A Model-Based Downlink Resource Allocation Framework for IEEE 802.16e Mobile WiMAX Systems
Bibliographic record
Abstract
In this paper, we propose a novel model-based resource allocation framework to provide quality-of-service (QoS) support in the downlink (DL) of an IEEE 802.16e mobile WiMAX system. First, we develop a queueing model that links important performance measures of DL service flows to a set of tunable parameters. Based on the queueing model, we show how these parameters could be set to appropriate values to meet the QoS performances sought by admitted service flows. We then introduce a resource allocation scheme that uses these parameter values in packet scheduling decisions. In this queue- and channel-aware scheme, the queue-length-based packet scheduler is complemented by a cross-layer orthogonal frequency-division multiple access (OFDMA) slot allocation mechanism that adapts to channel conditions at the destination mobile stations (MSs). Compared with existing schemes, the proposed scheme is compatible with the updated definition of some key resource allocation concepts in IEEE 802.16e and offers a simple yet more effective way to provide QoS to a heterogeneous mix of applications. Its cross-layer aspect ensures efficient resource utilization in the presence of link adaptations due to mobility and channel fading. It also offers greater flexibility to service providers by allowing probabilistic delay guarantees to delay-sensitive multimedia applications. Simulation results show the performance benefits of the proposed scheme in providing QoS support for both real-time and non-real-time applications in mobile WiMAX systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".