LiFi through Reconfigurable Intelligent Surfaces: A New Frontier for 6G?
Bibliographic record
Abstract
Light fidelity (LiFi), which is based on visible-light communications (VLC), is celebrated as a cutting-edge technological paradigm that is envisioned to be an indispensable part of 6G systems. Nonetheless, LiFi performance is subject to efficiently overcoming line-of-sight (LoS) blockage, whose adverse effect on the reliability of wireless reception becomes even more pronounced in highly dynamic environments, such as vehicular applications. Meanwhile, reconfigurable intelligent surfaces (RISs) have recently emerged as a revolutionary concept that transforms the physical propagation environment into a fully controllable and customizable space using a low-cost, low-power approach. We anticipate that the integration of RISs into LiFi-enabled networks will not only support blockage mitigation but will also provision complex interactions among network entities, and is hence manifested as a promising platform that enables a plethora of technological trends and new applications. In this article, for the first time in open literature, we set the scene for a holistic overview of RIS-assisted LiFi systems. Specifically, we explore the underlying RIS architecture from the perspective of physics and present a forward-looking vision that outlines potential operational elements supported by RIS-enabled transceivers and environments. Finally, we highlight major associated challenges and offer a look ahead toward promising future directions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".