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Publisher: TUBITAK
Types: Article
Subjects: Rayleigh fading,Bayesian compressive sensing,belief propagation,mean square error performance
Compressive sensing (CS) is a novel digital signal processing technique that has found great interest in\ud many applications including communication theory and wireless communications. In wireless communications, CS\ud is particularly suitable for its application in the area of spectrum sensing for cognitive radios, where the complete\ud spectrum under observation, with many spectral holes, can be modeled as a sparse wide-band signal in the frequency\ud domain. Considering the initial works performed to exploit the benefits of Bayesian CS in spectrum sensing, the fading\ud characteristic of wireless communications has not been considered yet to a great extent, although it is an inherent feature\ud for all sorts of wireless communications and it must be considered for the design of any practically viable wireless system.\ud In this paper, we extend the Bayesian CS framework for the recovery of a sparse signal, whose nonzero coefficients follow\ud a Rayleigh distribution. It is then demonstrated via simulations that mean square error significantly improves when\ud appropriate prior distribution is used for the faded signal coefficients and thus, in turns, the spectrum reconstruction\ud improves. Different parameters of the system model, e.g., sparsity level and number of measurements, are then varied\ud to show the consistency of the results for different cases.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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