Cognitive radio (CR) technology has recently been introduced to opportunistically exploit the spectrum. We present a robust and cost-effective design to ensure the improvement of spectrum efficiency with CR. We first propose probability-based spectrum sensing by utilizing the statistical characteristics of licensed channel occupancy, which achieves nearly optimal performance with relatively low complexity. Based on the statistical model, we then propose periodic spectrum sensing scheduling to determine the optimal inter-sensing duration and vary the transmit power at each data sample to enhance throughput and reduce interference. We further develop a probability-based scheme for combination of local sensing information collected from cooperative CR users, which enables combination of both synchronous and asynchronous sensing information. To satisfy the stringent bandwidth constraint for reporting, we also propose to simultaneously send local sensing data to a combining node through the same narrowband channel. With proper preprocessing at individual users, such a design maintains reasonable detection performance while the bandwidth required for reporting does not change with the number of cooperative users. To better utilize the spectrum and avoid possible interference, we propose spectrum shaping schemes based on spectral precoding, which enable efficient spectrum sharing between CR and licensed users and exhibit the advantages of both simplicity and flexibility. We also propose a novel resource allocation approach based on the probabilities of licensed channel availability obtained from spectrum sensing. Different from conventional approaches, the probabilistic approach exploits the flexibility of CR to ensure efficient spectrum usage and protect licensed users from unacceptable interference.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/42714 |
| Date | 11 August 2011 |
| Creators | Zhou, Xiangwei |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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