Subchannel and Power Allocation in Downlink VLC Under Different System Configurations
Bibliographic record
Abstract
Visible light communication (VLC) has attracted a significant amount of research interest due to its ability to support high data-rates. However, the issue of inter-cell interference (ICI) caused by resource sharing and the line-of-sight (LoS) blockage problem are significant challenges that need to be considered in the design and analysis of VLC systems. This paper investigates the resource allocation problem for the downlink of an orthogonal frequency-division multiple access-based multi-cell VLC system, while considering ICI and LoS blockage. This is carried out under various system configurations employing different transmission modes. Specifically, the joint problem of subchannel allocation (SA) and power allocation (PA) to maximize the sum-rate is formulated as a combinatorial and highly non-convex optimization problem due to the binary and continuous optimization variables. To obtain an efficient solution, the original problem is first separated into the SA problem and the PA problem. Two simple, yet efficient, procedures based on the quality of the channel conditions and matching theory are proposed for the SA problem, respectively. Then, the quadratic transform approach is exploited to develop an algorithm for the PA problem. Simulation results demonstrate the effectiveness of the proposed solutions in terms of their fast convergence and overall performance.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".