TY - GEN
T1 - Compressive Spectrum Sensing for Wideband Signals Using Improved Matching Pursuit Algorithms
AU - Anupama, R.
AU - Kulkarni, S. Y.
AU - Prasad, S. N.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The upcoming wireless communication technologies increase the demand for spectrum, the dynamic spectrum allocation technique is a promising solution for the spectrum allocation. Spectrum sensing plays a key role in dynamic spectrum allocation, scanning through a wideband for the detection of spectrum holes which poses a problem of very high sampling rate and compressive sensing (CS) uses sub-Nyquist samples to addresses this problem. This paper proposes how orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) and stage wise orthogonal matching pursuit (StOMP) signal reconstruction algorithms can be used for detection of spectrum holes. Simulation results reveal that the probability of detection of these algorithms is very high for the detection problem than for the estimation problem.
AB - The upcoming wireless communication technologies increase the demand for spectrum, the dynamic spectrum allocation technique is a promising solution for the spectrum allocation. Spectrum sensing plays a key role in dynamic spectrum allocation, scanning through a wideband for the detection of spectrum holes which poses a problem of very high sampling rate and compressive sensing (CS) uses sub-Nyquist samples to addresses this problem. This paper proposes how orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP) and stage wise orthogonal matching pursuit (StOMP) signal reconstruction algorithms can be used for detection of spectrum holes. Simulation results reveal that the probability of detection of these algorithms is very high for the detection problem than for the estimation problem.
UR - https://www.scopus.com/pages/publications/85128722190
UR - https://www.scopus.com/pages/publications/85128722190#tab=citedBy
U2 - 10.1007/978-981-16-8546-0_20
DO - 10.1007/978-981-16-8546-0_20
M3 - Conference contribution
AN - SCOPUS:85128722190
SN - 9789811685453
T3 - Lecture Notes in Electrical Engineering
SP - 241
EP - 250
BT - International Conference on Artificial Intelligence and Sustainable Engineering - Select Proceedings of AISE 2020
A2 - Sanyal, Goutam
A2 - Travieso-González, Carlos M.
A2 - Awasthi, Shashank
A2 - Pinto, Carla M.
A2 - Purushothama, B. R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Artificial Intelligence and Sustainable Engineering, AISE 2020
Y2 - 27 November 2020 through 29 November 2020
ER -