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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".