Please use this identifier to cite or link to this item:
Title: Deep non-cooperative spectrum sensing over Rayleigh fading channel
Authors: Su, Zhengyang
Teh, Kah Chan
Razul, Sirajudeen Gulam
Kot, Alex Chichung
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Su, Z., Teh, K. C., Razul, S. G. & Kot, A. C. (2021). Deep non-cooperative spectrum sensing over Rayleigh fading channel. IEEE Transactions On Vehicular Technology, 71(4), 4460-4464.
Journal: IEEE Transactions on Vehicular Technology
Abstract: 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.
ISSN: 0018-9545
DOI: 10.1109/TVT.2021.3138593
Schools: School of Electrical and Electronic Engineering 
Research Centres: Temasek Laboratories @ NTU 
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
TL Journal Articles

Citations 50

Updated on Feb 21, 2024

Web of ScienceTM
Citations 50

Updated on Oct 26, 2023

Page view(s)

Updated on Feb 21, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.