Prioritized multi-class adaptive framework for multimedia wireless networks
Bibliographic record
Abstract
The next generation of wireless cellular networks (WCNs) is expected to support real-time multimedia applications with different classes of traffic and diverse bandwidth requirements. Bandwidth is a scarce resource in wireless networking that needs to be carefully allocated amidst competing connections with different quality of service (QoS) requirements. In this paper, we propose an adaptive framework for supporting multiple classes of multimedia services with different QoS requirements in WCNs. The framework combines the following components: (i) a threshold-based bandwidth allocation policy that gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls by assigning a threshold to each class, (ii) an efficient threshold-type call admission control (CAC) algorithm, and (iii) a bandwidth adaptation algorithm (BAA) that dynamically adjusts the bandwidth of an ongoing multimedia call to minimize the number of calls receiving lower bandwidth than the requested. Numerical results show that the performance of our adaptive multimedia framework outperforms that of existing non-adaptive schemes in terms of the handoff call dropping probability and effective utilization.
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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 | 0.002 | 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) | 0.003 | 0.001 |
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".