Mobility-Aware Modeling and Analysis of Dense Cellular Networks with\n C-plane/U-plane Split Architecture
Bibliographic record
Abstract
The unrelenting increase in the population of mobile users and their traffic\ndemands drive cellular network operators to densify their network\ninfrastructure. Network densification shrinks the footprint of base stations\n(BSs) and reduces the number of users associated with each BS, leading to an\nimproved spatial frequency reuse and spectral efficiency, and thus, higher\nnetwork capacity. However, the densification gain come at the expense of higher\nhandover rates and network control overhead. Hence, users mobility can diminish\nor even nullifies the foreseen densification gain. In this context, splitting\nthe control plane (C-plane) and user plane (U-plane) is proposed as a potential\nsolution to harvest densification gain with reduced cost in terms of handover\nrate and network control overhead. In this article, we use stochastic geometry\nto develop a tractable mobility-aware model for a two-tier downlink cellular\nnetwork with ultra-dense small cells and C-plane/U-plane split architecture.\nThe developed model is then used to quantify the effect of mobility on the\nforeseen densification gain with and without C-plane/U-plane split. To this\nend, we shed light on the handover problem in dense cellular environments, show\nscenarios where the network fails to support certain mobility profiles, and\nobtain network design insights.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".