Free-Space Optical Gateway Placement in Hybrid Wireless Mesh Networks
Bibliographic record
Abstract
The capacity of wireless mesh networks (WMN) must usually be upgraded as usage demands evolve over time. This is normally done by adding gateways which serve to increase the backhaul capacity of the network. In this paper we consider adding capacity in this manner using free-space optical (FSO) backhaul links. To accomplish this, we formulate a joint clustering and gateway placement problem which includes the strong rate-distance dependence of practical FSO links. The formulation incorporates the positions of existing wireline gateways and minimizes the number of additional hybrid-FSO/RF gateways which are needed to satisfy the target capacity requirements. After showing the complexity of the problem, a solution that is motivated by genetic algorithms is proposed. The performance of our algorithm is then compared to an optimal solution generated via an integer linear program (ILP) for small WMNs. The proposed algorithm is then modified to allow for balancing the traffic load that is carried by each gateway in the WMN. Many scenarios are considered which demonstrate the value of using FSO backhaul links to obtain post-deployment capacity upgrades in response to changes in user traffic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".