Traffic prediction based access control using different video traffic models in 3G CDMA high speed data networks
Bibliographic record
Abstract
The evolution of 3G Code Division Multiple Access (CDMA) network towards higher data rates is through the introduction of High Speed Downlink Packet Access (HSDPA) enhancement to the existing 3rd Generation Partnership Project (3GPP) standards. In this paper, an access control protocol is proposed for an integrated voice, video and non real-time data traffic on the forward link (cell-site to mobile). The protocol involves predicting the residual capacity available for the HSDPA traffic. This paper evaluates the performance of three video traffic models in predicting the number of data packets that could be scheduled at the next time slot. All three video traffic models exploit the frame properties of Motion Picture Experts Group (MPEG) traffic. The traffic models are based on Markovian, Autogressive (AR) and two-sided Markov Renewal Model (TSMR) processes. The performances of the proposed estimation schemes are compared with estimation scheme using static guard margin. Findings of this paper can be used to improve the downlink performance of non-real time data traffic in the presence of MPEG video traffic in 3G CDMA networks.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".