Toward Smart Factories: Modelling Hybrid RF and VLC Communication Channels for IIoT Applications
Bibliographic record
Abstract
LED lighting technology has gained wide popularity in recent years.Visible Light Communication (VLC) stands to become an excellent replacement medium for indoor high-speed Internet and Industrial Internet of Things (IIoT).This work aims to model a hybrid wireless communication system, where a visible light channel and a radio frequency (RF) channel work together to form a Multiple Input Multiple Output (MIMO) system.Combining optical and RF communication channels can boost data rate as optical devices have higher bandwidths.The analysis is conducted by examining the application of collaborative robots (Cobots).VLC links will direct downwards from ceilings (downlink) to deliver data as well as illumination.However, shining lamps upwards (uplink) will produce an uncomfortable glare.RF can not only provide the uplink, but it can also improve stability and speed if it operates together with VLC.Many principles, like frequency reuse plan and Shannon spectral efficiency, have been applied to enhance and improve our results.This study investigates the Bit Error Ratio (BER) in OOK modulation, multi-user access, received power distribution, and hybrid VLC/Wireless Fidelity (Wi-Fi) networks.Clear advantages can be seen, particularly in critical areas where traditional RF communications may pose security risks or suffer from interference.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".