As the size and power consumption of microelectronic circuits continues to decrease, passively-powered sensors promise to come to the forefront of commercial electronics. One of the most promising technologies that could realize this goal is backscatter sensing. Backscatter sensors could harvest power from and modulate data onto an impinging carrier waveform. Currently radio frequency identification (RFID) technology passively powers itself and transmits statically stored data. However, this technology has two major weaknesses: lack of resiliency against narrowband interference and slow data rates. Both of these issues could be detrimental in sensing applications. This thesis will lay out a method for addressing both of these weaknesses through a unique application of spread spectrum encoding. Instead of spread spectrum being viewed as the multiplication of an already encoded data sequence with a periodic pseudorandom sequence, each sequence could be viewed in an aperiodic manner, where a single period of a pseudorandom sequence represents a data symbol. In this manner, backscatter sensors not only benefit from the increased resiliency that spread spectrum provides, but also can have higher data rates, since multiple bits can be encoded on a single symbol and multiple nodes can be read simultaneously, using spread spectrum multiple access techniques. In this thesis, 63-chip and 255-chip Kasami sequences, as well as 127-chip Gold sequences, will be analyzed for their use in various aperiodic direct sequence spread spectrum/multiple access system configurations (systems that have up to three nodes and use up to four different aperiodic sequences per node to represent different symbols). For each different configuration, near-"ideal" code configurations/rotations will be determined for use in the system.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/33946 |
| Date | 08 April 2010 |
| Creators | Graf, Patrick Anthony |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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