Energy Minimization in STAR-RIS Assisted UAV Enabled SWIPT Systems with FHB Protocol
Bibliographic record
Abstract
This paper investigates how to improve the energy efficiency of unmanned aerial vehicle (UAV)-enabled simultane-ous wireless information and power transfer (SWIPT) systems with multiple outdoor energy receivers (ERs) and multiple indoor wired-charging information receivers (IRs) by utilizing simul-taneously transmitting and reflecting reconfigurable intelligence (STAR-RIS), in which the UAV avoids flying over the indoor no-fly zone. Specifically, the total UAV energy consumption is minimized, while ensuring that the energy harvesting require-ment (EHR) of each ER and the communication throughput requirement (CTR) of each IR are met. To achieve this, the total UAV energy consumption is minimized by optimizing the STAR-RIS phase-shifts, the UAV trajectory, and hovering time using an iterative technique based on the fly-hover-broadcast (FHB) protocol. The technique allows the UAV to radiate energy-carrying information signals for the ERs and IRs at a limited number of hover positions. Simulation results demonstrate that the proposed design significantly outperforms other benchmark schemes, demonstrating its potential for improving the energy efficiency of UAV-enabled SWIPT systems while meeting the EHRs of each ER and the CTR of each IR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".