The successful implementation and employment of various cognitive radio services are largely dependent on the spectrum sensing performance of the cognitive radio terminals. Previous works on detection of cognitive radio have suggested the necessity of user cooperation in order to be able to detect at low signal-to-noise ratios experienced in practical situations. This report provides a brief overview of the impact of different fusion strategies on the spectrum hole detection performance of a fusion center in a distributed detection environment. Different decision or detection rule and fusion strategies, like single sensor scenario, counting rule, and linear decision metric, were used to analyze their influence on the spectrum sensing performance of the cognitive radio network. We consider a system of cognitive radio users who cooperate with each other in trying to detect licensed transmissions. Assuming that the cooperating nodes use identical energy detectors, we model the received signals as correlated log-normal random variables and study the problem of fusing the decisions made by the individual nodes. The cooperating radios were assumed to be designed in such a way that they satisfy the interference probability constraint individually. The interference probability constraint was also met at the fusion center. The simulation results strongly suggests that even when the observations at the individual sensors are moderately correlated, it is important not to ignore the correlation between the nodes for fusing the local decisions made by the secondary users. The thesis mainly focuses on the performance measurement of linear decision combiner in detecting primary users in a cognitive radio network.

RSS-based localization is a promising solution for estimating the position of a non-collaborative emitter using a network of collaborative sensors. This paper examines RSS-based localization and differential RSS (DRSS) localization in the presence of correlated shadowing with no knowledge of the emitter's reference power.  A new non-linear least squares (NLS) DRSS location estimator that uses correlated shadowing information to improve performance is introduced. The existing maximum likelihood (ML) estimator and Cram\' er Rao lower bound (CRLB) for RSS-based localization given do not account for correlated shadowing. This paper presents a new ML estimator and CRLB for RSS-based localization that account for spatially correlated shadowing and imperfect knowledge of the emitter's reference power. The performance of the ML estimator is compared to the CRLB under different simulation conditions. The ML estimator is shown to be biased when the number of sensors is small or the shadowing variance is large. The effects of correlated shadowing on an RSS-based location estimator are thoroughly examined. It is proven that an increase in correlated shadowing will improve the accuracy of an RSS-based location estimator. It is also demonstrated that the ideal sensor geometry which minimizes the average error becomes more compact as correlation is increased. A geometric dilution of precision (GDOP) formulation is derived that provides a metric for the effect of the position of the sensors and emitter on the location estimator performance. A measurement campaign is conducted that characterizes the path loss at 3.4 GHz. The measurements are compared to the log-distance model. The errors between the model and the measurements, which should theoretically be Gaussian, have a Kurtosis value of 1.31. The errors were determined to be spatially correlated with an average correlation coefficient of 0.5 at a distance of 160 meters. The performance of the location estimators in simulation is compared to the performance using measurements from the measurement campaign. The performance is very similar, with the largest difference between the simulated and actual results in the ML estimator. In both cases, the new NLS DRSS estimator outperformed the other estimators and achieved the CRLB. / Master of Science

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20032号 / 情博第627号 / 新制||情||109(附属図書館) / 33128 / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 原田 博司, 教授 守倉 正博, 教授 梅野 健 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM