Browsing by Author "Turgut, Esma"
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Item Open Access Restricted Neyman-Pearson approach based spectrum sensing in cognitive radio systems(2012) Turgut, EsmaOver the past decade, the demand for wireless technologies has increased enormously, which leads to a perceived scarcity of the frequency spectrum. Meanwhile, static allocation of the frequency spectrum leads to under-utilization of the spectral resources. Therefore, dynamic spectrum access has become a necessity. Cognitive radio has emerged as a key technology to solve the conflicts between spectrum scarcity and spectrum under-utilization. It is an intelligent wireless communication system that is aware of its operating environment and can adjust its parameters in order to allow unlicensed (secondary) users to access and communicate over the frequency bands assigned to licensed (primary) users when they are inactive. Therefore, cognitive radio requires reliable spectrum sensing techniques in order to avoid interference to primary users. In this thesis, the spectrum sensing problem in cognitive radio is studied. Specifically, the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under the constraints on the minimum detection and false alarm probabilities, is applied to the spectrum sensing problem in cognitive radio systems in the presence of uncertainty in the prior probability distribution of primary users’ signals. First, we study this problem in the presence of Gaussian noise and assume that primary users’ signals are Gaussian. Then, the problem is reconsidered for non-Gaussian noise channels. Simulation results are obtained in order to compare the performance of the restricted NP approach with the existing methods such as the generalized likelihood ratio test (GLRT) and energy detection. The restricted NP approach outperforms energy detection in all cases. It is also shown that the restricted NP approach can provide important advantages over the GLRT in terms of the worst-case detection probability, and sometimes in terms of the average detection probability depending on the situation in the presence of imperfect prior information for Gaussian mixture noise channels.Item Open Access Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise(IEEE, 2013) Turgut, Esma; Gezici, SinanIn this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under constraints on the worst-case detection and false-alarm probabilities. We derive a restricted NP based spectrum sensing algorithm for additive Gaussian mixture noise channels, and compare its performance against the generalized likelihood ratio test (GLRT) and the energy detector. Simulation results show that the proposed spectrum sensing algorithm provides improvements over the other approaches in terms of minimum (worst-case) and/or average detection probabilities. © 2013 IEEE.