Sentence examples for sparse spectrum from inspiring English sources

The present literature for sparse spectrum sensing is still in its early stages of development.

The sparse representation perspective transforms the DOA estimation problem into sparse spectrum recovery problem.

To recover a signal, we need to firstly solve the sparse spectrum α(f) from Eq. (4).

Under the sparse spectrum assumption, the FRV can be recovered by solving the combinatorial optimization problem r ̂ = arg min r r 0 s.

On the other hand, because the Meridian distribution has the characteristic of high energy concentration, it can suppress the small elements (such as noise and diffuse components) during the process and finally generates a sparse spectrum.

Then, construct the matrix A S corresponding to the columns of matrix A and support set S. Finally, reconstruct the sparse spectrum α(f) and the original signal by using Eq. (6).

Actually, in the existing works, block-sparse spectrum sensing is based on ℓ1-norm optimization, spectrum-blind reconstruction and LASSO-CWSS are based on ℓ2-norm optimization, respectively.

We consider a sparse wideband spectrum contains 10 TV bands each with 6-MHz bandwidth.

Assume N = 512, p 0 = − 95.2 dBm, and a sparse wideband spectrum contains 10 TV bands each with 6-MHz bandwidth.

Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra.

We first provide the sparse spatial spectrums obtained from the two Bayesian compressive sensing algorithms when there is no narrowband interference in Fig. 2. The true DOAs are marked with red vertical lines.