Resource Allocation of Hybrid VLC/RF Systems With Light Energy Harvesting
Bibliographic record
Abstract
In this paper, we study the problem of maximizing the sum throughput of${K}$users in a hybrid heterogeneous visible light communication (VLC)/radio frequency (RF) wireless communication system. This is done by optimizing the transmission time intervals of a time division multiple access (TDMA) scheme allocated to the users in two different downlink (DL) scenarios. In both scenarios, using the harvested energy, each user transmits its signal in an optimal allocated time interval over the uplink (UL) channel. In the first scenario, a lightwave powered communication network (LPCN)-based VLC system is used to radiate only optical energy to be harvested by the users over DL channel. Specifically, UL sum-rate maximization problem is formulated by optimizing UL time allocations. In the second scenario, a time switching-based simultaneous lightwave information and power transfer (TS-based SLIPT) is considered where the light-emitting diode (LED) transmitter sends both information and power simultaneously over DL channel. Specifically, we propose a multi-objective optimization problem (MOOP) to study the trade-off between the UL and DL sum-rates. The non-convex MOOP framework is then transformed into an equivalent form, which yields a set of Pareto optimal resource allocation policies. The effectiveness of the proposed approaches is illustrated through numerical results.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".