Optimized bandwidth allocation in broadband wireless access networks
Bibliographic record
Abstract
Abstract Towards satisfying the requirements of International Mobile Telecommunications–Advanced, both the Institute of Electrical and Electronics Engineers (IEEE) and Third Generation Partnership Project (3GPP) introduced revolutionary wireless technologies, exploiting advanced technologies and architectures. Both IEEE's 802.16 (Worldwide Interoperability for Microwave Access (WiMAX)) and 3GPP's Long Term Evolution have been introduced to accommodate the increasing demand for mobile services and applications. To realize the true potential of these technologies, however, opportunistic frameworks for radio resource management must be designed to exploit the adaptive nature of mobile traffic. The utility optimized quality‐of‐service (QoS) framework proposed in this paper for the mobile WiMAX networks achieves this objective. To maintain support for QoS guarantees, the framework capitalizes on the adaptive nature of WiMAX traffic by individually linking connections with a utility function designed to both uphold the end users’ perceived performance and determine bandwidth allocations by a search tree maximization algorithm. In doing so, bandwidth utilization is maximized for all active connections, and blocking and dropping probabilities for new and handover calls, respectively, are minimized. The framework is evaluated through an extensive simulation model and is shown to outperform state‐of‐the‐art solutions. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".