Downlink scheduling in visible light communications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Visible Light Communication (VLC) using Light Emitting Diodes (LEDs) within the existing lighting infrastructure would reduce the implementation cost and may operate at higher throughput than RF or Infrared (IR) based wireless systems. One of the major concerns in VLC implementation is developing resource allocation schemes that maintain or increase channel throughput, ensure fairness and fast link recovery while reducing delay. To address this challenge, the characteristics of VLC channel is modeled in detail mathematically and the resource allocation problem is formulated for a centrally controlled indoor VLC system in this paper. We focus on a VLC system providing location based services and it is shown that the resource allocation problem can be solved by optimal scheduling, and the solution has to consider different transmission scenarios based on different transmitters and receivers' locations. Specifically, a scheduling algorithm using proportional fair principle is proposed and the simulation results demonstrate that the proposed algorithm outperform the maximum rate scheduling and round robin by balancing the user throughput and fairness among users. A prototype of the VLC system is currently under development to demonstrate the effectiveness of the proposed system.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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