A New QoS Provisioning Method for Adaptive Multimedia in Wireless Networks
Bibliographic record
Abstract
Future wireless networks are designed to support adaptive multimedia by controlling individual ongoing flows to increase or decrease their bandwidths 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 average reward reinforcement learning in conjunction with stochastic approximation, which can maximize the network revenue subject to several predetermined QoS constraints. Unlike other model-based algorithms (e.g., linear programming), 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. In addition, when we consider the status of neighboring cells, the proposed scheme can dynamically adapt to changes in traffic condition. Moreover, the algorithm can control the bandwidth adaptation frequency effectively by accounting for the cost of bandwidth switching in the model. The effectiveness of the proposed approach is demonstrated using simulation results in adaptive multimedia wireless 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".