Sensor Networks for Estimating and Updating the Performance of Cellular Systems
Bibliographic record
Abstract
We investigate the use of an auxiliary network of sensors to assist radio resource management in a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if the mobile receiver there has inadequate signal-to-noise ratio (SNR-based outage) or, using another criterion, inadequate signal-to-interference ratio (SIR-based outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to confine the number of sensors per cell to an acceptable level while accurately estimating the outage probability. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is the performance prediction accuracy of the sensor network, rather than cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of ~ Po can be accurately estimated using ~ 10/Po power-measuring sensors distributed in a random uniform way over base-mobile distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based cases, in both indoor and outdoor environments.
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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 | 0.000 | 0.000 |
| 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".