Deep non-cooperative spectrum sensing over Rayleigh fading channel

• المؤلفون الرئيسيون: Su, Zhengyang, Teh, Kah Chan, Razul, Sirajudeen Gulam, Kot, Alex Chichung
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Deep Learning; Spectrum Sensing
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/163818

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.