The wireless transfer of power is the enabling technology for realizing a true internet-of-things. Broad sensor networks capable of monitoring environmental pollutants, health-related biological data, and building utility usage are just a small fraction of the myriad of applications which are part of an ever evolving ubiquitous lifestyle. Realizing these systems requires a means of powering their electronics sans batteries. Removing the batteries from the billions or trillions of these envisioned devices not only reduces their size and lowers their cost, but also avoids an ecological catastrophe.
Increasing the efficiency of microwave-to-DC power conversion in energy-harvesting circuits extends the range and reliability of passive sensor networks. Multi-frequency waveforms are one technique that assists in overcoming the energy-harvesting circuit diode voltage threshold which limit the energy-conversion efficiency at low RF input powers typically encountered by sensors at the fringe of their coverage area.
This thesis discusses a systematic optimization approach to the design of energy-conversion circuits along with multi-frequency waveform excitation. Using this methodology, a low-power 5.8 GHz rectenna showed an output power improvement of over 20 dB at -20 dBm input power using a 3-POW (power-optimized waveform) compared to continuous waveforms (CW). The resultant efficiency is the highest reported efficiency for low-power 5.8 GHz energy harvesters. Additionally, new theoretical models help to predict the maximum possible range of the next generation of passive electronics based upon trends in the semiconductor industry. These models predict improvements in diode turn-on power of over 20 dB using modern Schottky diodes. This improvement in turn-on power includes an improvement in output power of hundreds of dB when compared to CW.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/52295 |
| Date | 27 August 2014 |
| Creators | Valenta, Christopher Ryan |
| Contributors | Durgin, Gregory D. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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