Design and performance evaluation of a QoS-based dynamic channel allocation protocol for wireless and mobile networks
Bibliographic record
Abstract
In recent years, we have witnessed a growing interest in the study of channel allocation and hand-off strategies for wireless networks to ensure continuous services that guarantee QoS to mobile users. To the best of our knowledge, most of the proposed channel allocation schemes do not take the QoS provisioning into account In this paper, we propose a distributed algorithm for dynamic channel allocation with an efficient adaptive channel reservation schema providing continuous QoS support. To acquire the low dropping rate, a proper number of channels in the congested cells is reserved for the handoff calls. This number of reserved channels is related to the wireless data traffic network. Our presented channel allocation protocol is based upon the mutual exclusion paradigm where all the channels are grouped into three groups and any cell in a cluster can not hold a channel group as long as another cell in the same cluster is holding the same group. We present our QoS-Based dynamic channel allocation protocol, and its performance evaluation, and discuss our experimental results we have obtained using 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 | 0.004 | 0.006 |
| 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".