Coverage and Rate Analysis for Co-Existing RF/VLC Downlink Cellular Networks
Bibliographic record
Abstract
This paper provides a stochastic geometry framework to perform the coverage and rate analysis of a typical user in co-existing visible light communication (VLC) and radio frequency (RF) networks. The framework can be customized to capture the performance of a typical user in various network configurations such as 1) RF-only, in which only small base-stations (SBSs) are available to provide the coverage to a user; 2) VLC-only, in which only optical BSs (OBSs) are available to provide the coverage to a user; 3) opportunistic RF/VLC, where a user selects the network with maximum received signal power; and 4) hybrid RF/VLC, where a user can simultaneously utilize the available resources from both RF and VLC networks. The developed model for VLC network precisely captures the impact of the field-of-view (FOV) of the photo-detector receiver on the number of optical interferers, distribution of the aggregate interference, association probability, the coverage probability, and average rate of a typical user. A closed-form approximation is presented for special cases and for asymptotic scenarios, such as when the intensity of SBSs becomes very low or the intensity of OBSs becomes very high. The closed-form solutions for network design parameters (such as intensity of OBSs and SBSs, transmit power, and/or FOV) enable network operators to distribute the users among RF and VLC networks according to their choice. Moreover, we also optimize the network parameters in order to prioritize the association of users to VLC network. Finally, simulations are carried out to verify the derived solutions. It is shown that the performance of VLC network depends significantly on the receiver's FOV/intensity of SBSs/OBSs and careful selection of such parameters is crucial to harness the benefits of VLC networks. Important trade-offs between height and intensity of OBSs are highlighted to optimize the performance of VLC networks.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".