Review of LiFi Technology and Its Future Applications
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In order to respond to the increasing demand of capacity and bandwidth caused by the high number of wireless applications and users, LiFi technology was introduced. It uses the visible light spectrum instead of the radio spectrum to transmit data wirelessly through the illumination of LED lamps. The main advantage of this technology is to provide wireless communications with high data rates. Other advantages include efficiency, availability, security and safety. Also, this technology uses free unlicensed spectrum, and it is cost-effective. Additionally, unlike RF systems, no multipath fading and the transmitter and receiver circuits are not complex. However, LiFi has several issues, which include high path loss, sensitivity to blockages and Non-line-of-sight (NLOS) situations. Probably, the biggest issue of LiFi is the uplink communication which is difficult to implement due to practical and cost reasons. Several future applications of this technology include places where RF is restricted such as hospitals and airplanes. Also, it can be used for traffic management, underwater communication, and outdoor access to the Internet. Moreover, it can be combined with WiFi technology either in hybrid technique or aggregated technique. It is found that later technique gives better results. Another possible application is the optical attocells. It is found that the hexagonal cells model is the best for deterministic deployments of optical APs, whereas the hard-core point process (HCPP) model is the best for random deployments. Furthermore, LiFi can be used for multiuser access with high data rate by using non-orthogonal multiple access (NOMA) technique. Due to the great features of LiFi, more applications and everyday life devices will adopt this technology in the future. However, Because of its limitations, it may not totally replace RF technology, but they will work collaboratively to achieve a better performance.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it