1-D Convolutional Neural Networks based models for Cooperative Spectrum Sensing
Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted consid?erable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduced multiple cooperative spectrum sensing schemes based on a tree deep neural network architecture, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity pattern of the primary user. The scenario of an imperfect transmission channel is considered to demonstrate the robustness of the proposed model. The performance of the proposed methods are evaluated with the receiver operating characteristic curves, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory based method, surpassing the performance of the other proposed schemes and the current state of the art in terms of detection probability while ensuring a reasonable online detection time.
Email Address of Submitting Authorsserrano@unime.it
ORCID of Submitting Author0000-0003-0507-5186
Submitting Author's InstitutionUniversity of Messina
Submitting Author's Country