A new QoS provisioning method for adaptive multimedia in cellular wireless networks
Why this work is in the frame
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Bibliographic record
Abstract
Third generation cellular wireless networks are designed to support adaptive multimedia by controlling individual ongoing flows to increase or decrease their bandwidth in response to changes in traffic load There is growing interest in quality of service (QoS) provisioning under this adaptive multimedia framework, in which a bandwidth adaptation algorithm needs to be used in conjunction with the call admission control algorithm. This paper presents a novel method for QoS provisioning via the use of the average reward reinforcement learning, which can maximize the network revenue subject to several predetermined QoS constraints. By considering handoff dropping probability, average allocated bandwidth and intraclass fairness simultaneously, our algorithm formulation guarantees that these QoS parameters are kept within predetermined constraints. Unlike other model-based algorithms, our scheme does not require explicit state transition probabilities and therefore the assumptions behind the underlying system model are more realistic than those in previous schemes. Moreover, by considering the status of neighboring cells, the proposed scheme can dynamically adapt to changes in traffic condition. Simulation results demonstrate the effectiveness of the proposed approach in adaptive multimedia cellular networks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it