Deep non-cooperative spectrum sensing over Rayleigh fading channel

• Main Authors: Su, Zhengyang, Teh, Kah Chan, Razul, Sirajudeen Gulam, Kot, Alex Chichung
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Deep Learning; Spectrum Sensing
• Online Access: https://hdl.handle.net/10356/163818
• Institution: Nanyang Technological University

In this paper, we propose a robust non-cooperative spectrum sensing algorithm based on deep learning over Rayleigh fading channel. We conduct noise cancellation on the received sensing data using the stacked convolutional auto-encoder (SCAE) as a pre-processing step. The series of the denoised signal in the time domain is then fed into the proposed Hybrid CNN-SA-GRU (H-CSG) network. The proposed network combines convolutional neural network (CNN), self-attention (SA) modules and gate recurrent unit (GRU). It can extract input features from spatial and temporal domains. The proposed algorithm has been shown to be effective and robust in detecting weak signals at the low signal-to-noise ratio (SNR) level.