Cross-Layer optimal connection admission control for variable bit rate multimedia traffic in packet wireless CDMA networks
Why this work is in the frame
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Bibliographic record
Abstract
Next generation wireless code division multiple access (CDMA) networks are required to support packet multimedia traffic. This paper addresses the connection admission control problem for multiservice packet traffic modeled as Markov modulated Poisson process (MMPP) with the quality of service (QoS) requirements on both physical layer signal-to-interference ratio (SIR) and network layer blocking probability. Optimal linear-programming-based algorithms are presented that take into account of SIR outage probability constraints. By exploiting the MMPP traffic models and introducing a small SIR outage probability, the proposed algorithms can dramatically improve the network utilization. In addition, we propose two reduced complexity algorithms that require less computation and can have satisfactory approximation to the optimal solutions. Numerical examples illustrating the performance of the proposed schemes are presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it