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 (SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> ). The call is accepted if the SINR is greater than SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> , otherwise it is rejected. The choice of the SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> value is restricted by two opposing factors: the signal quality and the network utilization. Setting SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> at high value is desirable to increase the signal quality. However, a high SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> value increases the blocking probability (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) and reduces the network utilization. An upper bound of SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> (SINR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th_ub</sub> ) 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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".