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            Chapter Spinel Ferrite Nanoparticles: Correlation of Structure and Magnetism

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            Author(s)
            Reznickova, Alice
            Vejpravova, Jana
            Kubickova, Simona
            Pacakova, Barbara
            Language
            English
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            Abstract
            Filter bank multi‐carrier (FBMC) modulation, as a potential candidate for physical data communication in the fifth generation (5G) wireless networks, has been widely investigated. This chapter focuses on the spectral efficiency analysis of FBMC‐based cognitive radio (CR) systems, and spectral efficiency comparison is conducted with another three types of multi‐carrier modulations: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), and universal‐filtered multi‐carrier (UFMC). In order to well evaluate and compare the spectral efficiency, we propose two resource allocation (RA) algorithms for single‐cell and two‐cell CR systems, respectively. In the single‐cell system, the RA algorithm is divided into two sequential steps, which incorporate subcarrier assignment and power allocation. In the two‐cell system, a noncooperative game is formulated and the multiple access channel (MAC) technique assists to solve the RA problem. The channel state information (CSI) between CR users and licensed users cannot be precisely known in practice, and thus, an estimated CSI is considered by defining a prescribed outage probability of licensed systems. Numerical results show that FBMC can achieve the highest channel capacity compared with another three waveforms.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/165163
            Keywords
            filter bank multi‐carrier, spectral efficiency, resource allocation, cognitive radio, 5G networks
            DOI
            10.5772/66074
            Publisher
            InTechOpen
            Publication date and place
            2017
            Classification
            WAP (wireless) technology
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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