Radio-frequency-based Wireless Energy Transfer in LTE-A heterogenous networks
Bibliographic record
Abstract
Wireless Energy Transfer (WET) promises charging wireless sensor networks, cell phones and on-body medical devices without the need of battery replacement nor plugging in to the mains. Magnetic induction and electromagnetic radiation are two alternative technologies for WET. Magnetic induction based WET is a mature technology while electromagnetic radiation based WET has been recently studied for WSNs or RFID tags in many studies. On the other hand, powering cell phones, PDAs or other User Equipment (UE) from ambient electromagnetic signals has unique challenges and is an emerging field of study. In this paper, we consider Radio Frequency WET (RF-WET) for prolonging UE lifetime in a Heterogeneous wireless network (HetNet). In a HetNet, coverage and capacity of the macro cell is augmented by small cells such as picocells, femtocells or Wi-Fi hotspots. In this paper, we assume small cell base stations and dedicated Energy Transmission Towers (ETTs) work together towards supplying power to the UEs. Power is supplied in the same frequency band with the communications in a time-sharing manner. We propose an ILP model where a mix of Picocell Base Stations (PBSs) and ETTs are placed such that the harvested energy is maximized while the number of ETTs and the number of actively power transmitting PBSs are minimized. We show that extended range of PBSs aid in increasing the amount of energy harvested by the UEs while as the number of serviced UEs increase the overall power harvesting capacity of the system improves.
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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 | 0.000 |
| 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) | 0.000 | 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".