This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are
considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/32085 |
| Date | 19 January 2012 |
| Creators | Zarrin, Sepideh |
| Contributors | Lim, Teng Joon |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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