Wireless Power Transfer Aided with Reconfigurable Intelligent Surfaces: Design, and Coverage Analysis
Bibliographic record
Abstract
This paper investigates the wireless power coverage in a network where intelligent reconfigurable surfaces (RISs), distributed according to a homogeneous Poisson point process, cooperate in the energy transfer from the power beacon to the harvesting devices. First, using energy beamforming and maximum ratio transmission, the harvested energy at a typical device is obtained. Then, leveraging stochastic geometry tools, and adopting an approach based on the moment generating function, the power coverage probability is obtained. For such, we also characterize the distributions of the channel gains and the device distances. Novel closed-form expressions for the coverage probability are provided for the cases when the wireless power transfer is assisted by multiple RISs, deployed in cascaded or distributed configurations, as well as when the power beacon is aided by a single RIS. The impact of the main network parameters on performance is analyzed. In particular, comparative results show the significant gains that can be achieved in the network coverage when multiple RISs cooperate in the wireless power transfer, as compared to the non-cooperative scheme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".