Improving the Accuracy of SINR Threshold Lower Bound for SINR-based Call Admission Control in CDMA Networks
Bibliographic record
Abstract
Call admission control (CAC) is used in CDMA networks to guarantee the signal quality in terms of the signal-to-interference-and-noise ratio (SINR). SINR is employed as the criterion for user admission by comparing the SINR with a threshold value (SINRth). A small threshold level is desirable to reduce the blocking rate. However, a lower bound of SINRthis essential to keep the outage probability (Pout) below a maximum value. A lower bound of SINRth(SINRth-lb) has been derived in M.H. Ahmed and H. Yanikomeroglu, (2005) by finding the relationship between Poutand SINRth. Then, SINRth-lbis determined as the lowest SINRththat keeps Poutbelow a certain Pout_max. In this paper, we improve the accuracy of SINRth-lbby modeling the admitted traffic using a Markov model (instead of the inaccurate Poisson model used in M.H. Ahmed and H. Yanikomeroglu, (2005)). In addition, we improve the accuracy by choosing the transition value of the step function that approximates the conditional outage probability distribution using the minimum mean square error criterion
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".