Handoff-Aware Channel Assignment for Multi-Radio Wireless Mesh Networks
Bibliographic record
Abstract
Channel assignment schemes in Multi-Radio Wireless Mesh Networks (MR-WMNs) usually leave several links sharing the same channel within overlapped transmissions or interference ranges; this is especially true when only one radio is used or when the number of radios is very small compared to the number of orthogonal channels. In order to further improve MR-WMN performance, especially for mobile multimedia users, we propose a new assignment scheme that takes into account the presence of non uniform handoff traffic besides the non uniform traffic load. In fact, following handoffs of a large number of mesh clients (MCs), several ongoing flows associated to these clients need to be re-routed along other paths through other mesh routers (MRs). Re-routing that involves MRs (e.g., second-hop MR) further first-hop MRs will result in a much higher service disruption (i.e., data losses), during handoff, than one that involves only first-hop MR due to much bigger re-routing latency. This is not acceptable for real-time multimedia flows. Therefore, in this paper, we propose a dynamic scheme that carefully re-assigns channels to interfaces with the purpose of limiting the re-routing overhead/latency during client handoffs. The proposed scheme chooses a channel re-assignment that achieves a better load balancing among MRs. Therefore, it increases the capacity of MR-WMNs by accepting more users while satisfying their QoS requirements. Simulation results show that our proposed approach achieves good performance in terms of delay, loss rate and overall throughput.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".