Efficient FFT Algorithms for Mobile Devices

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Di Francesco, Mario
dc.contributor.advisor Kortoci, Pranvera
dc.contributor.author Sugawara, Koki
dc.date.accessioned 2016-11-02T09:23:14Z
dc.date.available 2016-11-02T09:23:14Z
dc.date.issued 2016-10-27
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/23201
dc.description.abstract Increased traffic on wireless communication infrastructure has exacerbated the limited availability of radio frequency ({RF}) resources. Spectrum sharing is a possible solution to this problem that requires devices equipped with Cognitive Radio ({CR}) capabilities. A widely employed technique to enable {CR} is real-time {RF} spectrum analysis by applying the Fast Fourier Transform ({FFT}). Today’s mobile devices actually provide enough computing resources to perform not only the {FFT} but also wireless communication functions and protocols by software according to the software-defined radios paradigm. In addition to that, the pervasive availability of mobile devices make them powerful computing platform for new services. This thesis studies the feasibility of using mobile devices as a novel spectrum sensing platform with focus on {FFT}-based spectrum sensing algorithms. We benchmark several open-source {FFT} libraries on an Android smartphone. We relate the efficiency of calculating the {FFT} to both algorithmic and implementation-related aspects. The benchmark results also show the clear potential of special {FFT} algorithms that are tailored for sparse spectrum detection. en
dc.format.extent 40
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title Efficient FFT Algorithms for Mobile Devices en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.subject.keyword cognitive radios en
dc.subject.keyword software-defined radio en
dc.subject.keyword spectrum sensing en
dc.subject.keyword fast fourier transform en
dc.subject.keyword sparse FFT en
dc.subject.keyword crowdsourcing en
dc.identifier.urn URN:NBN:fi:aalto-201611025302
dc.programme.major Signal Processing fi
dc.programme.mcode S3013 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Wichman, Risto
dc.programme TLT_2 fi
dc.location P1 fi

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