Dynamic QoS-Based Bandwidth Allocation Framework for Broadband Wireless Networks
Bibliographic record
Abstract
Broadband wireless communication systems, namely, Worldwide Interoperability for Microwave Access (WiMAX) and Long-Term Evolution (LTE), promise to revolutionize the mobile users wireless experience by offering many of the services and features promised by fourth-generation (4G) wireless systems, such as supporting multimedia services with high data rates and wide coverage area, as well as all-Internet Protocol (IP) with security and quality-of-service (QoS) support. These systems, however, require proficient radio resource management (RRM) schemes to provide the aforementioned features they promise. In this paper, we propose a new framework, which is called dynamic QoS-based bandwidth allocation (DQBA), to support heterogeneous traffic with different QoS requirements in WiMAX networks. The DQBA framework operates as such; it dynamically changes the bandwidth allocation (BA) for ongoing and new arrival connections based on traffic characteristics and service demand. The DQBA aims at maximizing the system capacity by efficiently utilizing its resources and by being fair, practical, and in compliance with the IEEE 802.16 standard specifications. To achieve its objectives, DQBA employs a flexible architecture that combines the following related components: 1) a two-level packet scheduler scheme; 2) an efficient call admission control policy; and 3) a dynamic BA mechanism. Simulation results and comparisons with existing schemes show the effectiveness and strengths of the DQBA framework in delivering promising QoS and being fair to all classes of services in a WiMAX network.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".