A predictive demand assignment multiple access protocol for Internet access over broadband satellite networks
Bibliographic record
Abstract
Abstract Medium access control (MAC) protocol is responsible for wireless resource management, which is crucial to satellite communication. To enhance the performance of broadband satellite networks, the application characteristics should be considered in the design of the MAC protocol. With the rapid expansion of Internet applications and the attractiveness of high speed Internet access via broadband satellite networks, it becomes an attractive objective to take into account the self‐similar nature of Internet traffic in MAC protocol designs. This paper presents a novel predictive demand assignment multiple access (PRDAMA) protocol for packet communications over broadband satellite networks. PRDAMA allocates free bandwidth resources by estimating the positive varying trend of the Internet traffic to facilitate prediction of the bandwidth requirements of the earth stations. Simulation results demonstrate that PRDAMA achieves a lower average delay and delay jitter compared with other DAMA protocols under highly bursty traffic due to its accurate traffic trend prediction. With less bursty traffic, PRDAMA still performs better than the other DAMA protocols under a heavy traffic load. Copyright © 2003 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".