Intelligent simulation for the estimation of the uplink outage probabilities in CDMA networks
Bibliographic record
Abstract
In CDMA mobile networks, callers that are transmitting through a power station may cause interference at other power stations. When many users are already connected in the network, a new call may cause the signal to noise ratio to drop below a tolerance threshold. This phenomenon is called 'outage' and it provides an important measure of performance, useful in the design and control of the system. Evaluating this probability analytically has proven unsuccessful and only approximations exist today. Direct simulation of such networks is at present very slow because outage occurs infrequently - it may take hours to simulate directly a realistic model if a reasonable precision is desired. Thus this approach is not useful for design problems where one wishes to evaluate and compare performance of many different network models. In this work we implement a change of measure to estimate the outage probability using importance sampling. We present a functional estimator and a stochastic approximation method that are capable of learning the best parameters for the change of measure.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".