected amount of traffic/users), the constraints of the physical environment (potential locations of wireless routers, e.g., poles, and gateways, e.g., data centers), etc. The objective is to minimize the cost of the network and its operations while satisfying the requirements. The proposed model is shown to outperform considerably existing solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".