Spectrum Sensing With Sparsity Estimation For Cognitive Radio Systems

Shouhdy, Jiovana Elia; Abdelhamid, Bassant; Ramly, Salwa H. El

doi:10.1109/menacomm46666.2019.8988565

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Direct SOE methods that exploit the characteristics of sensing matrices are available in [19]- [22]. In [19], a spectrum sensing algorithm solved an optimization problem to remove the measurement noise effect, followed by an energy minimization problem using QR decomposition of the sensing matrix, and applied a threshold to obtain the sparsity order. This algorithm requires the signal and noise power to be known a priori to tune the threshold parameter and is not suitable for estimating a higher sparsity order.…”

Section: B Related Work On Soementioning

confidence: 99%

See 1 more Smart Citation

Sparsity Order Estimation for Compressed Sensing System Using Sparse Binary Sensing Matrix

et al. 2022

View full text Add to dashboard Cite

We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where a sparse Binary Sensing Matrix aids Sparsity Order Estimation, and a Gaussian Sensing Matrix aids reconstruction. The Binary Sensing Matrix is deterministic and is adapted according to the varying nature of the sparsity order. We estimate the sparsity order by exploiting the sparse structure of the Binary Sensing Matrix and the statistics of the obtained measurements. We refine the estimates of the sparsity order using a Kalman filter with a discrete Markov model that characterizes the sparsity order variation. A Binary Sensing Matrix-Aided Orthogonal Matching Pursuit is developed for faster recovery of compressible signals. Simulation results on real-world and synthetic data demonstrate the merits of the proposed sparsity order estimation and recovery methods compared to other existing methods. Our proposed methods are practical and recover compressible signals at least 25% faster than the existing methods.

show abstract

Section: B Related Work On Soementioning

confidence: 99%

“…By combining (19) and (20) with that of the measurement noise, each component y i of the BSM measurement vector y BSM is given as…”

Section: ) Statistics Of Bsm Measurementsmentioning

confidence: 99%