A performance evaluation of distributed dynamic channel allocation protocols for mobile networks
Bibliographic record
Abstract
Abstract Technological advances coupled with the proliferation of wireless devices among mobile users require efficient resource management and reuse of the scare radio spectrum allocated to wireless and mobile communication systems. Several channel allocation protocols based on 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 based on the mutual exclusion paradigm. While the first three channel alloctaion protocols (Caoet al., Choyet al. and Prakashet al.) are based on the co‐channel interference, the fourth protocol, which is known as DDRA, adopts the co‐group interference approach. We present an extensive set of simulation experiments to evaluate and compare the performance of these four protocols using realistic scenarios. Our results indicate clearly that DDRA algorithm has shown the shortestresponse timeand highestblocking rateamong all of the four channel allocation protocols. Caoet al. algorithm exhibits a betterblocking ratewhen compared to the three other schemes. This is due to the fact that it reuses communication channels optimally. Last, but not least, we discuss the basic fault tolerant machanisms that can be used to enhance further these protocols while dealing with the base stations, mobile hosts, or links' failure. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 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".