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Exploiting sparsity recovery for compressive spectrum sensing: A machine learning approach
(Institute of Electrical and Electronics Engineers Inc., 2019)
Sub-Nyquist sampling for spectrum sensing has the advantages of reducing the sampling and computational complexity burdens. However, determining the sparsity of the underlying spectrum is still a challenging issue for this ...
Primary user emulation and jamming attack detection in cognitive radio via sparse coding
(Springer, 2020)
Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade ...
Efficient spectrum occupancy prediction exploiting multidimensional correlations through composite 2D-LSTM models
(MDPI, 2021)
In cognitive radio systems, identifying spectrum opportunities is fundamental to efficiently use the spectrum. Spectrum occupancy prediction is a convenient way of revealing opportunities based on previous occupancies. ...
Using OMP and SD algorithms together in mm-Wave mMIMO channel estimation
(Springer London Ltd, 2022)
Lens antenna array is considered as an effective beam selection mechanism in millimeter wave massive multiple input multiple output systems. Efficient channel estimation (CE) algorithms are required to use the advantage ...
Sparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO
(Institute of Electrical and Electronics Engineers Inc., 2023)
Millimeter-wave (mmWave) massive multiple-input-multiple-output (mMIMO) is reported as a key enabler in fifth-generation communication and beyond. It is customary to use a lens antenna array to transform a mmWave mMIMO ...