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Compressive sensing involves 3 main processes: signal sparse representation, linear encoding or measurement collection, and nonlinear decoding or sparse recovery. In the measurement process, a measurement matrix is used to sample only the components that best represent the signal. The choice of the measurement matrix has an important impact on the accuracy and the processing time of the sparse recovery...
Spectrum sensing is an important process in cognitive radio. Spectrum sensing techniques suffer from high processing time, hardware cost, and computational complexity. To address these problems, compressive sensing has been proposed to decrease the processing time and expedite the scanning process of the radio spectrum. Selection of a suitable sparse recovery algorithm is necessary to achieve this...
A key features of the second generation for video broadcasting (DVB-S2) is the adoption of Adaptive Coding and Modulation (ACM) technology and Generic Stream Encapsulation (GSE) standards. In order to increase the system performance up to the Shannon limit a suitable resource management (RRM) exploiting this key features is a needed. Packet scheduling mechanisms particularly play a fundamental role...
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