Stochastic Geometry Analysis of User Mobility in RF/VLC Hybrid Networks
Bibliographic record
Abstract
The integration of visible light communication (VLC) with existing radio frequency (RF) networks has emerged as a new network architecture to meet the rapidly growing traffic demand. The resulting RF/VLC hybrid network structures offer capacity-per-area improvements due to the use of two technologies operating at different frequency bands and the relatively higher base station (BS) density. However, the reduced BS coverage footprints and the heterogeneous BS types result in challenges for user mobility such as frequent handovers and the need for suitable BS association policies. To help addressing these challenges, in this paper, we conduct a user mobility analysis for RF/VLC hybrid networks by deriving the user-to-BS association probabilities and handover rates. The analysis makes use of stochastic geometry and modeling BSs’ locations via a Poisson point process (PPP). Since PPP modeling has not yet been well established for hybrid RF/VLC networks, we support the applicability of our approach by comparing the user mobility performance to those obtained for an actual deployment, a Matérn hard-core point process (MHCPP) based deployment, and a deterministic square lattice deployment of VLC luminaries. Furthermore, since the handover rates directly depend on the association policies, we consider two popular association policies. Our numerical results show that the PPP, the MHCPP, the square lattice, and the actual deployments have comparable performance in terms of handover rates regardless of the association policy, and they highlight the tradeoff between balancing network load and handover rates achieved by the association policies.
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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 | 0.001 | 0.004 |
| 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".