Link-Level Traffic Scheduling for Providing Predictive QoS in Wireless Multimedia Networks
Bibliographic record
Abstract
A set of centralized burst-level cell scheduling schemes, namely, First Come First Served with Frame Reservation (FCFS-FR), FCFR-FR+, Earliest Deadline First with Frame Reservation (EDF-FR), EDF-FR+, and Multitraffic Dynamic Reservation (MTDR), are investigated for transmission of multiservice traffic over time division multiple access (TDMA)/time division duplex (TDD) channels in wireless ATM (WATM) networks. In these schemes, the number of time slots allocated to a virtual circuit (VC) during a frame-time is changed dynamically depending on the traffic type, system traffic load, the time of arrival (TOA)/time of expiry (TOE) value of the data burst and data burst length. The performances of these schemes are evaluated by computer simulation for realistic voice, video and data traffic models and their quality-of-service (QoS) requirements in a wireless mobile multimedia network. Both the error-free and the correlated fading channel conditions are considered. Simulation results show that the EDF-FR+ and MTDR schemes outperform the other schemes and can provide high channel utilization with predictive QoS guarantee in a multiservice traffic environment even in the presence of bursty channel errors. The EDF-FR+ scheme is found to provide better cell multiplexing performance than the MTDR scheme, Such a scheme would be easy to implement and would also result in a power conservative TDMA/TDD medium access control (MAC) protocol for broadband wireless access. Burst-level cell scheduling schemes such as EDF-FR+ can be easily adapted as MAC protocols in the emerging differentiated services (DS) enhanced wireless Internet protocol (IP) networks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".