LINK-STATE AWARE DYNAMIC TRAFFIC SCHEDULING FOR PROVIDING PREDICTIVE QoS IN WIRELESS MOBILE MULTIMEDIA NETWORKS
Bibliographic record
Abstract
The performance of a centralized traffic priority based dynamic burst-level cell scheduling scheme is investigated in a correlated fading channel. The scheduling scheme is designed for the transmission of multiservice traffic over TDMA (Time Division Multiple Access)/TDD (Time Division Duplex) channels in a WATM (Wireless ATM) network. In this scheme, the number of slots allocated to a VC (Virtual Circuit) during a frame-time is changed dynamically depending on the traffic type, system traffic load, TOE (Time of Expiry) value of the data burst and data burst length. While allocating bandwidth, the channel error status is also taken into consideration. SR-ARQ (Selective Repeat - Automatic Repeat Request)-based link-level error control is assumed for all the traffic types in a multiservice traffic environment. The performance of the proposed scheme under correlated Rayleigh fading is evaluated using computer simulation for realistic voice, video and data traffic models and the QoS (Quality of Service) requirements of different traffic classes in a wireless mobile network. Simulation results show that the proposed scheduling framework can provide reasonably high channel utilization with predictive QoS guarantee in a multiservice traffic environment. The channel utilization and the perceived QoS for different services is highly affected by the traffic burstiness of the corresponding traffic type. Such a scheme can result in an energy efficient TDMA/TDD medium access control protocol for broadband wireless access.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".