Energy Harvesting in Solar-Powered UAV Communication With Rate Splitting Multiple Access
Bibliographic record
Abstract
Future wireless networks are anticipated to evolve by aerial communication platforms. Nonetheless, the operational lifespan and efficacy of transceivers such as unmanned aerial vehicle (UAVs) and Internet of Things (IoT) devices are strictly prohibited by their constrained onboard power sources. This paper focuses on an aerial network configuration where a UAV harvests solar power to serve energy-limited IoT devices through simultaneous wireless information and power transfer. In this setup, the UAV and the IoT devices, each are equipped with energy and data buffers. This system also benefits from rate splitting multiple access for efficient interference management. Upon optimizing the system efficacy, we formulate a long-term resource allocation problem to maximize the time-averaged energy efficiency. To address this stochastic and non-convex optimization problem, we propose a multi-stage solution strategy. Firstly, by leveraging Lyapunov optimization theory, we transform the long-term stochastic problem into an equivalent deterministic short-term form. Next, by recasting this equivalent problem into Markov decision process, we propose a resource allocation mechanism based on actor-critic hindsight experience replay (AC-HER), tailored to capture the problem dynamics and optimize its variables. Moreover, given the UAV high mobility and the system reconfigurations, we fortify the trained AC-HER model with meta-learning strategy, enhancing its adaptability to system variations. Simulations verified that the proposed resource allocation strategy considerably outperforms its counterparts.
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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) | 0.000 | 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 | 0.000 | 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".