Disjoint Multipath Routing and QoS Provisioning Under Physical Interference Constraints
Bibliographic record
Abstract
This paper addresses the problem of discovering least interfering paths in the context of multipath routing in multihop wireless networks using the SINR based interference model. Whereas traditionally interference has been quantified using the protocol model, we propose a disjoint multipath interference aware routing algorithm, known as DMPR:SINR, using a weighted SINR conflict graph to quantify interference. In addition, to ensure QoS, bandwidth and path flow restoration is implemented on the secondary routing path using an optimization formulation if a link failure occurs on the primary routing path. We compare our algorithms with disjoint multipath routing protocols in the literature that 1) considers interference using a version of the protocol model and 2) considers interference induced by the SINR model within a restricted range. We show that our algorithms outperform established disjoint multipath routing protocols in terms of normalized throughput, end-to-end delay and bandwidth usage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".