The limited availability of spectrum resources has been growing into a
critical problem in wireless communications, remote sensing, and electronic
surveillance, etc. To address the high-speed sampling bottleneck of wideband
spectrum sensing, a fast and practical solution of power spectrum estimation
for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR
architectures is can theoretically achieve the full-band signal sensing with a
hundred percent of probability of intercept. But the existing algorithm is
difficult to realize in real-time due to its high complexity and complicated
calculations. By exploring the sub-sampling principle inherent in NYFR, a
computationally efficient method is introduced with compressive covariance
sensing. That can be efficient implemented via only the non-uniform fast
Fourier transform, fast Fourier transform, and some simple multiplication
operations. Meanwhile, the state-of-the-art power spectrum reconstruction model
for NYFR of time-domain and frequency-domain is constructed in this paper as a
comparison. Furthermore, the computational complexity of the proposed method
scales linearly with the Nyquist-rate sampled number of samples and the
sparsity of spectrum occupancy. Simulation results and discussion demonstrate
that the low complexity in sampling and computation is a more practical
solution to meet the real-time wideband spectrum sensing applications