Maximum Network Lifetime in Interference-Aware WiMax/802.16 Mesh Centralized Scheduling
Bibliographic record
Abstract
WiMax/802.16 mesh network is an emerging infrastructure that offers a cost-effective deployment for high-speed wireless broadband access to the back haul network. In order to provide the best cost-effective deployment solution, nodes must be powered by batteries which do not require electrical cables and voltage transformers deployment (city power lines voltage is not suited to WiMax/802.16 nodes voltage). Moreover this deployment approach (the only one compatible with a mobile nodes topology) is environment constraint free since not all cities have easy-reachable power lines (e.g. rural cities). However it requires a predictive mechanism to calculate the lifetime of the network, in purpose to provide the adequate maintenance (recharge/change nodes batteries, reconfigure the nodes) at the appropriate time. We study the problem of maximizing the network lifetime (MNL) in order to minimize the maintenance of WiMax/802.16 mesh centralized scheduling networks powered by batteries which causes them to go off line. Moreover our study incorporates an explicit novel modeling of interference and hidden terminal nodes using an appropriate time slot allocation. Results show that power aware routing and a convenient frame size improve the network lifetime.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".