Evaluation of RF Energy Harvesting by Mobile D2D Nodes Within a Stochastic Field of Base Stations
Bibliographic record
Abstract
Radio frequency energy harvesting can prolong the battery life and improve energy efficiency of device-to-device (D2D) communication. In this paper, we analyze the performance of a mobile D2D device powered by EH from the transmissions of underlying cellular base stations (BSs), whose locations are modeled as a homogeneous Poisson point process. We model the movements of D2D nodes via a modified random waypoint model. Log-distance path loss and Rayleigh fading are considered, and EH takes place solely within harvesting zones surrounding each BS and each D2D user harvests energy for a fixed number of charging time slots before attempting to transmit. We derive the probability of a D2D device being within an EH region surrounding BSs after multiple movements, and the probability of being within the fully charged state using a Markov-chain approach taking into account temporal effects. Moreover, the statistics of the harvested energy are characterized, and subsequently, the outage probability of a D2D transmission utilizing the harvested energy is derived. We show that the number of movements required to be within a harvesting region increases significantly when the harvesting threshold power increases, and that the number of harvesting time slots should be selected judiciously.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".