Secondary Wireless Mesh Network Design Using Leased Frequency Spectra
Bibliographic record
Abstract
This paper considers the design of secondary wireless mesh networks which use leased frequency channels. In a given geographic region, the available channels are individually priced and leased exclusively through a primary spectrum owner. The usage of each channel is also subject to published interference constraints so that the primary user is not adversely affected. When the network is designed and deployed, the secondary user would like to minimize the costs of using the required resources while satisfying its own traffic and interference requirements. This problem is formulated as a mixed integer optimization which gives the optimum deployment cost as a function of the secondary node positioning, routing, and frequency allocations. Because of the problem's complexity, the optimum result can only be found for small problem sizes. To accommodate more practical deployments, two algorithms are proposed and their performance is compared to solutions obtained from the optimization. The first algorithm is a greedy flow-based scheme (GFB) which iterates over the individual node flows based on solving a much simpler optimization at each step. The second algorithm (ILS) uses an iterated local search whose initial solution is based on constrained shortest path routing. Our results show that the proposed algorithms perform well for a variety of network scenarios.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".