Downlink scheduling in visible light communications
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".