Bibliographic record
Abstract
In classical channel assignment (CA) in Multi-Radio Multi-Channel (MRMC) Wireless Mesh Networks (WMNs), the number of available frequency channels is assumed to be fixed.Two links that are within the interference range of each other could be assigned the same frequency channel, causing co-channel interference that degrades the network throughput.The objective of this research is to develop a realistic CA method that finds the smallest number of frequency channels required for interferencefree communication among the mesh nodes (MNs) in order to achieve the maximum network throughput while maintaining fairness among the multiple network flows in a dynamic MRMC WMN.As a first step towards achieving this objective, a novel CA method is developed, which ensures interference-free communication among the MNs based on the protocol interference model, and determines a small number of frequency channels required to achieve the maximum network throughput while maintaining fairness among the multiple network flows.Secondly, in order to develop a CA method using a realistic interference model, a novel and computationally simple method of building the conflict graph based on signal-to-interference ratio model with shadowing is developed.Computationally simple and effective new heuristics are developed to find channel assignments from the conflict graph for the extended coloring problem with cumulative interference constraints.The heuristics are orders of magnitude faster than the exact solution method while consistently returning near-optimum results.As a final step, the problem of co-channel interference in a dynamic WMN environment is addressed by using beamforming.The novel Linear Array Beamformingbased Channel Assignment (LAB-CA) method reduces the number of frequency channels required (NCR) and significantly outperforms the classical omni-directional antenna pattern-based channel assignment (OAP-CA) method in terms of NCR.The beamforming-based CA framework is extended to incorporate heterogeneous MNs (i.e.nodes having differing numbers of radio interfaces).The LAB-CA method for heterogeneous MNs outperforms OAP-CA for heterogeneous MNs in terms of NCR in both sparse and dense mesh networks.It also provides a further significant reduction in NCR when the number of antennas in the linear antenna arrays of MNs is increased.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| grok | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| opus | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".