A delay bounded approach for streaming services in CDMA cellular networks
Bibliographic record
Abstract
This paper considers the design of resource management schemes for cellular networks where mobile users are interested in receiving streaming media flows, and the cell utilizes a Discrete Sequence Code Division Multiple Access (DS-CDMA) air interface. Due to the relatively high data rate requirement of the streaming service, and the limiting effect of the multiple access interference (MAI) in CDMA networks, the cell may undergo overload conditions as the wireless channel path losses increase for any subset of users. In response, the base station is expected to delay, or perhaps forcibly terminate, some traffic streams.To achieve acceptable performance, we examine the use of a novel admission control scheme that takes user mobility into consideration. The utilized scheme admits a traffic stream only if the estimated cell overload probability after a prescribed prediction interval does not exceed a specified threshold value. As well, we devise a packet scheduling algorithm that aims at minimizing the number of forced terminations of connections that exceed specified delay bounds. Performance of the proposed admission control scheme in conjunction with the devised scheduling procedure is analyzed using simulation. The obtained results show significant improvement of using the proposed methods over methods that don't take user mobility into account.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".