An Experimental Multi-Antenna RF Wireless Power Transfer and Energy Harvesting System
Bibliographic record
Abstract
Wireless networks are experiencing an unprecedented surge in data traffic due to the widespread adoption of mobile devices and connected terminals. Effective energy management and efficiency are critical for ensuring sustainable green communication. Energy harvesting (EH) plays a vital role in achieving low carbon emissions and net-zero goals. However, many studies in the literature rely on idealized mathematical models without experimental validation, thereby overlooking potential discrepancies with real-world applications. This paper presents an experimental multi-antenna radio frequency (RF) wireless power transfer (WPT) and EH system. The system comprises a software-defined radio as a digital transmitter, a wireless receiver with an RF energy harvester, and a power splitter/combiner unit to enable multi-antenna functionality. Comprehensive measurements are conducted to investigate the power splitter/combiner unit, transmission and conversion efficiencies, line of sight and non-line of sight propagation scenarios, and linear and nonlinear energy harvesting models. The results are presented in terms of received power, harvested power, and charging time. The findings show that, although the power splitter/combiner unit introduces losses to the system, employing multiple antennas, particularly on the transmitter side, enhances system performance. Modulation schemes with constant envelopes prove more advantageous for EH, with frequency shift keying and phase shift keying achieving, on average, up to 30% more harvested power than quadrature amplitude modulation. Nonlinear three-piecewise and heuristic models are shown to be well-suited for the multi-antenna WPT-EH system.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".