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Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification
(Institute of Electrical and Electronics Engineers Inc., 2019)
Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper ...
Spectrum occupancy prediction exploiting time and frequency correlations through 2D-LSTM
(Institute of Electrical and Electronics Engineers Inc., 2020)
The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on ...
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 ...