Opportunistic performance enhancement of reservation multiple access protocols of wireless broadband networks
Bibliographic record
Abstract
Most of today's broadband networks, including the recently ratified IEEE 802.16 standard, employ reservation based multiple media access control. A problem pertinent to reservation MAC protocols is the division of frame slots between the contention and data transmission processes. In most of the reservation MAC protocols no specific ratio is standardized, leaving proprietary solutions address the local network environment. As both processes are equally important for maintaining efficient delay and throughput performance, a solution must consider the timely varying traffic load. For example, heterogeneity and cooperation of networks promote access technologies that can sustain waves of increasing traffic load. In this paper, we start by instituting a framework for efficient allocation of frame resources to the contention and data transmission processes in light of the delay and throughout performance. We then propose a dynamic resource allocation controller based on a Markovian optimization model, where the optimization parameters are tuned according to specific preferential criteria of service providers. Our model achieves opportunistic performance improvements, on a per frame basis, over the best-case static allocation. Through simulation, we study the merits of our proposed optimized controller with respect to the framework. We show by illustrative examples and numerical results that the controller successfully fulfills the framework objectives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".