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Discriminating 4G and Broadcast Signals via Cyclostationary Feature Detection


Khoshnevis,  Hossein
International Max Planck Research School, MPI for Informatics, Max Planck Society;

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Khoshnevis, H. (2012). Discriminating 4G and Broadcast Signals via Cyclostationary Feature Detection. Master Thesis, Universität des Saarlandes, Saarbrücken.

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According to the FCC, spectrum allocation will be one of the problems of future telecommunication systems. Indeed, the available parts of the spectrum have been assigned statically to some applications such as mobile networks and broadcasting systems; hence finding a proper operating band for new systems is difficult. These telecommunication systems are called primary users. However, primary users do not always use their entire bandwidth, and therefore a lot of spectrum holes can be detected. These spectrum holes can be utilized for undefined systems called secondary users. Federal communication commission (FCC) introduced cognitive radio which detects these holes and assigns them to secondary users. There are several techniques for detention of signals such as energy based detection, matched filter detection and cyclostationary based detection. Cyclostationary based detection as one of the most sensitive methods, can be used for detection and classification of different systems. However, traditional multi-cycle and single-cycle detectors suffer from high complexity. Fortunately, using some prior knowledge about the signal, this shortcoming can be solved. In this thesis, signals of DVB-T2 as a broadcasting system and 3GPP LTE and IEEE 802.16 (WiMAX) as mobile networks have been evaluated and two cyclostationary based algorithms for detection and classification of these signals in SISO and MIMO antenna configurations are proposed.