Link layer solutions for supporting real‐time traffic over CDMA wireless mesh networks
Bibliographic record
Abstract
Abstract With recent advances in the development of wireless communication networks, wireless mesh networks (WMNs) have been receiving considerable research interests in recent years. The need to support integrated services and ensure quality of service (QoS) satisfaction for various applications is one of the fundamental challenges for successful WMN deployment. In order to provide differentiated services, medium access control (MAC) should have priority management at the link layer. In code division multiple access (CDMA)‐based WMNs, the interference phenomenon and simultaneous transmissions must be considered. We propose two priority schemes for MAC in a distributed CDMA‐based WMN, taking into account interference, multimedia services, QoS requirements, and simultaneous transmissions. The first priority scheme is within a node. Each node has an independent queue for each traffic class. According to QoS requirements, the queue that should be served first is determined. The second priority scheme is among neighbor nodes. It is proposed for multiple simultaneous transmissions in the CDMA network. This scheme gives a larger chance of correct transmission to high priority traffic than low priority traffic. In addition, we propose to use adaptive spreading gain and a frame structure to achieve high resource utilization. Simulation results demonstrate that the proposed schemes can achieve effective QoS guarantee. Copyright © 2009 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".