1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing

Omar Serghini, Hayat Semlali, Asmaa Maali, Abdelilah Ghammaz and Salvatore Serrano ()
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Omar Serghini: Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Cadi Ayyad University, Marrakesh 40000, Morocco
Hayat Semlali: Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Cadi Ayyad University, Marrakesh 40000, Morocco
Asmaa Maali: Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Cadi Ayyad University, Marrakesh 40000, Morocco
Abdelilah Ghammaz: Laboratory of Electrical Systems, Energy Efficiency and Telecommunications, Cadi Ayyad University, Marrakesh 40000, Morocco
Salvatore Serrano: Laboratory of Digital Signal Processing, University of Messina, 98122 Messina, ME, Italy

Future Internet, 2023, vol. 16, issue 1, 1-26

Abstract: 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 considerable 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 introduce multiple cooperative spectrum sensing schemes based on a deep neural network, 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 patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, 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 methods in terms of detection probability, while ensuring a reasonable online detection time.

Keywords: cognitive radio; cooperative spectrum sensing; deep learning; convolutional neural networks; long short-term memory; multilayer perceptron (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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