Abstract:
interest in many applications including communication theory and wireless
communications. In wireless communication, CS is particularly suitable for its
application in the area of spectrum sensing for cognitive radios, where the complete
spectrum under observation, with many spectral holes, can be modeled as a sparse widebandsignal in frequency domain.
In this work, the CS framework is extended for the estimation of wide-band spectrum by
reconstructing the spectrum using compressive sensing matrix and reduced time samples
of the wide-band signal. The proposed algorithm outperforms conventional channel-bychannel
scanning in a sense that sensing time is reduced. The Mean Square Error (MSE)
estimation of the reconstructed spectrum via MATLAB simulations shows that a better
approximation of the reconstructed spectrum is obtained even when the number of time
samples is reduced, such that the vacant channels can be identified. The wide-band signal
detection is also performed via CS using cognitive Bayesian energy detector, which
shows that as the number of wide-band filters is increased, probability of detection of CS
algorithm improves. Bayesian Compressive Sensing (BCS) framework is also modified
for the recovery of a sparse signal, whose non-zero coefficients follow a Rayleigh
distribution. It is then demonstrated via simulations that MSE significantly improves,
when appropriate prior distribution is used for the faded signal coefficients. Different
parameters for the system model, e.g., sparsity level, number of measurements, etc., are
then varied to show the consistency of the results for different cases.
