Accurate Upper Bound of SINR-based Call Admission Threshold in CDMA Systems with Imperfect Power Control
Bibliographic record
Abstract
Since the capacity of CDMA networks is interference-limited, it is vital to have a call admission control (CAC) mechanism to preserve the signal quality in terms of the signal-to-interference-and-noise ratio (SINR). SINR-based CAC schemes compare the SINR of the incoming call with a threshold value (SINRth). The call is accepted if the SINR is greater than SINRth, otherwise it is rejected. The choice of the SINRthvalue is restricted by two opposing factors: the signal quality and the network utilization. Setting SINRthat high value is desirable to increase the signal quality. However, a high SINRthvalue increases the blocking probability (Pb) and reduces the network utilization. An upper bound of SINRth(SINRth_ub) in CDMA systems with imperfect power control has been determined. However, the accuracy of that upper bound is questionable since the possibility of power control (PC) infeasibility has not been taken into consideration. Also, the analysis used the normal distribution to model SINR instead of the widely-accepted lognormal distribution. Moreover, the noise and the inter-cell interference were ignored. In this letter, we derive a more accurate upper bound by taking the possibility of PC infeasibility into consideration and by using the lognormal distribution of SINR for imperfect PC. In addition, our analysis takes the noise and the inter-cell interference 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 | 0.004 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 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".