A framework for efficient bandwidth management in broadband wireless access systems
Bibliographic record
Abstract
Broadband Wireless Access Systems (BWASs) such as High Speed Downlink Packet Access (HSDPA) and the Worldwide Interoperability for Microwave Access (WiMAX), pose a myriad of new opportunities for leveraging the support of a wide range of “content-rich” mobile multimedia services with diverse Quality of Service (QoS) requirements. This is due to the remarkably high bandwidth that is supported by these systems, which was previously only available to wireline connections. Despite the support for such high bandwidth, satisfying the diverse QoS of users while maximizing the revenues of network operators is still one of the major issues in these systems. Bandwidth management, therefore, will play a decisive role in the success of such wireless access systems. Without efficient bandwidth management, network operators may not be able to meet the growing demand of users for multimedia services, and may consequently suffer great revenue loss. Bandwidth management in BWASs is, however, a challenging problem due to many issues that need to be taken into consideration. Examples of such issues include the diverse QoS requirements of the services that BWASs support, the varying channel quality conditions of mobile users, and hence the varying amount of resources that are needed to guarantee certain QoS levels during the lifetime of user connections, the utilization of shared channels for data delivery instead of dedicated ones and network congestion. In this thesis, we address the problem of bandwidth management in BWASs and propose efficient economic-based solutions in order to deal with these issues at different bandwidth management levels, and hence enhance the QoS support in these systems. Specifically, we propose a bandwidth management framework for BWASs. The framework is designed to support multiple classes of traffic with different users having different QoS requirements, maximize the throughput of BWASs, support inter- and intra-class fairness, prevent network congestion and maximize the network operator's revenues. The framework consists of three related components, namely packet scheduling, bandwidth provisioning and Call Admission Control-based dynamic pricing. By efficiently managing the wireless bandwidth prior to users' admission (i.e., pre-admission bandwidth management) and during the users' connections (i.e., post-admission bandwidth management), these schemes are shown to achieve the design goals of our framework. Keywords: BWASs, bandwidth management, packet scheduling, bandwidth provisioning, Call Admission Control, dynamic pricing, revenues and fairness.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".