Diverse QoS Support in Multimedia Communication with Multiple MAC Layer Queues Using FSMC
Bibliographic record
Abstract
Diverse quality of service (QoS) guarantee is critical in wireless multimedia communications to fulfill the requirements of various applications. QoS with conventional single queue scenario has been very much explored but few studies were done on multiple queue system. In this paper, we propose a new multiple queue finite-state Markov chain model where multiple queues are employed at medium access control (MAC) layer and the system is modeled by combining the multiple queues with the finite-state Markov channel (FSMC) at physical (PHY) layer. We also introduce queue control parameters at MAC layer to determine the different priorities of different queues for the provision of diverse QoS, which can further be adjusted dynamically according to users' real-time requirements by configuring queue control parameters. The stationary distribution of the Markov chain is then obtained to derive the closed-form expression of the system QoS performance and finally we validate the proposed multiple queue algorithm by simulations.
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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 | 0.000 | 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".