An efficient distributed fault‐tolerant protocol for dynamic channel allocation
Bibliographic record
Abstract
Abstract Recent demand for mobile telephone services has been growing rapidly while the electromagnetic spectrum of frequencies allocated for this purpose remains limited. Any solution to the channel assignment problem is subject to this limitation, as well as the interference constraint between adjacent channels in the spectrum. The early research focused on the fixed channel allocation and centralized schemes. Recently, distributed channel allocation schemes have received much attention because of their high reliability and scalability. In these schemes, a base station (BS) has to consult with its neighboring BSs in order to assign a channel to a call. If it cannot communicate with its neighbors, it fails in allocating a channel. However, it is a common phenomenon that a BS fails in communicating with its neighboring BSs due to some reasons, such as heavy traffic load. In this paper, we propose a distributed fault‐tolerant channel allocation schemes which can work well under the mobile host (MH) failures, BS failures, and communication link failures. This algorithm is based upon the mutual exclusion model where the channels are grouped into three equal sized groups and each group of channels cannot be shared concurrently within the same cluster. We prove its correctness. We also report our algorithm's performance with several channel systems using different types of call arrival pattern through comparing with a popular generic distributed algorithm for channel allocation DDRA. Copyright © 2008 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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".