Hybrid Resource Allocation in Wireless Ad Hoc Networks
Bibliographic record
Abstract
Inefficient resource management may cause problems of reliability of service in shared medium wireless networks. Resources include bandwidth, processor cycles and buffers. Excessive competition for these resources can cause severe packet collision rates, compound network congestion, or even result in starvation of some nodes. In such a situation, data transmission is subject to very long delays and significant packet losses. Although transport layer protocols can help improve end-to-end performance, these approaches are slow in responding to network changes and incur additional overhead. This paper aims to reduce transmission delay and increase packet delivery ratio. A hybrid resource allocation problem is formulated by the Primal algorithm and a controller is derived to decrease congestion globally and to reduce collisions locally. Our simulation results show that this hybrid controller can achieve packet loss rates close to 1% and significantly shorten end-to-end delay even in a high interference environment with heavy system load. In addition, we also compare the performance of the hybrid controller with the impact of multipath routing. The accuracy of our simulation is improved by adding a probabilistic preamble detection model and SINR collision model based on frame error rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 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".