Measuring the spatial heterogeneity of outdoor users in wireless cellular networks based on open urban maps
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Bibliographic record
Abstract
Wireless cellular network planning benefits from accurate and realistic, yet relatively simple and manageable, spatial traffic models. User locations in cellular networks are often modeled as homogeneous (uniform) Poisson point processes (PPPs). However, the real user distributions are seldom purely homogeneous. Network users are usually concentrated at social attractors such as residential and office buildings, shopping malls, and bus stations. Wireless spectral efficiency depends significantly on the users' spatial heterogeneity, and thus relevant spatial traffic generators and models are important. In future (5G) networks, for which device-to-device (D2D), millimeter-wave (mmWave), and small-cell deployments in Heterogeneous Networks (HetNets), are promising technologies, it will become more important to have spatial traffic models which can represent the broad possibilities from completely homogeneous cases (e.g., a deterministic lattice) to extremely heterogeneous cases (e.g., highly clustered scenarios). In this paper, we study the spatial traffic heterogeneity of outdoor users in the denser areas of the city center of Paris, France. The building shape data is freely available from the OpenStreetMaps project. We measure the heterogeneity via a second-order statistic: the Coefficient of Variation (CoV) of two spatial metrics of the resulting point process: the Voronoi cell areas and the Delaunay cell edge lengths. The expected value of the CoV of these metrics allows us to study how the heterogeneity increases with the density of users. Moreover, we find that the statistical distribution of both these metrics is close to Weibull. Our results illustrate that the topology of the buildings in the city imposes a significant degree of heterogeneity on the spatial distribution of the wireless traffic.
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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 | 0.001 | 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 it