Coverage and Rate Analysis for Co-Existing RF/VLC Downlink Cellular\n Networks
Bibliographic record
Abstract
This paper provides a stochastic geometry framework to perform the coverage\nand rate analysis of a typical user in co-existing VLC and RF networks covering\na large indoor area. The developed framework can be customized to capture the\nperformance of a typical user in various network configurations such as (i)\nRF-only, in which only small base-stations (SBSs) are available to provide the\ncoverage to a user, (ii) VLC-only, in which only optical BSs (OBSs) are\navailable to provide the coverage to a user, (iii) opportunistic RF/VLC, where\na user selects the network with maximum received signal power, and (iv) hybrid\nRF/VLC, where a user can simultaneously utilize the available resources from\nboth RF and VLC networks. The developed model for VLC network precisely\ncaptures the impact of the field-of-view (FOV) of the photo-detector (PD)\nreceiver on the number of interferers, distribution of the aggregate\ninterference, association probability, and the coverage of a typical user.\nClosed-form approximations are presented for special cases of practical\ninterest and for asymptotic scenarios such as when the intensity of SBSs\nbecomes very low. The derived expressions enable us to obtain closed-form\nsolutions for various network design parameters (such as intensity of OBSs and\nSBSs, transmit power, and/or FOV) such that the number of active users can be\ndistributed optimally among RF and VLC networks. Also, we optimize the network\nparameters in order to prioritize the association of users to VLC network.\nFinally, simulations are carried out to verify the derived analytical\nsolutions. Important trade-offs between height and intensity of OBSs are\nhighlighted to optimize the performance of a user in VLC networks.\n
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".