Multipath load balancing in multi-hop wireless networks
Bibliographic record
Abstract
Multi-hop wireless networks have the potential to dramatically reduce the cost of deploying communication infrastructure. However, the nature of this technology limits the capacity of radio links. Thus, it is important to utilize them as efficiently as possible. In this paper, we investigate load balancing across multiple paths as a possible mechanism to improve performance in multi-hop wireless networks. Given the inherent interference of multi-hop transmissions in a single radio channel, it is generally assumed that single-channel multipath routing cannot provide any benefits, but in fact would have detrimental effects on resource efficiency. However, a careful investigation of the issue reveals that under certain theoretic conditions, significant gains are possible. In fact, we show throughput improvements of 80-100% in some scenarios. We present a novel interference metric to assess the quality of a set of disjoint paths. We further present a heuristic path selection algorithm to find appropriate routing paths in structured networks, which is a first step towards the application of our basic results in realistic scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".