AG Kommunikationstheorie


Compressive Sampling for Distributed Spectrum Sensing in Wireless Communication Networks


Reliable spectrum sensing is one of the most crucial tasks in cognitive radio (CR) networks. The scenario that we consider is composed of a primary user using only few subchannels and several CRs sensing the channel to detect which subchannels are used. The CRs cooperate to achieve an accurate detection. In this context, the new theories of compressed sensing and matrix completion can be used efficiently to reduce the number of measurements performed by the CRs while keeping a high reliability in the detection. In this talk we introduce compressed sensing and matrix completion with emphasis on the sampling methods and the reconstruction algorithms. We explain how these methodologies can be used in CR networks and propose new research directions to optimize several parameters of spectrum sensing.

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