A performance comparison of dynamic channel and ressource allocation protocols for mobile cellular networks
Bibliographic record
Abstract
Communication channels are the most important resources in mobile cellular networks. However, with the increasing needs of mobile users and the limited and scare bandwidth available to cellular networks, resource management is crucial to allocate these communication channels efficiently. Several channel allocation protocols based upon a mutual exclusion paradigm have been developed. However, very little data have been reported to compare these protocols. In this paper, we review four of the best known distributed dynamic channel and resource allocation algorithms. The first channel allocation scheme (known as a DDRA) adopts the co-group interference, while the other three algorithms (Cao-Singhal, Choy-Singh and Prakash-Shivaratri-Singhal) are based upon the co-channel interference. We present an extensive set of simulation experiments to compare these four schemes, and report on their performance evaluation. Our results indicate clearly DDRA algorithm has shown the shortest response time and highest blocking rate among all of the four channel allocation protocols. Cao-Singhal algorithm exhibits a better blocking rate when compared to the three other schemes. This is due to the fact that it re-uses communication channels optimally.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".